Feature/adc clip tag

data/proto_nd_flow/2x2_run2.yaml

@@ -0,0 +1,65 @@
+# The configuration accept the following three ways for e_field, lifetime, response_bin_size
+# Can be extended to other parameters
+#       a single value, e.g e_field: 0.50
+#       a list with a single element, e.g e_field: [0.50]
+#       a list with the length of module numbers, e_field: [0.50, 0.51, 0.49, 0.52]
+# Note that if simulation with module variation is activated,
+# but the list of the parameter of interest is shorter than the number of modules,
+# then it would likely to take the first value
+temperature: 87.17 # K
+e_field: 0.50 # kV/cm
+lifetime: 2.2e+3 # us
+time_interval: [0, 200.] # us
+long_diff: 4.0e-6 # cm * cm / us
+tran_diff: 8.8e-6 # cm * cm / us
+drift_length: 30.27225 # cm
+response_sampling: 0.1 # us
+response_bin_size: 0.04434 # cm
+time_padding: 190 # us
+time_window: 189.1 # us
+tpc_offsets: # cm
+  - [33.5, 0., 33.5]
+  - [33.5, 0., -33.5]
+  - [-33.5, 0., 33.5]
+  - [-33.5, 0., -33.5]
+tile_map:
+  - [[7,5,3,1],[8,6,4,2]]
+  - [[16,14,12,10],[15,13,11,9]]
+module_to_io_groups:
+  1: [1, 2]
+  2: [3, 4]
+  3: [5, 6]
+  4: [7, 8]
+
+# Light geometry parameters
+module_to_tpcs:
+  1: [0, 1]
+  2: [2, 3]
+  3: [4, 5]
+  4: [6, 7]
+n_op_channel: 384
+tpc_to_op_channel:
+    - [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47]
+    - [48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95]
+    - [96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143]
+    - [144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191]
+    - [192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239]
+    - [240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287]
+    - [288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335]
+    - [336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383]
+
+# Light simulation parameters
+singlet_fraction: 0.3
+tau_s: 0.001 # us
+tau_t: 1.530 # us
+op_channel_efficiency: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
+light_gain: [-7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0, -7.0] # ADC us / PE
+light_det_noise_sample_spacing: 0.016 # us
+light_trig_threshold: [-4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000, -4500, -2000]
+light_trig_mode: 1
+light_window: [0, 9.6] # us
+light_trig_window: [1.6, 14.4] # us
+light_digit_sample_spacing: 0.016 # us
+light_nbit: 14
+max_light_truth_ids: 50 # set to zero to disable light truth backtracking
+mc_truth_threshold: 0.1 # pe/us

src/proto_nd_flow/reco/light/adc64_event_generator.py

@@ -65,14 +65,15 @@ class LightADC64EventGenerator(H5FlowGenerator):
         ``wvfm`` datatype::
 
             samples     i2(n_adc,n_channels,n_samples), sample 10-bit ADC value (lowest 6 bits are not used)
+            clipped     ?(n_adc,n_channels),            boolean indicator if any samples are saturated
     '''
     defaults = dict(
         n_adcs = 8,
         n_channels = 64,
         batch_size = 64,
         sync_channel = 0,
         sync_threshold = 40000,
-        sync_buffer = 200,        
+        sync_buffer = 200,
         clock_timestamp_factor = 1.0,
         utime_ms_window = 1000,
         tai_ns_window = 1000,
@@ -89,7 +90,8 @@ def event_dtype(self): return np.dtype([
     ])
 
     def wvfm_dtype(self): return np.dtype([
-        ('samples', 'i2', (self.n_adcs, self.n_channels, self.n_samples))  # sample value
+        ('samples', 'i2', (self.n_adcs, self.n_channels, self.n_samples)),  # sample value
+        ('clipped', '?', (self.n_adcs, self.n_channels))  # boolean indicator if any samples are saturated
     ])
 
     def __init__(self, **params):
@@ -187,6 +189,8 @@ def next(self):
                     event_arr[ievent]['tai_ns'][iadc] = time[data_index]['tai_s']*1e9 + time[data_index]['tai_ns']
                     event_arr[ievent]['wvfm_valid'][iadc, channels] = True
                     wvfm_arr[ievent]['samples'][iadc, channels] = data[data_index]['voltage']
+                    # Check for clipping (ADC saturation at max value ~32764)
+                    wvfm_arr[ievent]['clipped'][iadc, channels] = np.any(np.abs(data[data_index]['voltage']) >= 32764, axis=-1)
 
             # apply different clock frequency
             event_arr['tai_ns'] = (event_arr['tai_ns'] * self.clock_timestamp_factor).astype(event_arr.dtype['tai_ns'].base)

src/proto_nd_flow/reco/light/hit_finder.py

@@ -1,3 +1,4 @@
+from networkx import random_shell_graph
 import numpy as np
 import numpy.ma as ma
 from collections import defaultdict
@@ -22,17 +23,16 @@ class WaveformHitFinder(H5FlowStage):
 
         Parameters:
          - ``wvfm_dset_name``: ``str``, path to input waveforms
+         - ``rms_dset_name``:  ``str``, path to noise RMS dataset from baselining func in wvfm filtering stage
          - ``t_ns_dset_name``: ``str``, path to corrected light PPS timestamps
          - ``hits_dset_name``: ``str``, path to output hits dataset
          - ``near_samples``:   ``int``, number of neighboring samples to keep
          - ``hit_level``:      ``str``, "sipm" or "sum" hit finder (defines variable names)
-         - ``mad_factor``:     ``float``, factor of median abs dev used to define threshold under which noise width is taken
          - ``noise_factor``:   ``float``, factor of noise width used to define threshold over which hit finder is run
          - ``n_bins_rolled``:  ``int``, number of bins over which the rolling threshold of the hit finder is defined
          - ``rt_sqrt_factor``: ``float``, factor used to scale the statistical contribution to the rolling threshold
          - ``pe_weight``:      ``float``, weight applied to the PEs in rolling threshold statistical component
          - ``rising_edge``:    ``bool``, True => hit finder tags first bin over rolling threshold as the hit
-         - ``local_maxima``:   ``bool``, True => uses 5 sample window after rising edge, tags hits as argmax of those samples (otherwise uses derivative based method)
          - ``prompt_window``:  ``float``, Prompt light window in ns (fprompt caluclation input for PSD)
          - ``long_window``:    ``float``, Long light window in ns (fprompt caluclation input for PSD)
          - ``tick_duration``:  ``float``, Duration of ticks in ADC sampling
@@ -68,6 +68,7 @@ class WaveformHitFinder(H5FlowStage):
     class_version = '2.0.0'
 
     default_hits_dset_name = 'light/hits'
+    default_rms_dset_name = 'light/rms'
     default_near_samples = 3
     default_interpolation = 256
     default_global_threshold = 2000
@@ -156,45 +157,38 @@ def calculate_fprompt(self, summed_wvfm, interactions, prompt_window_ns, long_wi
             for j in range(interactions.shape[1]):
                 # Loop over each trap type
                 for k in range(interactions.shape[2]):
+                    # Skip if no peak found in this channel
+                    if not np.any(interactions[i, j, k]):
+                        prompt_int[i, j, k] = np.nan
+                        total_int[i, j, k] = np.nan
+                        continue
                     # Calculate the prompt and total integrals
                     t0_bin = np.argmax(interactions[i, j, k]) - 5
                     end_prompt = t0_bin + prompt_bins
                     end_total = t0_bin + total_bins
-                    prompt_int[i, j, k] = np.sum(summed_wvfm[i, j, k, t0_bin:end_prompt])
-                    total_int[i, j, k] = np.sum(summed_wvfm[i, j, k, t0_bin:end_total])
-        # Calculate fprompt
-        with np.errstate(divide='ignore', invalid='ignore'):
-            fprompt = np.where(
-            (total_int > 0) & (prompt_int > 0) & ~np.isnan(prompt_int) & ~np.isnan(total_int),
-            np.divide(prompt_int, total_int),
-            np.nan
-            )
-        return total_int, fprompt
-
+                    if end_prompt > summed_wvfm.shape[-1] or end_total > summed_wvfm.shape[-1] or t0_bin < 0:
+                        prompt_int[i, j, k] = np.nan
+                        total_int[i, j, k] = np.nan
+                    else:
+                        prompt_int[i, j, k] = np.sum(summed_wvfm[i, j, k, t0_bin:end_prompt])
+                        total_int[i, j, k] = np.sum(summed_wvfm[i, j, k, t0_bin:end_total])
+        # create mask
+        invalid_mask = (total_int <= 0) | (prompt_int <= 0) | np.isnan(prompt_int) | np.isnan(total_int)
+        # Calculate fprompt using masked arrays
+        prompt_int_masked = ma.array(prompt_int, mask=invalid_mask)
+        total_int_masked = ma.array(total_int, mask=invalid_mask)
+        fprompt = ma.divide(prompt_int_masked, total_int_masked)
+        fprompt = fprompt.filled(np.nan)
 
-    def get_noise_threshold(self, wvfms, n_mad_factor):
-        # Initialize median and MAD
-        median = np.ma.median(wvfms, axis=-1)
-        mad = np.ma.median(np.abs(wvfms - median[..., np.newaxis]), axis=-1)
-        # identify outliers in the waveform
-        mad_factor = n_mad_factor * mad
-        noise_mask = np.abs(wvfms - median[..., np.newaxis]) < mad_factor[..., np.newaxis]
-        # set non mask values to nan
-        noise_samples = np.where(noise_mask, wvfms, np.nan)
-        # calculate noise as stddev of noise_samples
-        noise = np.where(np.nansum(noise_samples, axis=-1) != 0,
-                         np.nanstd(noise_samples, axis=-1),
-                         np.nan)
-        return  noise
+        return total_int, fprompt
 
 
     def peak_finder(self, wvfm, noise,
                     n_noise_factor,
                     n_bins_rolled,
                     n_sqrt_rt_factor,
                     pe_weight,
-                    use_rising_edge=False,
-                    use_local_maxima=True):
+                    use_rising_edge=False):
 
         # height = flat threshold over noise (n*sigma)
         height = n_noise_factor * noise[..., np.newaxis] * np.ones(wvfm.shape[-1])
@@ -211,24 +205,14 @@ def peak_finder(self, wvfm, noise,
         first_bins_over[..., 1:] &= ~bins_over_dynamic_threshold[..., :-1]
         if use_rising_edge:
             return first_bins_over
-        # Peak finding
-        elif use_local_maxima:
-            # check 5 bins after first_bins_over and add argmax
-            peak_bins = np.zeros_like(wvfm, dtype=bool)
-            first_bins_indices = np.where(first_bins_over)
-            for idx in zip(*first_bins_indices):
-                start_idx = idx[-1]
-                end_idx = min(start_idx + 5, wvfm.shape[-1])
-                peak_bin = np.argmax(wvfm[idx[:-1] + (slice(start_idx, end_idx),)])
-                peak_bins[idx[:-1] + (start_idx + peak_bin,)] = True
-        else:
-            # Derivative-based peak detection
-            wvfm_d1 = np.gradient(wvfm, axis=-1)
-            wvfm_d2 = np.gradient(wvfm_d1, axis=-1)
-            peak_bins = (wvfm > dynamic_threshold) & (wvfm > height) & \
-                (wvfm_d1 < 0) & (wvfm_d2 < 0)
-            # Keep only the first peak in consecutive runs
-            peak_bins[..., 1:] &= ~peak_bins[..., :-1]
+        # check 5 bins after first_bins_over and add argmax
+        peak_bins = np.zeros_like(wvfm, dtype=bool)
+        first_bins_indices = np.where(first_bins_over)
+        for idx in zip(*first_bins_indices):
+            start_idx = idx[-1]
+            end_idx = min(start_idx + 5, wvfm.shape[-1])
+            peak_bin = np.argmax(wvfm[idx[:-1] + (slice(start_idx, end_idx),)])
+            peak_bins[idx[:-1] + (start_idx + peak_bin,)] = True
 
         return peak_bins
 
@@ -237,19 +221,18 @@ def __init__(self, **params):
         super(WaveformHitFinder, self).__init__(**params)
         self.wvfm_dset_name = params.get('wvfm_dset_name')
         self.wvfm_align_dset_name = f'{self.wvfm_dset_name}/alignment'
+        self.rms_dset_name = params.get('rms_dset_name')
         self.t_ns_dset_name = params.get('t_ns_dset_name')
         self.hits_dset_name = params.get('hits_dset_name',
                                          self.default_hits_dset_name)
         self.near_samples = params.get('near_samples',
                                        self.default_near_samples)
         self.hit_level = params.get('hit_level')
-        self.mad_factor = params.get('mad_factor')
         self.noise_factor = params.get('noise_factor')
         self.n_bins_rolled = params.get('n_bins_rolled')
         self.rt_sqrt_factor = params.get('rt_sqrt_factor')
         self.pe_weight = params.get('pe_weight')
         self.rising_edge = params.get('rising_edge')
-        self.local_maxima = params.get('local_maxima')
         self.prompt_window = params.get('prompt_window')
         self.long_window = params.get('long_window')
         self.tick_duration = params.get('tick_duration')
@@ -334,15 +317,15 @@ def run(self, source_name, source_slice, cache):
 
         wvfm_det = np.broadcast_to(np.arange(wvfms.shape[-2]).reshape(1,1,-1), wvfms.shape[:-1])
 
-        noise = self.get_noise_threshold(wvfms, self.mad_factor)
+        noise = cache[self.rms_dset_name].reshape(cache[source_name].shape)[
+            'rms']
 
         peaks_found = self.peak_finder(wvfms, noise,
                                       self.noise_factor,
                                       self.n_bins_rolled,
                                       self.rt_sqrt_factor,
                                       self.pe_weight,
-                                      self.rising_edge,
-                                      self.local_maxima)
+                                      self.rising_edge)
 
         t0_bin = np.argmax(peaks_found, axis=-1)
 
@@ -357,6 +340,7 @@ def run(self, source_name, source_slice, cache):
         threshold_mask = peak_max >=self.threshold[peaks[1:-1]].ravel()
 
         if self.hit_level=="sum_tpc" or self.hit_level=="sum":
+
             integrals, fprompts = self.calculate_fprompt(wvfms, peaks_found,
                                                          self.prompt_window,
                                                          self.long_window,