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Commit 7e8d8da9 authored by Zineb Aly's avatar Zineb Aly Committed by Tamas Gal
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outsource reconstruction data in separate arrays

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CHANGELOG.rst
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Unreleased changes
------------------
* update of reco data from offline files
Version 0
---------
......@@ -44,7 +44,7 @@ Version 0
0.3.0 / 2019-11-19
~~~~~~~~~~~~~~~~~~~
* Preliminary Jpp timeslice reader prototype
* Updated ``AaetReader``
* Updated ``AanetReader``
* Updated docs
0.2.1 / 2019-11-15
......
README.rst
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......@@ -645,6 +645,90 @@ to get a specific value from track 0 in event 0, let's say for example the likli
>>>r[0].tracks[0].lik
294.6407542676734
to get the reconstruction parameters, first take a look at the available reconstruction keys:
.. code-block:: python3
>>>r.best_reco.dtype.names
['JGANDALF_BETA0_RAD',
'JGANDALF_BETA1_RAD',
'JGANDALF_CHI2',
'JGANDALF_NUMBER_OF_HITS',
'JENERGY_ENERGY',
'JENERGY_CHI2',
'JGANDALF_LAMBDA',
'JGANDALF_NUMBER_OF_ITERATIONS',
'JSTART_NPE_MIP',
'JSTART_NPE_MIP_TOTAL',
'JSTART_LENGTH_METRES',
'JVETO_NPE',
'JVETO_NUMBER_OF_HITS',
'JENERGY_MUON_RANGE_METRES',
'JENERGY_NOISE_LIKELIHOOD',
'JENERGY_NDF',
'JENERGY_NUMBER_OF_HITS']
the keys above can also be accessed with a tab completion:
.. image:: https://git.km3net.de/km3py/km3io/raw/master/examples/pictures/reco.png
to get a numpy `recarray <https://docs.scipy.org/doc/numpy/reference/generated/numpy.recarray.html>`__ of all fit data of the best reconstructed track:
.. code-block:: python3
>>>r.best_reco
to get an array of a parameter of interest, let's say `'JENERGY_ENERGY'`:
.. code-block:: python3
>>>r.best_reco['JENERGY_ENERGY']
array([1141.87137899, 4708.16378575, 499.7243005 , 103.54680875,
208.6103912 , 1336.52338666, 998.87632267, 1206.54345674,
16.28973662])
**Note**: In km3io, the best fit is defined as the track fit with the maximum reconstruction stages. When "nan" is returned, it means that the reconstruction parameter of interest is not found. for example, in the case of muon simulations: if `[1, 2]` are the reconstruction stages, then only the fit parameters corresponding to the stages `[1, 2]` are found in the Offline files, the remaining fit parameters corresponding to the stages `[3, 4, 5]` are all filled with nan.
to get a numpy recarray of the fit data of tracks with specific reconstruction stages, let's say `[1, 2, 3, 4, 5]` in the case of a muon track reconstruction:
.. code-block:: python3
>>>r.get_reco_fit([1, 2, 3, 4, 5])
again, to get the reconstruction parameters names:
.. code-block:: python3
>>>r.get_reco_fit([1, 2, 3, 4, 5]).dtype.names
('JGANDALF_BETA0_RAD',
'JGANDALF_BETA1_RAD',
'JGANDALF_CHI2',
'JGANDALF_NUMBER_OF_HITS',
'JENERGY_ENERGY',
'JENERGY_CHI2',
'JGANDALF_LAMBDA',
'JGANDALF_NUMBER_OF_ITERATIONS',
'JSTART_NPE_MIP',
'JSTART_NPE_MIP_TOTAL',
'JSTART_LENGTH_METRES',
'JVETO_NPE',
'JVETO_NUMBER_OF_HITS',
'JENERGY_MUON_RANGE_METRES',
'JENERGY_NOISE_LIKELIHOOD',
'JENERGY_NDF',
'JENERGY_NUMBER_OF_HITS')
to get the reconstruction data of interest, for example ['JENERGY_ENERGY']:
.. code-block:: python3
>>>r.get_reco_fit([1, 2, 3, 4, 5])['JENERGY_ENERGY']
array([1141.87137899, 4708.16378575, 499.7243005 , 103.54680875,
208.6103912 , 1336.52338666, 998.87632267, 1206.54345674,
16.28973662])
**Note**: When the reconstruction stages of interest are not found in all your data file, an error is raised.
reading mc hits data
""""""""""""""""""""
......
examples/pictures/reco.png 0 → 100644
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examples/pictures/reco.png

15.1 KiB

km3io/offline.py
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import uproot
import numpy as np
import warnings
# 110 MB based on the size of the largest basket found so far in km3net
......@@ -179,8 +180,7 @@ class OfflineKeys:
'JSTART_NPE_MIP', 'JSTART_NPE_MIP_TOTAL',
'JSTART_LENGTH_METRES', 'JVETO_NPE', 'JVETO_NUMBER_OF_HITS',
'JENERGY_MUON_RANGE_METRES', 'JENERGY_NOISE_LIKELIHOOD',
'JENERGY_NDF', 'JENERGY_NUMBER_OF_HITS', 'JCOPY_Z_M'
]
'JENERGY_NDF', 'JENERGY_NUMBER_OF_HITS', 'JCOPY_Z_M']
return self._fit_keys
@property
......@@ -320,6 +320,7 @@ class OfflineReader:
self._mc_hits = None
self._mc_tracks = None
self._keys = None
self._best_reco = None
self._header = None
def __getitem__(self, item):
......@@ -431,6 +432,157 @@ class OfflineReader:
[self._data[key] for key in self.keys.mc_tracks_keys])
return self._mc_tracks
@property
def best_reco(self):
"""returns the best reconstructed track fit data. The best fit is defined
as the track fit with the maximum reconstruction stages. When "nan" is
returned, it means that the reconstruction parameter of interest is not
found. for example, in the case of muon simulations: if [1, 2] are the
reconstruction stages, then only the fit parameters corresponding to the
stages [1, 2] are found in the Offline files, the remaining fit parameters
corresponding to the stages 3, 4, 5 are all filled with nan.
Returns
-------
numpy recarray
a recarray of the best track fit data (reconstruction data).
"""
if self._best_reco is None:
keys = ", ".join(self.keys.fit_keys[:-1])
empty_fit_info = np.array([match for match in
self._find_empty(self.tracks.fitinf)])
fit_info = [i for i,j in zip(self.tracks.fitinf,
empty_fit_info[:,1]) if j is not None]
stages = self._get_max_reco_stages(self.tracks.rec_stages)
fit_data = np.array([i[j] for i,j in zip(fit_info, stages[:,2])])
rows_size = len(max(fit_data, key=len))
equal_size_data = np.vstack([np.hstack([i, np.zeros(rows_size-len(i))
+ np.nan]) for i in fit_data])
self._best_reco = np.core.records.fromarrays(equal_size_data.transpose(),
names=keys)
return self._best_reco
def _get_max_reco_stages(self, reco_stages):
"""find the longest reconstructed track based on the maximum size of
reconstructed stages.
Parameters
----------
reco_stages : chunked array
chunked array of all the reconstruction stages of all tracks.
In km3io, it is accessed with
km3io.OfflineReader(my_file).tracks.rec_stages .
Returns
-------
numpy array
array with 3 columns: *list of the maximum reco_stages
*lentgh of the maximum reco_stages
*position of the maximum reco_stages
"""
empty_reco_stages = np.array([match for match in
self._find_empty(reco_stages)])
max_reco_stages = np.array([[max(i, key=len), len(max(i, key=len)),
i.index(max(i, key=len))] for i,j in
zip(reco_stages, empty_reco_stages[:,1])
if j is not None])
return max_reco_stages
def get_reco_fit(self, stages):
"""construct a numpy recarray of the fit information (reconstruction
data) of the tracks reconstructed following the reconstruction stages
of interest.
Parameters
----------
stages : list
list of reconstruction stages of interest. for example
[1, 2, 3, 4, 5].
Returns
-------
numpy recarray
a recarray of the fit information (reconstruction data) of
the tracks of interest.
Raises
------
ValueError
ValueError raised when the reconstruction stages of interest
are not found in the file.
"""
keys = ", ".join(self.keys.fit_keys[:-1])
fit_info = self.tracks.fitinf
rec_stages = np.array([match for match in
self._find_rec_stages(stages)])
if np.all(rec_stages[:,1]==None):
raise ValueError("The stages {} are not found in your file."
.format(str(stages)))
else:
fit_data = np.array([i[k] for i,j,k in zip(fit_info,
rec_stages[:,0], rec_stages[:,1])
if k is not None])
rec_array = np.core.records.fromarrays(fit_data.transpose(),
names=keys)
return rec_array
def _find_rec_stages(self, stages):
"""find the index of reconstruction stages of interest in a
list of multiple reconstruction stages.
Parameters
----------
stages : list
list of reconstruction stages of interest. for example
[1, 2, 3, 4, 5].
Yieldsma
------
generator
the track id and the index of the reconstruction stages of
interest if found. If the reconstruction stages of interest
are not found, None is returned as the stages index.
"""
for trk_index, rec_stages in enumerate(self.tracks.rec_stages):
try:
stages_index = rec_stages.index(stages)
except ValueError:
stages_index = None
yield trk_index, stages_index
continue
yield trk_index, stages_index
def _find_empty(self, array):
"""finds empty lists/arrays in an awkward array
Parameters
----------
array : awkward array
Awkward array of data of interest. For example:
km3io.OfflineReader(my_file).tracks.fitinf .
Yields
------
generator
the empty list id and the index of the empty list. When
data structure (list) is simply empty, None is written in the
corresponding index. However, when data structure (list) is not
empty and does not contain an empty list, then False is written in the
corresponding index.
"""
for i, rs in enumerate(array):
try:
if len(rs)==0:
j = None
if len(rs)!=0:
j = rs.index([])
except ValueError:
j = False # rs not empty but [] not found
yield i, j
continue
yield i, j
class OfflineEvents:
"""wrapper for offline events"""
......
notebooks/OfflineReader_tutorial.ipynb
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%% Cell type:code id: tags:
``` python
# Add file to current python path
from pathlib import Path
import sys
sys.path.append(str(Path.cwd().parent))
Path.cwd()
# test samples directory - aanet test file
files_path = Path.cwd().parent / 'tests/samples'
aanet_file = files_path / 'aanet_v2.0.0.root'
```
%% Cell type:markdown id: tags:
# Read offline files (aanet)
%% Cell type:code id: tags:
``` python
import km3io as ki
r = ki.OfflineReader(aanet_file)
r
```
%% Output
<km3io.aanet.OfflineReader at 0x7f87bd4ce310>
<km3io.offline.OfflineReader at 0x7fb60c2dc590>
%% Cell type:markdown id: tags:
To explore all the available branches in our offline file:
%% Cell type:code id: tags:
``` python
r.keys
```
%% Output
Events keys are:
id
det_id
mc_id
run_id
mc_run_id
frame_index
trigger_mask
trigger_counter
overlays
hits
trks
w
w2list
w3list
mc_t
mc_hits
mc_trks
comment
index
flags
t.fSec
t.fNanoSec
Hits keys are:
hits.id
hits.dom_id
hits.channel_id
hits.tdc
hits.tot
hits.trig
hits.pmt_id
hits.t
hits.a
hits.pos.x
hits.pos.y
hits.pos.z
hits.dir.x
hits.dir.y
hits.dir.z
hits.pure_t
hits.pure_a
hits.type
hits.origin
hits.pattern_flags
Tracks keys are:
trks.fUniqueID
trks.fBits
trks.id
trks.pos.x
trks.pos.y
trks.pos.z
trks.dir.x
trks.dir.y
trks.dir.z
trks.t
trks.E
trks.len
trks.lik
trks.type
trks.rec_type
trks.rec_stages
trks.status
trks.mother_id
trks.fitinf
trks.hit_ids
trks.error_matrix
trks.comment
Mc hits keys are:
mc_hits.id
mc_hits.dom_id
mc_hits.channel_id
mc_hits.tdc
mc_hits.tot
mc_hits.trig
mc_hits.pmt_id
mc_hits.t
mc_hits.a
mc_hits.pos.x
mc_hits.pos.y
mc_hits.pos.z
mc_hits.dir.x
mc_hits.dir.y
mc_hits.dir.z
mc_hits.pure_t
mc_hits.pure_a
mc_hits.type
mc_hits.origin
mc_hits.pattern_flags
Mc tracks keys are:
mc_trks.fUniqueID
mc_trks.fBits
mc_trks.id
mc_trks.pos.x
mc_trks.pos.y
mc_trks.pos.z
mc_trks.dir.x
mc_trks.dir.y
mc_trks.dir.z
mc_trks.t
mc_trks.E
mc_trks.len
mc_trks.lik
mc_trks.type
mc_trks.rec_type
mc_trks.rec_stages
mc_trks.status
mc_trks.mother_id
mc_trks.fitinf
mc_trks.hit_ids
mc_trks.error_matrix
mc_trks.comment
%% Cell type:markdown id: tags:
# read events data
%% Cell type:code id: tags:
``` python
r.events
```
%% Output
<OfflineEvents: 10 parsed events>
%% Cell type:markdown id: tags:
number of events:
%% Cell type:code id: tags:
``` python
len(r.events)
```
%% Output
10
%% Cell type:code id: tags:
``` python
r.events.id
```
%% Output
<ChunkedArray [1 2 3 ... 8 9 10] at 0x7f87906e86d0>
<ChunkedArray [1 2 3 ... 8 9 10] at 0x7fb5da350b50>
%% Cell type:code id: tags:
``` python
r.events.det_id
```
%% Output
<ChunkedArray [44 44 44 ... 44 44 44] at 0x7f87906e87d0>
<ChunkedArray [44 44 44 ... 44 44 44] at 0x7fb5da3506d0>
%% Cell type:code id: tags:
``` python
r.events.frame_index
```
%% Output
<ChunkedArray [182 183 202 ... 185 185 204] at 0x7f87906e8c10>
<ChunkedArray [182 183 202 ... 185 185 204] at 0x7fb5da350950>
%% Cell type:code id: tags:
``` python
r.events.hits
```
%% Output
<ChunkedArray [176 125 318 ... 84 255 105] at 0x7f87906f9210>
<ChunkedArray [176 125 318 ... 84 255 105] at 0x7fb5da350350>
%% Cell type:markdown id: tags:
lazyarrays can be used with any Numpy universal function. For example:
%% Cell type:code id: tags:
``` python
import numpy as np
np.log(r.events.hits)
```
%% Output
<ChunkedArray [5.170483995038151 4.8283137373023015 5.762051382780177 ... 4.430816798843313 5.541263545158426 4.653960350157523] at 0x7f879052d4d0>
<ChunkedArray [5.170483995038151 4.8283137373023015 5.762051382780177 ... 4.430816798843313 5.541263545158426 4.653960350157523] at 0x7fb60fcdd2d0>
%% Cell type:markdown id: tags:
let's look at event 0:
%% Cell type:code id: tags:
``` python
r.events[0]
```
%% Output
offline event:
id : 1
det_id : 44
mc_id : 0
run_id : 5971
mc_run_id : 0
frame_index : 182
trigger_mask : 22
trigger_counter : 0
overlays : 60
hits : 176
trks : 56
w : []
w2list : []
w3list : []
mc_t : 0.0
mc_hits : 0
mc_trks : 0
comment : b''
index : 0
flags : 0
t_fSec : 1567036818
t_fNanoSec : 200000000
%% Cell type:code id: tags:
``` python
r.events[0].overlays
```
%% Output
60
%% Cell type:code id: tags:
``` python
r.events[0].hits
```
%% Output
176
%% Cell type:markdown id: tags:
# read hits data:
%% Cell type:code id: tags:
``` python
r.hits.dom_id
```
%% Output
<ChunkedArray [[806451572 806451572 806451572 ... 809544061 809544061 809544061] [806451572 806451572 806451572 ... 809524432 809526097 809544061] [806451572 806451572 806451572 ... 809544061 809544061 809544061] ... [806451572 806455814 806465101 ... 809526097 809544058 809544061] [806455814 806455814 806455814 ... 809544061 809544061 809544061] [806455814 806455814 806455814 ... 809544058 809544058 809544061]] at 0x7f87906f9450>
<ChunkedArray [[806451572 806451572 806451572 ... 809544061 809544061 809544061] [806451572 806451572 806451572 ... 809524432 809526097 809544061] [806451572 806451572 806451572 ... 809544061 809544061 809544061] ... [806451572 806455814 806465101 ... 809526097 809544058 809544061] [806455814 806455814 806455814 ... 809544061 809544061 809544061] [806455814 806455814 806455814 ... 809544058 809544058 809544061]] at 0x7fb5da350fd0>
%% Cell type:code id: tags:
``` python
r.hits.tot
```
%% Output
<ChunkedArray [[24 30 22 ... 38 26 23] [29 26 22 ... 26 28 24] [27 19 13 ... 27 24 16] ... [22 22 9 ... 27 32 27] [30 32 17 ... 30 24 29] [27 41 36 ... 29 24 28]] at 0x7f87906f9810>
<ChunkedArray [[24 30 22 ... 38 26 23] [29 26 22 ... 26 28 24] [27 19 13 ... 27 24 16] ... [22 22 9 ... 27 32 27] [30 32 17 ... 30 24 29] [27 41 36 ... 29 24 28]] at 0x7fb5da3a3310>
%% Cell type:code id: tags:
``` python
r[0].hits
```
%% Output
<OfflineHits: 176 parsed elements>
%% Cell type:code id: tags:
``` python
r[0].hits.dom_id
```
%% Output
array([806451572, 806451572, 806451572, 806451572, 806455814, 806455814,
806455814, 806483369, 806483369, 806483369, 806483369, 806483369,
806483369, 806483369, 806483369, 806483369, 806483369, 806487219,
806487226, 806487231, 806487231, 808432835, 808435278, 808435278,
808435278, 808435278, 808435278, 808447180, 808447180, 808447180,
808447180, 808447180, 808447180, 808447180, 808447180, 808447186,
808451904, 808451904, 808472265, 808472265, 808472265, 808472265,
808472265, 808472265, 808472265, 808472265, 808488895, 808488990,
808488990, 808488990, 808488990, 808488990, 808489014, 808489014,
808489117, 808489117, 808489117, 808489117, 808493910, 808946818,
808949744, 808951460, 808951460, 808951460, 808951460, 808951460,
808956908, 808956908, 808959411, 808959411, 808959411, 808961448,
808961448, 808961504, 808961504, 808961655, 808961655, 808961655,
808964815, 808964815, 808964852, 808964908, 808969857, 808969857,
808969857, 808969857, 808969857, 808972593, 808972698, 808972698,
808972698, 808974758, 808974758, 808974758, 808974758, 808974758,
808974758, 808974758, 808974758, 808974758, 808974758, 808974758,
808974773, 808974773, 808974773, 808974773, 808974773, 808974972,
808974972, 808976377, 808976377, 808976377, 808979567, 808979567,
808979567, 808979721, 808979721, 808979721, 808979721, 808979721,
808979721, 808979721, 808979729, 808979729, 808979729, 808981510,
808981510, 808981510, 808981510, 808981672, 808981672, 808981672,
808981672, 808981672, 808981672, 808981672, 808981672, 808981672,
808981672, 808981672, 808981672, 808981672, 808981672, 808981672,
808981672, 808981672, 808981812, 808981812, 808981812, 808981864,
808981864, 808982005, 808982005, 808982005, 808982018, 808982018,
808982018, 808982041, 808982041, 808982077, 808982077, 808982547,
808982547, 808982547, 808997793, 809006037, 809524432, 809526097,
809526097, 809544061, 809544061, 809544061, 809544061, 809544061,
809544061, 809544061], dtype=int32)
%% Cell type:code id: tags:
``` python
r[0].hits[0]
```
%% Output
offline hit:
id : 0
dom_id : 806451572
channel_id : 8
tdc : 0
tot : 24
trig : 1
pmt_id : 0
t : 70104010.0
a : 0.0
pos_x : 0.0
pos_y : 0.0
pos_z : 0.0
dir_x : 0.0
dir_y : 0.0
dir_z : 0.0
pure_t : 0.0
pure_a : 0.0
type : 0
origin : 0
pattern_flags : 0
%% Cell type:code id: tags:
``` python
r[0].hits[0].dom_id
```
%% Output
806451572
%% Cell type:markdown id: tags:
# read tracks data:
%% Cell type:code id: tags:
``` python
r.tracks
```
%% Output
<OfflineTracks: 10 parsed elements>
%% Cell type:code id: tags:
``` python
r.tracks.lik
```
%% Output
<ChunkedArray [[294.6407542676734 294.6407542676734 294.6407542676734 ... 67.81221253265059 67.7756405143316 67.77250505700384] [96.75133289411137 96.75133289411137 96.75133289411137 ... 39.21916536442286 39.184645826013806 38.870325146341884] [560.2775306614813 560.2775306614813 560.2775306614813 ... 118.88577278801066 118.72271313687405 117.80785995187605] ... [71.03251451148226 71.03251451148226 71.03251451148226 ... 16.714140573909347 16.444395245214945 16.34639241716669] [326.440133294878 326.440133294878 326.440133294878 ... 87.79818671079849 87.75488082571873 87.74839444768625] [159.77779654216795 159.77779654216795 159.77779654216795 ... 33.8669134999348 33.821631538334984 33.77240735670646]] at 0x7f8790683d50>
<ChunkedArray [[294.6407542676734 294.6407542676734 294.6407542676734 ... 67.81221253265059 67.7756405143316 67.77250505700384] [96.75133289411137 96.75133289411137 96.75133289411137 ... 39.21916536442286 39.184645826013806 38.870325146341884] [560.2775306614813 560.2775306614813 560.2775306614813 ... 118.88577278801066 118.72271313687405 117.80785995187605] ... [71.03251451148226 71.03251451148226 71.03251451148226 ... 16.714140573909347 16.444395245214945 16.34639241716669] [326.440133294878 326.440133294878 326.440133294878 ... 87.79818671079849 87.75488082571873 87.74839444768625] [159.77779654216795 159.77779654216795 159.77779654216795 ... 33.8669134999348 33.821631538334984 33.77240735670646]] at 0x7fb5da302b50>
%% Cell type:code id: tags:
``` python
r[0].tracks
```
%% Output
<OfflineTracks: 56 parsed elements>
%% Cell type:code id: tags:
``` python
r[0].tracks.lik
```
%% Output
array([294.64075427, 294.64075427, 294.64075427, 291.64653113,
291.27392663, 290.69031512, 289.19290546, 289.08449217,
289.03373947, 288.19030836, 282.92343367, 282.71527118,
282.10762402, 280.20553861, 275.93183966, 273.01809111,
257.46433694, 220.94357656, 194.99426403, 190.47809685,
79.95235686, 78.94389763, 78.90791169, 77.96122466,
77.9579604 , 76.90769883, 75.97546175, 74.91530508,
74.9059469 , 72.94007716, 72.90467038, 72.8629316 ,
72.81280833, 72.80229533, 72.78899435, 71.82404165,
71.80085542, 71.71028058, 70.91130096, 70.89150223,
70.85845637, 70.79081796, 70.76929743, 69.80667603,
69.64058976, 68.93085058, 68.84304037, 68.83154232,
68.79944298, 68.79019375, 68.78581291, 68.72340328,
67.86628937, 67.81221253, 67.77564051, 67.77250506])
%% Cell type:code id: tags:
``` python
r[0].tracks[0]
```
%% Output
offline track:
fUniqueID : 0
fBits : 33554432
id : 1
pos_x : 445.835395997812
pos_y : 615.1089636184813
pos_z : 125.1448339836911
dir_x : 0.0368711082700674
dir_y : -0.48653048395923415
dir_z : -0.872885221293917
t : 70311446.46401498
E : 99.10458562488608
len : 0.0
lik : 294.6407542676734
type : 0
rec_type : 4000
rec_stages : [1, 3, 5, 4]
status : 0
mother_id : -1
hit_ids : []
error_matrix : []
comment : 0
JGANDALF_BETA0_RAD : 0.004957442219414389
JGANDALF_BETA1_RAD : 0.003417848024252858
JGANDALF_CHI2 : -294.6407542676734
JGANDALF_NUMBER_OF_HITS : 142.0
JENERGY_ENERGY : 99.10458562488608
JENERGY_CHI2 : 1.7976931348623157e+308
JGANDALF_LAMBDA : 4.2409761837248484e-12
JGANDALF_NUMBER_OF_ITERATIONS : 10.0
JSTART_NPE_MIP : 24.88469697331908
JSTART_NPE_MIP_TOTAL : 55.88169412579765
JSTART_LENGTH_METRES : 98.89582506402911
JVETO_NPE : 0.0
JVETO_NUMBER_OF_HITS : 0.0
JENERGY_MUON_RANGE_METRES : 344.9767431592819
JENERGY_NOISE_LIKELIHOOD : -333.87773581129136
JENERGY_NDF : 1471.0
JENERGY_NUMBER_OF_HITS : 101.0
%% Cell type:code id: tags:
``` python
r[0].tracks[0].lik
```
%% Output
294.6407542676734
%% Cell type:code id: tags:
``` python
r.tracks.reco.dtype.names
```
%% Output
('JGANDALF_BETA0_RAD',
'JGANDALF_BETA1_RAD',
'JGANDALF_CHI2',
'JGANDALF_NUMBER_OF_HITS',
'JENERGY_ENERGY',
'JENERGY_CHI2',
'JGANDALF_LAMBDA',
'JGANDALF_NUMBER_OF_ITERATIONS',
'JSTART_NPE_MIP',
'JSTART_NPE_MIP_TOTAL',
'JSTART_LENGTH_METRES',
'JVETO_NPE',
'JVETO_NUMBER_OF_HITS',
'JENERGY_MUON_RANGE_METRES',
'JENERGY_NOISE_LIKELIHOOD',
'JENERGY_NDF',
'JENERGY_NUMBER_OF_HITS')
%% Cell type:code id: tags:
``` python
r.tracks.reco["JENERGY_ENERGY"]
```
%% Output
array([99.10458562, 99.10458562, 99.10458562, 37.85515249, 99.10458562,
7.16916787, 99.10458562, 99.10458562, 49.13672986, 20.35137468])
%% Cell type:markdown id: tags:
# read mc hits:
%% Cell type:code id: tags:
``` python
r.mc_hits
```
%% Output
<OfflineHits: 10 parsed elements>
%% Cell type:markdown id: tags:
# read mc tracks:
%% Cell type:code id: tags:
``` python
r.mc_tracks
```
%% Output
<OfflineTracks: 10 parsed elements>
%% Cell type:code id: tags:
``` python
```
%% Cell type:code id: tags:
``` python
```
......
tests/test_offline.py
+ 52
− 0
View file @ 7e8d8da9
import unittest
import numpy as np
from pathlib import Path
from km3io.offline import Reader, OfflineEvents, OfflineHits, OfflineTracks
......@@ -115,6 +116,7 @@ class TestReader(unittest.TestCase):
class TestOfflineReader(unittest.TestCase):
def setUp(self):
self.r = OfflineReader(OFFLINE_FILE)
self.nu = OfflineReader(OFFLINE_NUMUCC)
self.Nevents = 10
self.selected_data = OfflineReader(OFFLINE_FILE,
data=self.r._data[0])._data
......@@ -132,6 +134,56 @@ class TestOfflineReader(unittest.TestCase):
# check that there are 10 events
self.assertEqual(Nevents, self.Nevents)
def test_find_empty(self):
fitinf = self.nu.tracks.fitinf
rec_stages = self.nu.tracks.rec_stages
empty_fitinf = np.array([match for match in
self.nu._find_empty(fitinf)])
empty_stages = np.array([match for match in
self.nu._find_empty(rec_stages)])
self.assertListEqual(empty_fitinf[:5, 1].tolist(), [23, 14,
14, 4, None])
self.assertListEqual(empty_stages[:5, 1].tolist(), [False, False,
False, False,
None])
def test_find_rec_stages(self):
stages = np.array([match for match in
self.nu._find_rec_stages([1, 2, 3, 4, 5])])
self.assertListEqual(stages[:5, 1].tolist(), [0, 0, 0, 0, None])
def test_get_reco_fit(self):
JGANDALF_BETA0_RAD = [0.0020367251782607574,
0.003306725805622178,
0.0057877124222254885,
0.015581698352185896]
reco_fit = self.nu.get_reco_fit([1, 2, 3, 4, 5])['JGANDALF_BETA0_RAD']
self.assertListEqual(JGANDALF_BETA0_RAD, reco_fit[:4].tolist())
with self.assertRaises(ValueError):
self.nu.get_reco_fit([1000, 4512, 5625])
def test_get_max_reco_stages(self):
rec_stages = self.nu.tracks.rec_stages
max_reco = self.nu._get_max_reco_stages(rec_stages)
self.assertEqual(len(max_reco.tolist()), 9)
self.assertListEqual(max_reco[0].tolist(), [[1, 2, 3, 4, 5],
5, 0])
def test_best_reco(self):
JGANDALF_BETA1_RAD = [0.0014177681261476852,
0.002094094517471032,
0.003923368624980349,
0.009491461076780453]
best = self.nu.best_reco
self.assertEqual(best.size, 9)
self.assertEqual(best['JGANDALF_BETA1_RAD'][:4].tolist(), JGANDALF_BETA1_RAD)
def test_reading_header(self):
# head is the supported format
head = OfflineReader(OFFLINE_NUMUCC).header
......
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