Fix counts and samples (#545)

* Fix counts

* raise error in counts if avg_count is 1

* Improvement.

* Fix tests

* Add check.

---------

Co-authored-by: victor <visangim@gmail.com>

src/qililab/result/qblox_results/qblox_bins_acquisitions.py

@@ -59,27 +59,27 @@ def counts(self) -> Counts:
             raise IndexError("Sequencers must have the same number of bins to compute the counts.")
         # TODO: Add limitations to check we are doing single-shot for multi qubit?
         counts_object = Counts(n_qubits=len(self.bins))
+        for seq in self.bins:
+            if any(bin_avg > 1 for bin_avg in seq.avg_cnt):
+                raise ValueError("Counts cannot be used when using a hardware average higher than 1.")
         for bin_idx in range(num_bins):
             # The threshold inside of a qblox bin is the name they use for already classified data as a value between
             # 0 and 1, not the value used in the comparator to perform such classification.
-            measurement_as_list = [int(bins_data.threshold[bin_idx]) for bins_data in self.bins]
+            measurement_as_list = [bins_data.threshold[bin_idx] for bins_data in self.bins]
             measurement = "".join(str(bit) for bit in measurement_as_list)
             counts_object.add_measurement(state=measurement)
         return counts_object
 
     def samples(self) -> np.ndarray:
-        """Returns an array containing the measured samples.
+        """Returns an array containing the samples measured in each bin of each sequencer.
+
+        The shape of the returned array is ``(# sequencers, # bins)``.
 
         Returns:
-            np.ndarray: An array containing the measured samples (0 or 1).
+            np.ndarray: An array containing the samples measured in each bin of each sequencer.
         """
         # Check that all sequencers have the same number of bins.
-        if any(len(seq_bins) != (num_bins := len(self.bins[0])) for seq_bins in self.bins):
-            raise IndexError("Sequencers must have the same number of bins to compute the samples.")
-        samples = []
-        for bin_idx in range(num_bins):
-            # The threshold inside of a qblox bin is the name they use for already classified data as a value between
-            # 0 and 1, not the value used in the comparator to perform such classification.
-            measurement_as_list = [int(bins_data.threshold[bin_idx]) for bins_data in self.bins]
-            samples.append(measurement_as_list)
+        if any(len(seq_bins) != len(self.bins[0]) for seq_bins in self.bins):
+            raise IndexError("Sequencers must have the same number of bins to return the samples.")
+        samples = [seq_data.threshold for seq_data in self.bins]
         return np.array(samples)

tests/data.py

@@ -525,8 +525,8 @@ class Galadriel:
                     },
                     "bins": {
                         "integration": {"path0": [-0.08875841551660968], "path1": [-0.4252879595139228]},
-                        "threshold": [0.48046875],
-                        "avg_cnt": [1024],
+                        "threshold": [0],
+                        "avg_cnt": [1],
                     },
                 }
             ],
@@ -542,8 +542,8 @@ class Galadriel:
                     },
                     "bins": {
                         "integration": {"path0": [-0.14089025097703958], "path1": [-0.3594594414081583]},
-                        "threshold": [0.4599609375],
-                        "avg_cnt": [1024],
+                        "threshold": [0],
+                        "avg_cnt": [1],
                     },
                 }
             ],
@@ -576,8 +576,8 @@ class Galadriel:
                     },
                     "bins": {
                         "integration": {"path0": [-0.08875841551660968], "path1": [-0.4252879595139228]},
-                        "threshold": [0.48046875],
-                        "avg_cnt": [1024],
+                        "threshold": [0],
+                        "avg_cnt": [1],
                     },
                 }
             ],
@@ -593,8 +593,8 @@ class Galadriel:
                     },
                     "bins": {
                         "integration": {"path0": [-0.14089025097703958], "path1": [-0.3594594414081583]},
-                        "threshold": [0.4599609375],
-                        "avg_cnt": [1024],
+                        "threshold": [0],
+                        "avg_cnt": [1],
                     },
                 }
             ],

tests/execution/test_execution_manager.py

@@ -283,7 +283,7 @@ def test_execute_method_with_keyboard_interrupt(
             "bins": {
                 "integration": {"path0": [1, 2, 3], "path1": [4, 5, 6]},
                 "threshold": [1, 1, 1],
-                "avg_cnt": [1000, 1000, 1000],
+                "avg_cnt": [1, 1, 1],
             },
         },
     }

tests/test_utils.py

@@ -20,13 +20,13 @@ def mock_instruments(mock_rs: MagicMock, mock_pulsar: MagicMock, mock_keithley:
                 "index": 0,
                 "acquisition": {
                     "scope": {
-                        "path0": {"data": [1, 1, 1, 1, 1, 1, 1, 1], "out-of-range": False, "avg_cnt": 1000},
-                        "path1": {"data": [0, 0, 0, 0, 0, 0, 0, 0], "out-of-range": False, "avg_cnt": 1000},
+                        "path0": {"data": [1, 1, 1, 1, 1, 1, 1, 1], "out-of-range": False, "avg_cnt": 1},
+                        "path1": {"data": [0, 0, 0, 0, 0, 0, 0, 0], "out-of-range": False, "avg_cnt": 1},
                     },
                     "bins": {
                         "integration": {"path0": [-0.08875841551660968], "path1": [-0.4252879595139228]},
-                        "threshold": [0.48046875],
-                        "avg_cnt": [1024],
+                        "threshold": [0],
+                        "avg_cnt": [1],
                     },
                 },
             }