Fixing Indentation and test_optimizers extra tests deleted

Author: Niharika Karnik

pysensors/utils/_norm_calc.py

@@ -149,8 +149,8 @@ def returnInstance(cls, name):
     Returns
     -------
     __norm_calc_type[name], instance of class
-  """
-  if name not in __norm_calc_type:
-    raise NotImplementedError("{} NOT IMPLEMENTED!!!!!\n".format(name))
-  return __norm_calc_type[name]
+    """
+    if name not in __norm_calc_type:
+        raise NotImplementedError("{} NOT IMPLEMENTED!!!!!\n".format(name))
+    return __norm_calc_type[name]
 

tests/optimizers/test_optimizers.py

@@ -1,8 +1,9 @@
 """Unit tests for optimizers"""
-
 import numpy as np
 
-from pysensors.optimizers import CCQR, GQR, QR
+from pysensors.optimizers import CCQR
+from pysensors.optimizers import QR
+from pysensors.optimizers import GQR
 
 
 def test_num_sensors(data_vandermonde):
@@ -50,7 +51,6 @@ def test_ccqr_negative_costs(data_vandermonde):
     chosen_sensors = set(sensors[: min(x.shape)])
     assert all(s in chosen_sensors for s in set(desirable_sensors))
 
-
 def test_gqr_qr_equivalence(data_vandermonde):
     x = data_vandermonde
 
@@ -60,7 +60,6 @@ def test_gqr_qr_equivalence(data_vandermonde):
 
     np.testing.assert_array_equal(gqr_sensors, qr_sensors)
 
-
 def test_gqr_ccqr_equivalence(data_random):
     x = data_random
 
@@ -77,16 +76,7 @@ def test_gqr_ccqr_equivalence(data_random):
     # Get ranked sensors from GQR
     # first we should pass all_sensors to GQR
     all_sensors = np.arange(x.shape[1]) #QR().fit(x.T).get_sensors()
-    sensors_GQR = (
-        GQR()
-        .fit(
-            x.T,
-            idx_constrained=forbidden_sensors,
-            n_const_sensors=0,
-            constraint_option="exact_n_const_sensors",
-        )
-        .get_sensors()
-    )
+    sensors_GQR = GQR().fit(x.T, all_sensors=all_sensors, idx_constrained=forbidden_sensors,n_const_sensors=0, constraint_option='exact_n').get_sensors()
 
     # Forbidden sensors should not be included
     chosen_sensors_GQR = set(sensors_GQR[: (x.shape[1] - len(forbidden_sensors))])
@@ -116,73 +106,15 @@ def test_gqr_exact_constrainted_case1(data_random):
     assert set(sensors_CCQR).isdisjoint(set(totally_forbidden_sensors))
 
 
+
     # Get ranked sensors from GQR
     sensors_GQR = GQR().fit(x.T, idx_constrained=forbidden_sensors,all_sensors=sensors_QR, n_sensors=total_sensors,n_const_sensors=exact_n_const_sensors, constraint_option='exact_n').get_sensors()[:total_sensors]
     assert sensors_CCQR.all() == sensors_GQR.all()
 
-def test_gqr_max_constrained_case1(data_random):
-    # In this case we want to place a total of 10 sensors
-    # with a constrained region that is allowed to have a maximum of 3 sensors
-    # but 4 of the first 10 are in the constrained region
-    x = data_random
-    # unconstrained sensors (optimal)
-    sensors_QR = QR().fit(x.T).get_sensors()
-    # exact number of sensors allowed in the constrained region
-    total_sensors = 10
-    max_n_const_sensors = 3
-    forbidden_sensors = [8, 5, 2, 6]
-    totally_forbidden_sensors = [x for x in forbidden_sensors if x in sensors_QR][
-        :max_n_const_sensors
-    ]
-    totally_forbidden_sensors = [
-        y for y in forbidden_sensors if y not in totally_forbidden_sensors
-    ]
-    costs = np.zeros(x.shape[1])
-    costs[totally_forbidden_sensors] = 100
-    # Get ranked sensors
-    sensors = CCQR(sensor_costs=costs).fit(x.T).get_sensors()[:total_sensors]
-
-    # Forbidden sensors should not be included
-    chosen_sensors = set(sensors[: (x.shape[1] - len(totally_forbidden_sensors))])
-    assert chosen_sensors.isdisjoint(set(totally_forbidden_sensors))
 
-    # Get ranked sensors from GQR
-    sensors_GQR = (
-        GQR()
-        .fit(
-            x.T,
-            idx_constrained=forbidden_sensors,
-            n_sensors=total_sensors,
-            n_const_sensors=max_n_const_sensors,
-            constraint_option="max_n_const_sensors",
-        )
-        .get_sensors()[:total_sensors]
-    )
-    assert sensors_GQR.intersection(forbidden_sensors) == 3
-
-
-def test_gqr_predetermined_case1(data_random):
-    # In this case we want to place a total of 10 sensors
-    # 2 of the sensors are predetermined by the user
-    x = data_random
-    # unconstrained sensors (optimal)
-    sensors_QR = QR().fit(x.T).get_sensors()  # noqa: F841
-    # Predtermined sensors
-    total_sensors = 10
-    n_sensors_pre = 2
-    predetermined_sensors = [8, 5]
+## TODO
+def test_gqr_max_constrained():
+    pass
 
-    # Predetermined sensors shopuld be included
-    # Get ranked sensors from GQR
-    sensors_GQR = (
-        GQR()
-        .fit(
-            x.T,
-            idx_constrained=predetermined_sensors,
-            n_sensors=total_sensors,
-            n_const_sensors=n_sensors_pre,
-            constraint_option="predetermined",
-        )
-        .get_sensors()[:total_sensors]
-    )
-    assert sensors_GQR.intersection(predetermined_sensors) == 2
+def test_gqr_radii_constrained():
+    pass