Fix based on review

cloudnetpy/instruments/ceilo.py

@@ -1,13 +1,13 @@
 """Module for reading and processing Vaisala / Lufft ceilometers."""
 import linecache
 from typing import Union, Optional
+import logging
 import numpy as np
 import netCDF4
-from cloudnetpy.instruments.lufft import LufftCeilo, CL61d
+from cloudnetpy.instruments.lufft import LufftCeilo, Cl61d
 from cloudnetpy.instruments.vaisala import ClCeilo, Ct25k
 from cloudnetpy import utils, output, CloudnetArray
 from cloudnetpy.metadata import MetaData
-import logging
 
 
 def ceilo2nc(full_path: str,
@@ -75,14 +75,14 @@ def ceilo2nc(full_path: str,
 
 
 def _initialize_ceilo(full_path: str,
-                      date: Optional[str] = None) -> Union[ClCeilo, Ct25k, LufftCeilo, CL61d]:
+                      date: Optional[str] = None) -> Union[ClCeilo, Ct25k, LufftCeilo, Cl61d]:
     model = _find_ceilo_model(full_path)
     if model == 'cl31_or_cl51':
         return ClCeilo(full_path, date)
     if model == 'ct25k':
         return Ct25k(full_path)
     if model == 'cl61d':
-        return CL61d(full_path, date)
+        return Cl61d(full_path, date)
     return LufftCeilo(full_path, date)
 
 
@@ -110,7 +110,7 @@ def _find_ceilo_model(full_path: str) -> str:
     raise RuntimeError('Error: Unknown ceilo model.')
 
 
-def _append_height(ceilo: Union[ClCeilo, Ct25k, LufftCeilo, CL61d],
+def _append_height(ceilo: Union[ClCeilo, Ct25k, LufftCeilo, Cl61d],
                    site_altitude: float) -> None:
     """Finds height above mean sea level."""
     tilt_angle = np.median(ceilo.metadata['tilt_angle'])
@@ -120,7 +120,7 @@ def _append_height(ceilo: Union[ClCeilo, Ct25k, LufftCeilo, CL61d],
     ceilo.data['altitude'] = CloudnetArray(site_altitude, 'altitude')
 
 
-def _append_data(ceilo: Union[ClCeilo, Ct25k, LufftCeilo, CL61d],
+def _append_data(ceilo: Union[ClCeilo, Ct25k, LufftCeilo, Cl61d],
                  beta_variants: tuple,
                  depol_variants: Optional[tuple] = None):
     """Adds data / metadata as CloudnetArrays to ceilo.data."""
@@ -138,7 +138,7 @@ def _append_data(ceilo: Union[ClCeilo, Ct25k, LufftCeilo, CL61d],
             ceilo.data[field] = CloudnetArray(np.array(ceilo.metadata[field], dtype=float), field)
 
 
-def _save_ceilo(ceilo: Union[ClCeilo, Ct25k, LufftCeilo, CL61d],
+def _save_ceilo(ceilo: Union[ClCeilo, Ct25k, LufftCeilo, Cl61d],
                 output_file: str,
                 location: str,
                 keep_uuid: bool,

cloudnetpy/instruments/ceilometer.py

@@ -20,6 +20,7 @@ def __init__(self, full_path: str):
         self.date = []
         self.noise_params = (1, 1, 1, (1, 1))
         self.calibration_factor = 1
+        self.wavelength = None
 
     def calc_beta(self) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
         """Converts range-corrected raw beta to noise-screened beta."""
@@ -32,11 +33,11 @@ def calc_beta(self) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
 
     def calc_depol(self) -> Tuple[np.ndarray, np.ndarray]:
         """Converts raw depolarisation to noise-screened depolarisation."""
-        snr_limit = 3
+        snr_limit = 4
         noisy_data = NoisyData(*self._get_args(), snr_limit)
+        sigma = _calc_sigma_units(self.time, self.range)
         x_pol = noisy_data.screen_data(self.processed_data['x_pol'], keep_negative=True)
         depol = x_pol / self.processed_data['p_pol']
-        sigma = _calc_sigma_units(self.time, self.range)
         p_pol_smooth = scipy.ndimage.filters.gaussian_filter(self.processed_data['p_pol'], sigma)
         x_pol_smooth = scipy.ndimage.filters.gaussian_filter(self.processed_data['x_pol'], sigma)
         x_pol_smooth = noisy_data.screen_data(x_pol_smooth, is_smoothed=True)

cloudnetpy/instruments/lufft.py

@@ -14,7 +14,7 @@ def __init__(self, file_name: str, date: Optional[str] = None):
         self._expected_date = date
         self.model = 'Lufft CHM15k'
         self.dataset = netCDF4.Dataset(self.file_name)
-        self.noise_params = (170, 2e-14, 0.3e-6, (1e-9, 4e-9))
+        self.noise_params = (70, 2e-14, 0.3e-6, (1e-9, 4e-9))
         self.wavelength = 1064
 
     def read_ceilometer_file(self, calibration_factor: Optional[float] = None) -> None:
@@ -82,13 +82,14 @@ def _read_metadata(self) -> dict:
         return {'tilt_angle': tilt_angle}
 
 
-class CL61d(LufftCeilo):
+class Cl61d(LufftCeilo):
     """Class for Vaisala CL61d ceilometer."""
     def __init__(self, file_name: str, date: Optional[str] = None):
         super().__init__(file_name)
         self._expected_date = date
         self.model = 'Vaisala CL61d'
         self.wavelength = 910.55
+        self.noise_params = (100, 2e-14, 0.3e-6, (1e-9, 4e-9))
 
     def read_ceilometer_file(self, calibration_factor: Optional[float] = None) -> None:
         """Reads data and metadata from concatenated Vaisala CL61d netCDF file."""
@@ -109,16 +110,15 @@ def _calc_range(self) -> np.ndarray:
     def _read_date(self) -> List[str]:
         if self._expected_date:
             return self._expected_date.split('-')
-        else:
-            time = self.dataset.variables['time'][:]
-            date_first = utils.seconds2date(time[0], epoch=(1970, 1, 1))
-            date_last = utils.seconds2date(time[-1], epoch=(1970, 1, 1))
-            date_middle = utils.seconds2date(time[round(len(time)/2)], epoch=(1970, 1, 1))
-            if date_first != date_last:
-                logging.warning('No expected date given and different dates in CL61d timestamps.')
-            return date_middle[:3]
-
-    def _calibrate_backscatter(self, calibration_factor: Union[float, None]) -> np.ndarray:
+        time = self.dataset.variables['time'][:]
+        date_first = utils.seconds2date(time[0], epoch=(1970, 1, 1))
+        date_last = utils.seconds2date(time[-1], epoch=(1970, 1, 1))
+        date_middle = utils.seconds2date(time[round(len(time)/2)], epoch=(1970, 1, 1))
+        if date_first != date_last:
+            logging.warning('No expected date given and different dates in CL61d timestamps.')
+        return date_middle[:3]
+
+    def _calibrate_backscatter(self, calibration_factor: Optional[float] = None) -> np.ndarray:
         beta_raw = self._getvar('beta_att')
         if calibration_factor is None:
             logging.warning('Using default calibration factor')