pymchelper.detector module¶
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class
pymchelper.detector.
Detector
[source]¶ Bases:
object
Detector data including scoring mesh description.
This class handles in universal way data generated with MC code. It includes data (
data
anddata_raw
fields) and optinal errors (error
anderror_raw
). Detector holds also up to 3 binning axis (x
,y
andz
fields). Scored quantity can be assigned aname
(i.e. dose) andunit
(i.e. Gy). Several other fields are also used:- nstat: number of simulated histories
- counter: number of files read to construct detector object
- corename: common core part of input files defining a name of detector
- error_type: none, stderr or stddev - error type
Detector data can be either read from the file (see
fromfile
method inio
module or constructed directly:>>> d = Detector() >>> d.x = MeshAxis(n=2, min_val=0.0, max_val=10.0, name="X", unit="cm", binning=MeshAxis.BinningType.linear) >>> d.x.data array([ 2.5, 7.5]) >>> d.y = MeshAxis(n=3, min_val=0.0, max_val=150.0, name="Y", unit="cm", binning=MeshAxis.BinningType.linear) >>> d.y.data array([ 25., 75., 125.]) >>> d.z = MeshAxis(n=1, min_val=0.0, max_val=1.0, name="Z", unit="cm", binning=MeshAxis.BinningType.linear) >>> d.z.data array([ 0.5]) >>> d.data_raw = np.arange(6) >>> d.data.shape (2, 3, 1) >>> d.data array([[[0], [1], [2]], [[3], [4], [5]]])
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axis
(id)[source]¶ Mesh axis selector method based on integer id’s.
Instead of getting mesh axis data by calling d.x, d.y or d.z (assuming d an object of Detector class) we can get that data by calling d.axis(0), d.axis(1) or d.axis(2). See for example: >>> d = Detector() >>> d.x = MeshAxis(n=2, min_val=0.0, max_val=10.0, name=”X”, unit=”cm”, binning=MeshAxis.BinningType.linear) >>> d.y = MeshAxis(n=3, min_val=0.0, max_val=150.0, name=”Y”, unit=”cm”, binning=MeshAxis.BinningType.linear) >>> d.z = MeshAxis(n=1, min_val=0.0, max_val=1.0, name=”Z”, unit=”cm”, binning=MeshAxis.BinningType.linear) >>> d.axis(1) MeshAxis(n=3, min_val=0.0, max_val=150.0, name=’Y’, unit=’cm’, binning=<BinningType.linear: 0>)
Parameters: id – axis id (0, 1 or 2) Returns: MeshAxis object
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data
¶ 3-D view of detector data.
Detector data are stored originally in data_raw 1-D array. This property provides efficient view of detector data, suitable for numpy-like indexing.
>>> d = Detector() >>> d.x = MeshAxis(n=2, min_val=0.0, max_val=10.0, name="X", unit="cm", binning=MeshAxis.BinningType.linear) >>> d.y = MeshAxis(n=3, min_val=0.0, max_val=150.0, name="Y", unit="cm", binning=MeshAxis.BinningType.linear) >>> d.z = MeshAxis(n=1, min_val=0.0, max_val=1.0, name="Z", unit="cm", binning=MeshAxis.BinningType.linear) >>> d.data_raw = np.arange(6) >>> d.data.shape (2, 3, 1) >>> d.data[1, 2, 0] 5
Returns: reshaped view of data_raw
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dimension
¶ Let’s take again detector d with YZ scoring. >>> d = Detector() >>> d.x = MeshAxis(n=1, min_val=0.0, max_val=1.0, name=”X”, unit=”cm”, binning=MeshAxis.BinningType.linear) >>> d.y = MeshAxis(n=3, min_val=0.0, max_val=150.0, name=”Y”, unit=”cm”, binning=MeshAxis.BinningType.linear) >>> d.z = MeshAxis(n=2, min_val=0.0, max_val=2.0, name=”Z”, unit=”cm”, binning=MeshAxis.BinningType.linear) >>> d.dimension 2
Returns: number of axes which have more than one point
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error
¶ 3-D view of detector error
For more details see
data
property. :return:
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plot_axis
(id)[source]¶ Calculate new order of detector axis, axis with data (n>1) comes first Axes with constant value goes last.
Let’s take a detector d with YZ scoring. >>> d = Detector() >>> d.x = MeshAxis(n=1, min_val=0.0, max_val=1.0, name=”X”, unit=”cm”, binning=MeshAxis.BinningType.linear) >>> d.y = MeshAxis(n=3, min_val=0.0, max_val=150.0, name=”Y”, unit=”cm”, binning=MeshAxis.BinningType.linear) >>> d.z = MeshAxis(n=2, min_val=0.0, max_val=2.0, name=”Z”, unit=”cm”, binning=MeshAxis.BinningType.linear)
First axis for plotting will be Y (as X axis holds only one bin): >>> d.plot_axis(0) MeshAxis(n=3, min_val=0.0, max_val=150.0, name=’Y’, unit=’cm’, binning=<BinningType.linear: 0>)
Second axis for plotting will be Z (its the next after Y with n > 1 bins) >>> d.plot_axis(1) MeshAxis(n=2, min_val=0.0, max_val=2.0, name=’Z’, unit=’cm’, binning=<BinningType.linear: 0>)
Finally the third axis will be X, but it cannot be used for plotting as it has only one bin. >>> d.plot_axis(2) MeshAxis(n=1, min_val=0.0, max_val=1.0, name=’X’, unit=’cm’, binning=<BinningType.linear: 0>)
Parameters: id – axis number (0, 1 or 2) Returns: axis object
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class
pymchelper.detector.
ErrorEstimate
[source]¶ Bases:
enum.IntEnum
An enumeration.
-
none
= 0¶
-
stddev
= 2¶
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stderr
= 1¶
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class
pymchelper.detector.
MeshAxis
[source]¶ Bases:
pymchelper.detector.MeshAxis
Scoring mesh axis data.
It can represent an axis variety of scorers: x,y or z in cartesian scoring, r, rho or z in cylindrical. An axis represents a sequence of
n
numbers, defining linear or logarithmic binning. This sequence of numbers is not stored in the memory, but can be generated using data property method.min_val
is lowest bin left edge, max_val is highest bin right edgename
can be used to define physical quantity (i.e. position, energy, angle).unit
gives physical units (i.e. cm, MeV, mrad).MeshAxis
is constructed as immutable data structure, thus it is possible to set field values only upon object creation. Later they are available for read only.>>> x = MeshAxis(n=10, min_val=0.0, max_val=30.0, name="Position", unit="cm", binning=MeshAxis.BinningType.linear) >>> x.n, x.min_val, x.max_val (10, 0.0, 30.0) >>> x.n = 5 Traceback (most recent call last): ... AttributeError: can't set attribute
binning
field (use internalBinningType.linear
orBinningType.logarithmic
) can distinguish log from linear binning-
data
¶ Generates linear or logarithmic sequence of
n
numbers.These numbers are middle points of the bins defined by
n
,min_val
andmax_val
parameters.>>> x = MeshAxis(n=10, min_val=0.0, max_val=10.0, name="X", unit="cm", binning=MeshAxis.BinningType.linear) >>> x.data array([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5])
Binning may also consist of one bin: >>> x = MeshAxis(n=1, min_val=0.0, max_val=5.0, name=”X”, unit=”cm”, binning=MeshAxis.BinningType.linear) >>> x.data array([ 2.5])
Logarithmic binning works as well, middle bin points are calculated as geometrical mean. Here we define 3 bins: [1,4], [4,16], [16,64]. >>> x = MeshAxis(n=3, min_val=1.0, max_val=64.0, name=”X”, unit=”cm”, binning=MeshAxis.BinningType.logarithmic) >>> x.data array([ 2., 8., 32.])
For the same settings as below linear scale gives as expected different sequence: >>> x = MeshAxis(n=3, min_val=1.0, max_val=64.0, name=”X”, unit=”cm”, binning=MeshAxis.BinningType.linear) >>> x.data array([ 11.5, 32.5, 53.5])
For logarithmic axis min_val has to be positive: >>> x = MeshAxis(n=3, min_val=-2.0, max_val=64.0, name=”X”, unit=”cm”, binning=MeshAxis.BinningType.logarithmic) >>> x.data Traceback (most recent call last): … Exception: Left edge of first bin (-2) is not positive
Returns:
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