Added new GNSSSignalType and GNSSSignalsMessage definitions.

.github/workflows/release_build.yml

@@ -394,6 +394,29 @@ jobs:
         name: python_dist
         path: python/dist
 
+  # Check autogenerated signal definitions.
+  test_signal_defs:
+    name: GNSS Signal Definitions
+    runs-on: ubuntu-latest
+    defaults:
+      run:
+        working-directory: python
+    steps:
+    - uses: actions/checkout@v2
+
+    - name: Setup Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.x'
+
+    - name: Install Python Requirements
+      run: |
+        pip install -r requirements.txt
+
+    - name: Check Signal Definitions
+      run: |
+        python fusion_engine_client/messages/signal_def_gen.py --check-only
+
   # Create a release only on a tag (not on a branch push).
   release:
     name: Create Release

python/examples/binary_message_decode.py

@@ -125,7 +125,7 @@
                             payload.unpack(buffer=contents, offset=header.calcsize(),
                                            message_version=header.message_version)
                             logger.info("Decoded payload contents: %s" % str(payload))
-                        except ValueError as e:
+                        except Exception as e:
                             logger.warning(str(e))
                             logger.warning("Unable to decode payload contents.")
                     else:

python/fusion_engine_client/analysis/data_loader.py

@@ -89,20 +89,24 @@ def to_numpy(self, remove_nan_times: bool = True,
                 self.message_bytes = np.array(self.message_bytes, dtype=np.uint64)
                 self.message_index = np.array(self.message_index, dtype=int)
 
-                if remove_nan_times and 'p1_time' in self.__dict__:
-                    is_nan = np.isnan(self.p1_time)
+                # Helper function for removing entries with NAN timestamps.
+                def _remove_nan_from_dict(data_dict):
+                    if 'p1_time' not in data_dict:
+                        return
+
+                    is_nan = np.isnan(data_dict['p1_time'])
                     if np.any(is_nan):
                         keep_idx = ~is_nan
-                        for key, value in self.__dict__.items():
+                        for key, value in data_dict.items():
                             if (key not in ('message_type', 'message_class', 'params', 'messages') and
                                     isinstance(value, np.ndarray)):
-                                if key in self.__dict__.get('__metadata__', {}).get('not_time_dependent', []):
+                                if key in data_dict.get('__metadata__', {}).get('not_time_dependent', []):
                                     # Data is not time-dependent, even if it happens to have the same number of elements
                                     # as the time vector.
                                     pass
                                 elif len(value.shape) == 1:
                                     if len(value) == len(keep_idx):
-                                        self.__dict__[key] = value[keep_idx]
+                                        data_dict[key] = value[keep_idx]
                                     else:
                                         # Field has a different length than the time vector. It is likely a
                                         # non-time-varying element (e.g., a position std dev threshold).
@@ -113,21 +117,32 @@ def to_numpy(self, remove_nan_times: bool = True,
                                     # along the columns.
                                     if value.shape[1] == len(is_nan):
                                         # Assuming second dimension (columns) is time.
-                                        self.__dict__[key] = value[:, keep_idx]
+                                        data_dict[key] = value[:, keep_idx]
                                     # Otherwise, check to see if the data is transposed as NxA.
                                     elif value.shape[0] == len(is_nan):
                                         # Assuming first dimension is time.
-                                        self.__dict__[key] = value[keep_idx, :]
+                                        data_dict[key] = value[keep_idx, :]
                                     elif value.shape[1] == len(is_nan):
                                         # Assuming second dimension is time.
-                                        self.__dict__[key] = value[:, keep_idx]
+                                        data_dict[key] = value[:, keep_idx]
                                     else:
                                         # Unrecognized data shape.
                                         pass
                                 else:
                                     # Unrecognized data shape.
                                     pass
 
+                # If requested, remove any entries with NAN P1 timestamps.
+                if remove_nan_times:
+                    # Remove NANs from top-level numpy data.
+                    _remove_nan_from_dict(self.__dict__)
+
+                    # Special cast: also remove NANs from nested satellite/signal data elements.
+                    if self.message_type == MessageType.GNSS_SIGNALS:
+                        _remove_nan_from_dict(self.sv_data)
+                        _remove_nan_from_dict(self.signal_data)
+
+
             if not keep_messages:
                 self.messages = []
             if not keep_message_bytes:

python/fusion_engine_client/analysis/plotly_data_support.js

@@ -0,0 +1,28 @@
+var figure = document.getElementsByClassName("plotly-graph-div js-plotly-plot")[0];
+
+function GetTimeText(time_sec) {
+  if (p1_time_axis_rel) {
+    return `Rel: ${time_sec.toFixed(3)} sec (P1: ${(time_sec + p1_t0_sec).toFixed(3)} sec)`;
+  }
+  else {
+    return `Rel: ${(time_sec - p1_t0_sec).toFixed(3)} sec (P1: ${time_sec.toFixed(3)} sec)`;
+  }
+}
+
+function ChangeHoverText(point, new_text) {
+  // Note: Technically calling restyle() is more correct, however it can only restyle an entire trace, not just one
+  // point in a trace, and in practice it's very sluggish. Manually modifying fullData.text is much faster. Both options
+  // seem to have a small race condition and occasionally the text does not change before the hover div becomes visible.
+  // Nothing we can do about that right now.
+  // let text_array = point.data.text;
+  // text_array[point.pointNumber] = new_text;
+  // Plotly.restyle(d, {'text': text_array}, [point.curveNumber]);
+  point.fullData.text = new_text;
+}
+
+function GetCustomData(point, row) {
+  let customdata = point.data.customdata.hasOwnProperty("_inputArray") ?
+                   point.data.customdata._inputArray :
+                   point.data.customdata;
+  return customdata[row][point.pointNumber];
+}

python/fusion_engine_client/messages/defs.py

@@ -93,6 +93,7 @@ class MessageType(IntEnum):
     POSE_AUX = 10003
     CALIBRATION_STATUS = 10004
     RELATIVE_ENU_POSITION = 10005
+    GNSS_SIGNALS = 10006
 
     # Device status messages.
     SYSTEM_STATUS = 10500