network encoding for maximum portability. code cleanup

geomagio/imagcdf/ImagCDFFactory.py

@@ -132,9 +132,9 @@ class ImagCDFFactory(TimeseriesFactory):
         try:
             # Initialize the CDF writer
             cdf_spec = {
-                "Compressed": 9,  # Enable compression (0-9)
-                "Majority": CDFWriter.ROW_MAJOR,  # Data layout - gets set automatically
-                "Encoding": CDFWriter.HOST_ENCODING,  #  gets set automatically
+                "Compressed": 9,  # Enable compression (1-9)
+                "Majority": CDFWriter.ROW_MAJOR,
+                "Encoding": CDFWriter.NETWORK_ENCODING,  #  XDR Encoding - If a CDF must be portable between two or more different types of computers use network encoded.
                 "Checksum": True,  # Disable checksum for faster writes (optional)
                 "rDim_sizes": [],  # Applicable only if using rVariables - CDF protocol recommends only using zVariables.
             }
@@ -259,9 +259,6 @@ class ImagCDFFactory(TimeseriesFactory):
         for urlInterval in urlIntervals:
             interval_start = urlInterval["start"]
             interval_end = urlInterval["end"]
-            # Removes last data point ex: if endtime = 02:00:00, this could return 01:59:00 as last data point. 
-            # if interval_start != interval_end:
-                # interval_end = interval_end - delta
             url = self._get_url(
                 observatory=observatory,
                 date=interval_start,
@@ -357,13 +354,7 @@ class ImagCDFFactory(TimeseriesFactory):
             try:
                 # Read CDF data and merge
                 cdf = CDFReader(url_file)
-                # file_stream = self._read_cdf(cdf, channels)
                 timeseries = self._read_cdf(cdf, channels)
-                # Attempt to select only requested channelws (redundant as read_cdf can more efficiently filter)
-                # selected = Stream()
-                # for ch in channels:
-                #     selected += file_stream.select(channel=ch)
-                # timeseries += selected
             except Exception as e:
                 print(f"Error reading CDF file '{url_file}': {e}", file=sys.stderr)
 
@@ -395,48 +386,6 @@ class ImagCDFFactory(TimeseriesFactory):
         timeseries.sort()
         return timeseries
 
-    # Removed - cdflib takes a file path as an input more efficiently than taking in byte data.
-    # def parse_string(self, data: str, **kwargs):
-    #     """
-    #     Parse ImagCDF binary data into an ObsPy Stream.
-
-    #     This method writes the provided binary data to a temporary file,
-    #     reads the file using `cdflib`, and converts the data into an ObsPy
-    #     Stream.
-
-    #     Parameters
-    #     ----------
-    #     data : bytes
-    #         Binary data containing ImagCDF content.
-
-    #     Returns
-    #     -------
-    #     Stream
-    #         An ObsPy Stream object with the parsed geomagnetic time series data.
-
-    #     Raises
-    #     ------
-    #     TimeseriesFactoryException
-    #         If an error occurs while parsing the ImagCDF data.
-    #     """
-    #     # Create a temporary file to store the CDF data
-    #     with tempfile.NamedTemporaryFile(delete=False, suffix=".cdf") as tmp_file:
-    #         tmp_file_name = tmp_file.name
-    #         tmp_file.write(data)
-    #     channels = kwargs.get('channels', [])
-    #     try:
-    #         # Read the CDF from the temporary file
-    #         cdf = CDFReader(tmp_file_name)
-    #         stream = self._read_cdf(cdf, channels)
-    #         # no cdf.close() method required
-    #     except Exception as e:
-    #         raise TimeseriesFactoryException(f"Error parsing ImagCDF data: {e}")
-    #     finally:
-    #         # Clean up the temporary file
-    #         os.remove(tmp_file_name)
-
-    #     return stream
-
     def _create_global_attributes(
         self, timeseries: Stream, channels: List[str]
     ) -> dict:
@@ -654,7 +603,7 @@ class ImagCDFFactory(TimeseriesFactory):
             units = "Celsius"
             validmin = -273.15  # absolute zero
             validmax = 79_999
-            depend_0 = "DataTimes" #can be used for nonstandard element
+            depend_0 = "DataTimes"  # can be used for nonstandard element
         # elif channel in [REAL_TEMPERATURES]:
         #     units = "Celsius"
         #     fieldnam = f"Temperature {temperature_index} {trace.stats.location}"
@@ -782,7 +731,7 @@ class ImagCDFFactory(TimeseriesFactory):
                 f"{', '.join(missing_global_attrs)}"
             )
             raise TimeseriesFactoryException(error_message)
-        
+
         # Map global attributes to Stream-level metadata
         observatory = global_attrs.get("IagaCode", [""])[0]
         station_name = global_attrs.get("ObservatoryName", [""])[0]
@@ -811,26 +760,25 @@ class ImagCDFFactory(TimeseriesFactory):
 
         # Read data variables and associate them with time variables
         for var in cdf.cdf_info().zVariables:
-            
+
             # Skip time variables
             if var.endswith("Times"):
                 continue
 
-            # Map the variable name back to a standard channel code
-            # Geomagnetic fields are named like GeomagneticFieldH, GeomagneticFieldD, etc.
-            # Temperatures are named like Temperature1, Temperature2, ...
-            # Extract channel name by removing known prefixes
+            # Map the variable name back to a standard channel code by removing known prefixes
+            # Names are like GeomagneticFieldH, GeomagneticFieldD, Temperature1, Temperature2, ...
             if var.startswith("GeomagneticField"):
                 channel = var.replace("GeomagneticField", "")
-            elif var.startswith("Temperature"):
-                # Temperature variables may not map directly to a geomagnetic channel
-                # but to temperature sensors. We can just use the label from LABLAXIS if needed
-                channel = attrs.get("LABLAXIS", var)
+            # elif var.startswith("Temperature"):
+            #     # Temperature variables may not map directly to a geomagnetic channel
+            #     # but to temperature sensors. We can just use the label from LABLAXIS if needed
+            #     channel = attrs.get("LABLAXIS", var)
             else:
                 # fallback if naming doesn't match expected patterns
                 channel = var
 
-            if channels and channel not in channels: continue
+            if channels and channel not in channels:
+                continue
             data = cdf.varget(var)
             attrs = cdf.varattsget(var)
 
@@ -852,7 +800,7 @@ class ImagCDFFactory(TimeseriesFactory):
             #     continue
             times = []
             if matched_time_key in time_vars:
-                times = time_vars[matched_time_key]            
+                times = time_vars[matched_time_key]
 
             # Determine delta (sample interval)
             if len(times) > 1:
@@ -883,7 +831,7 @@ class ImagCDFFactory(TimeseriesFactory):
                 "VALIDMAX",
                 "DISPLAY_TYPE",
                 "LABLAXIS",
-                "DEPEND_0"
+                "DEPEND_0",
             ]
             # Validate presence of required variable attributes
             missing_var_attrs = []
@@ -1014,7 +962,7 @@ class ImagCDFFactory(TimeseriesFactory):
             base_path = self.urlTemplate[7:]
             if not base_path or base_path == "{obs}_{dt}_{t}.cdf":
                 base_path = os.getcwd()  # Default to current working directory
-                return os.path.join(base_path, "etc","imagcdf", filename)
+                return os.path.join(base_path, "etc", "imagcdf", filename)
             return os.path.join(self.urlTemplate, filename)
 
         # Unsupported URL scheme