Alter list type to Tuples for sample_width and bin_size in Beersheba.

invisible_cities/cities/beersheba.py

@@ -86,14 +86,13 @@
 from .. types.symbols          import InterpolationMethod
 from .. types.symbols          import CutType
 from .. types.symbols          import DeconvolutionMode
-
+from .. types.ic_types         import Tuple2Dor3D
 
 from typing import Tuple
 from typing import List
 from typing import Optional
 from typing import Union
 
-
 def event_info_adder(timestamp : float, dst : pd.DataFrame):
     return dst.assign(time=timestamp/1e3, nsipm=0, Xrms=0, Yrms=0)
 
@@ -104,8 +103,8 @@ def deconvolve_signal(det_db           : pd.DataFrame,
                       e_cut            : float,
                       n_iterations     : int,
                       iteration_tol    : float,
-                      sample_width     : List[float],
-                      bin_size         : List[float],
+                      sample_width     : Tuple2Dor3D,
+                      bin_size         : Tuple2Dor3D,
                       satellite_params : Union[dict, NoneType],
                       diffusion        : Optional[Tuple[float, float, float]]=(1., 1., 0.3),
                       energy_type      : Optional[HitEnergy]=HitEnergy.Ec,

invisible_cities/config/beersheba.conf

@@ -16,13 +16,13 @@ same_peak = True
 
 deconv_params = dict(
   q_cut         = 10,
-  drop_dist     = [10., 10.],
+  drop_dist     = (10., 10.),
   psf_fname     = '$ICDIR/database/test_data/PSF_dst_sum_collapsed.h5',
   e_cut         = 1e-3,
   n_iterations  = 100,
   iteration_tol = 0.01,
-  sample_width  = [10., 10.],
-  bin_size      = [ 1.,  1.],
+  sample_width  = (10., 10.),
+  bin_size      = ( 1.,  1.),
   energy_type   = Ec,
   diffusion     = (1.0, 1.0),
   deconv_mode   = joint,

invisible_cities/reco/deconv_functions.py

@@ -25,7 +25,7 @@
 from .. types.ic_types      import NoneType
 from .. types.symbols       import InterpolationMethod
 from .. types.symbols       import CutType
-
+from .. types.ic_types      import Tuple2Dor3D
 
 def collect_component_sizes(im_mask : np.ndarray) -> (np.ndarray, np.ndarray):
     '''
@@ -236,7 +236,7 @@ def drop_event(df):
 
     return drop_event
 
-def deconvolution_input(sample_width : List[float     ],
+def deconvolution_input(sample_width : Tuple2Dor3D,
                         det_grid     : List[np.ndarray],
                         inter_method : InterpolationMethod = InterpolationMethod.cubic
                        ) -> Callable:
@@ -350,7 +350,7 @@ def find_nearest(array : np.ndarray,
 @check_annotations
 def deconvolve(n_iterations         : int,
                iteration_tol        : float,
-               sample_width         : List[float],
+               sample_width         : Tuple2Dor3D,
                det_grid             : List[np.ndarray],
                satellite_start_iter : Union[int, NoneType],
                satellite_max_size   : int,

invisible_cities/types/ic_types.py

@@ -1,12 +1,16 @@
 from enum        import Enum
 from collections import OrderedDict
+from typing  import Union
+from typing  import Tuple
 
 import numpy as np
 
 NN= -999999  # No Number, a trick to aovid nans in data structs
 
 NoneType = type(None)
 
+Tuple2Dor3D = Union[Tuple[float, float], Tuple[float, float, float]]
+
 class NNN:
 
     def __getattr__(self, _):