add class to compute statistics on the fly

src/nectarchain/tests/test_stats.py

@@ -0,0 +1,127 @@
+import pytest
+
+def test_stats_simple():
+    import numpy as np
+    from nectarchain.utils.stats import Stats
+
+    s = Stats()
+    s.add(1)
+    s.add(2)
+    s.add(3)
+
+    np.testing.assert_allclose( s.mean, np.array([2.]) )
+    np.testing.assert_allclose( s.std, np.array([1.]) )
+    np.testing.assert_allclose( s.max, np.array([3.]))
+    np.testing.assert_allclose( s.min, np.array([1.]))
+    np.testing.assert_allclose( s.count, np.array([3]))
+
+def test_stats_camera():
+    import numpy as np
+    from nectarchain.utils.stats import Stats
+
+    s = Stats( shape = (5) )
+    s.add( np.array([0,0,0,0,0]), validmask = np.array( [False,False,False,False,False] )  )    
+    s.add( np.array([0,1,2,3,4]), validmask = np.array( [True,True,True,True,True] )  )    
+    s.add( np.array([1,2,3,4,5]), validmask = np.array( [True,True,True,False,False] ) )    
+    s.add( np.array([2,3,4,5,6]), validmask = None )
+    s.add( np.array([3,4,5,6,7]), validmask = np.array( [False,False,False,False,False] ) )
+
+    np.testing.assert_allclose( s.count,    np.array([3,3,3,2,2]) )
+    np.testing.assert_allclose( s.mean,     np.array([1,2,3,4,5]) )
+    np.testing.assert_allclose( s.variance, np.array([1,1,1,2,2]) )
+    np.testing.assert_allclose( s.min,      np.array([0,1,2,3,4]) )
+    np.testing.assert_allclose( s.max,      np.array([2,3,4,5,6]) )
+
+def test_stats_copy():
+    import numpy as np
+    from nectarchain.utils.stats import Stats, CameraStats, CameraSampleStats
+
+    a = Stats()
+    assert id(a) != id(a.copy())
+
+    a = CameraStats()
+    assert id(a) != id(a.copy())
+
+    a = CameraSampleStats()
+    assert id(a) != id(a.copy())
+
+def test_stats_lowcounts():
+
+    import numpy as np
+    from nectarchain.utils.stats import Stats
+
+    s = Stats( shape = (5) )
+    s.add( np.array([0,0,0,0,0]), validmask = np.array( [True,True,True,True,False] ))
+    s.add( np.array([1,1,1,1,1]))
+    s.add( np.array([2,2,2,2,2]))
+
+    np.testing.assert_array_equal( s.get_lowcount_mask(3), np.array([False,False,False,False,True]) )
+
+
+def test_stats_merge():
+    import numpy as np
+    from nectarchain.utils.stats import Stats
+
+    s = Stats( shape = (5) )
+    s.add( np.array([0,0,0,0,0]), validmask = np.array( [False,False,False,False,False] )  )    
+    s.add( np.array([0,1,2,3,4]), validmask = np.array( [True,True,True,True,True] )  )   
+
+    s2 =  Stats( shape = (5) )
+    s2.add( np.array([1,2,3,4,5]), validmask = np.array( [True,True,True,False,False] ) )    
+    s2.add( np.array([2,3,4,5,6]), validmask = None )
+    s2.add( np.array([3,4,5,6,7]), validmask = np.array( [False,False,False,False,False] ) )
+
+    s.merge(s2)
+
+    np.testing.assert_allclose( s.count,    np.array([3,3,3,2,2]) )
+    np.testing.assert_allclose( s.mean,     np.array([1,2,3,4,5]) )
+    np.testing.assert_allclose( s.variance, np.array([1,1,1,2,2]) )
+    np.testing.assert_allclose( s.min,      np.array([0,1,2,3,4]) )
+    np.testing.assert_allclose( s.max,      np.array([2,3,4,5,6]) )
+
+
+def test_stats_merge2():
+    import numpy as np
+    from nectarchain.utils.stats import Stats
+
+    s = Stats( shape = (5) )
+    s.add( np.array([0,0,0,0,0]), validmask = np.array( [False,False,False,False,False] )  )    
+    s.add( np.array([0,1,2,3,4]), validmask = np.array( [True,True,True,True,True] )  )   
+
+    s2 =  Stats( shape = (5) )
+    s2.add( np.array([1,2,3,4,5]), validmask = np.array( [True,True,True,False,False] ) )    
+    s2.add( np.array([2,3,4,5,6]), validmask = None )
+    s2.add( np.array([3,4,5,6,7]), validmask = np.array( [False,False,False,False,False] ) )
+
+    s += s2
+
+    np.testing.assert_allclose( s.count,    np.array([3,3,3,2,2]) )
+    np.testing.assert_allclose( s.mean,     np.array([1,2,3,4,5]) )
+    np.testing.assert_allclose( s.variance, np.array([1,1,1,2,2]) )
+    np.testing.assert_allclose( s.min,      np.array([0,1,2,3,4]) )
+    np.testing.assert_allclose( s.max,      np.array([2,3,4,5,6]) )
+
+
+def test_stats_merge3():
+    import numpy as np
+    from nectarchain.utils.stats import Stats
+
+    s1 = Stats( shape = (5) )
+    s1.add( np.array([0,0,0,0,0]), validmask = np.array( [False,False,False,False,False] )  )    
+    s1.add( np.array([0,1,2,3,4]), validmask = np.array( [True,True,True,True,True] )  )   
+
+    s2 =  Stats( shape = (5) )
+    s2.add( np.array([1,2,3,4,5]), validmask = np.array( [True,True,True,False,False] ) )    
+    s2.add( np.array([2,3,4,5,6]), validmask = None )
+    s2.add( np.array([3,4,5,6,7]), validmask = np.array( [False,False,False,False,False] ) )
+
+    s  = s1 + s2
+
+    assert id(s) != id(s1)
+    assert id(s) != id(s2) 
+
+    np.testing.assert_allclose( s.count,    np.array([3,3,3,2,2]) )
+    np.testing.assert_allclose( s.mean,     np.array([1,2,3,4,5]) )
+    np.testing.assert_allclose( s.variance, np.array([1,1,1,2,2]) )
+    np.testing.assert_allclose( s.min,      np.array([0,1,2,3,4]) )
+    np.testing.assert_allclose( s.max,      np.array([2,3,4,5,6]) )

src/nectarchain/utils/stats.py

@@ -0,0 +1,202 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+"""
+Code that implement Welford's algorithm for stats computation
+
+This is inspired by the implementation done at https://github.com/a-mitani/welford
+"""
+import numpy as np
+import warnings
+from copy import deepcopy
+
+class Stats:
+    """class Stats
+    Accumulator object for Welfords online / parallel variance algorithm.
+     
+
+    Examples
+    --------
+    Example with only one variable
+    >>> from nectarchain.utils.stats import Stats
+    >>> s = Stats()
+    >>> s.add(1)
+    >>> s.add(2)
+    >>> s.add(3)
+    >>> s.mean
+    2
+    >>> s.std
+    1
+    """
+
+    def __init__(self,shape=(1,)):
+        """__init__
+
+        Initialize with an optional data. 
+        For the calculation efficiency, Welford's method is not used on the initialization process.
+
+        """
+        # Initialize instance attributes
+        self._shape = shape
+        self._count = np.zeros( shape, dtype=int )
+        self._m     = np.zeros( shape, dtype=float )
+        self._s     = np.zeros( shape, dtype=float )
+        self._min   = np.full( shape, np.inf )
+        self._max   = np.full( shape, -np.inf )
+
+    def __str__(self):
+        infos = ""
+        infos += f"mean: {self.mean}" + "\n"
+        infos += f"std: {self.stddev}" + "\n"
+        infos += f"min: {self.min}" + "\n"
+        infos += f"max: {self.max}" + "\n"
+        infos += f"count: {self.count}" + "\n"
+        infos += f"shape: {self.shape}" + "\n"
+        return infos
+
+    def __repr__(self):
+        return self.__str__()
+
+    def copy(self):
+        return deepcopy(self)
+
+    def __add__(self, other):
+        r = self.copy()
+        r.merge(other)
+        return r
+
+    def __iadd__(self,other):
+        self.merge(other)
+        return self
+
+    @property
+    def shape(self):
+        return self._shape
+
+    @property
+    def count(self):
+        return self._count
+
+    @property
+    def mean(self):
+        return self._m
+
+    @property
+    def variance(self):
+        return self._getvars(ddof=1)
+
+    @property
+    def stddev(self):
+        return np.sqrt(self._getvars(ddof=1))
+
+    @property
+    def std(self):
+        return self.stddev
+
+    @property
+    def min(self):
+        return self._min
+
+    @property
+    def max(self):
+        return self._max
+
+    def get_lowcount_mask(self,mincount=3):
+        return self._count<mincount
+
+
+    def add(self, element, validmask = None):
+        """
+        Add entry. If mask is given, it will only update the entry from mask
+        element
+
+        Parameters
+        ----------
+        element : np.array
+            array of element to added to the stat object (must be similar shape as the Stats object)
+        validmask : np.array
+            array that indicate which value to use. Only element entry where validmask is True will be added. It must be a boolean array of the same shape as element
+
+        """
+
+        # Welford's algorithm
+        if validmask is None:
+            self._count += 1
+            delta = element - self._m
+            self._m += delta / self._count
+            self._s += delta * (element - self._m)
+            self._min = np.minimum( self._min, element )
+            self._max = np.maximum( self._max, element )
+        else:
+            self._count[validmask] += 1
+            delta = element[validmask] - self._m[validmask]
+            self._m[validmask] += delta / self._count[validmask]
+            self._s[validmask] += delta * (element[validmask] - self._m[validmask])
+            self._min[validmask] = np.minimum( self._min, element )[validmask]
+            self._max[validmask] = np.maximum( self._max, element )[validmask]
+
+
+    def merge(self, other):
+        """Merge this accumulator with another one.
+        
+        Parameters
+        ----------
+        other: nectarchain.utils.stats.Stats
+            Another object of the same type which you want to combined the statistics
+        """
+        if self._shape != other._shape:
+            raise ValueError(f"Trying to merge from a different shape this: {self._shape}, given: {other._shape}")        
+
+        #with warnings.catch_warnings():
+        count = self._count + other._count
+        delta = self._m - other._m
+        delta2 = delta * delta
+        mean = (self._count * self._m + other._count * other._m) / count
+        s = self._s + other._s + delta2 * (self._count * other._count) / count
+
+        self._count = count
+        self._m = mean
+        self._s = s
+
+        self._min = np.minimum( self._min, other._min )
+        self._max = np.maximum( self._max, other._max )
+
+
+    def _getvars(self, ddof):
+        #with warnings.catch_warnings():
+        #    warnings.simplefilter("ignore", category=RuntimeWarning)
+        variance = self._s / (self._count - ddof)
+        variance[ self.get_lowcount_mask(1+ddof) ] = np.nan
+        return variance
+
+
+class CameraStats(Stats):
+    """class CameraStats
+    Accumulator object for Welfords online / parallel variance algorithm, specialized for camera info
+    """ 
+
+    def __init__(self,shape=(2,1855), *args, **kwargs):
+        super().__init__(shape,*args,**kwargs)
+
+
+
+class CameraSampleStats(Stats):
+    """class CameraSampleStats
+    Accumulator object for Welfords online / parallel variance algorithm, specialized for trace info
+
+    Examples
+    --------
+    Cumulating the rawdata from a run to get the average waveform
+    >>> from nectarchain.utils.stats import CameraSampleStats
+    >>> from ctapipe_io_nectarcam import NectarCAMEventSource
+
+    >>> reader = NectarCAMEventSource(input_url='NectarCAM.Run4560.00??.fits.fz')
+
+    >>> s = CameraSampleStats()
+    >>> for event in reader:
+    >>>     s.add( event.r0.tel[0].waveform, validmask=~evt.mon.tel[0].pixel_status.hardware_failing_pixels )
+    >>> print(s.mean)
+    """ 
+
+    def __init__(self,shape=(2,1855,60), *args, **kwargs):
+        super().__init__(shape,*args,**kwargs)