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bergamaschi committed 2023-10-20 17:18:42 +02:00
2 parents 9b82363cef e1f22fd014
commit 8473f4d86a
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README.md
+31 -6
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@@ -1,18 +1,28 @@
# python_cpp_example
# creader
Minimal example building a C++ python extension.
Small python package to read cluster and raw files
Useful links:
* [Using NumPy C-API](https://numpy.org/doc/stable/user/c-info.html)
## Getting started
Run
```bash
export PYTHONPATH=$PWD:$PYTHONPATH
make
```
And then have a look at the examples:
- [Reading cluster files](examples/ClusterFile.ipynb)
- [Reading Moench03/04 raw files](examples/RawFile.ipynb)
### Build instructions
## Build instructions
**Simplified build using make**
```bash
$ make #build c extension inplace
```
Check what is available
```
$ make help
clean Remove the build folder and the shared library
@@ -53,4 +63,19 @@ typedef struct {
int16_t y;
int32_t data[9];
} Cluster ;
```
```
## Running tests
```bash
#Tell the program where the test data is located.
# Can change depending on how you mounted sls_det_storage
$ export CREADER_TEST_DATA=/mnt/sls_det_storage/moench_data/cluster_reader_test/
$ make test
```
## Useful links:
* [Using NumPy C-API](https://numpy.org/doc/stable/user/c-info.html)
* [Extending Python with C](https://docs.python.org/3/extending/extending.html)
creader/ClusterFile.py
+19 -2
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@@ -2,5 +2,22 @@
from . import ClusterFileReader
class ClusterFile(ClusterFileReader):
def __init__(self, fname):
super().__init__(fname)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def __iter__(self):
return self
def __next__(self):
clusters = self.read()
if clusters.size == 0:
raise StopIteration
else:
return clusters
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
pass
creader/RawFile.py
+136 -12
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@@ -1,29 +1,45 @@
import json
from pathlib import Path
import numpy as np
import re
from . import RawFileReader
from .enums import DetectorType
class RawFile:
"""
Generic Raw File reader. Picks up settings from .json master file
Currently supports: Moench03 =)
"""
def __init__(self, fname):
def __init__(self, fname, header = False):
self.findex = 0
self.fname = fname
self.json = False #Master file data comes from json
fname = Path(fname)
if fname.suffix != '.json':
raise ValueError("Need a master file in json format")
with open(fname) as f:
self.master = json.load(f)
if fname.suffix == '.json':
with open(fname) as f:
self.master = json.load(f)
self.json = True
elif fname.suffix == '.raw':
with open(fname) as f:
self._load_raw_master(f)
else:
raise ValueError(f'{fname.suffix} not supported')
#Figure out which file to open
if self.master['Detector Type'] == 'Moench' and self.master['Analog Samples'] == 5000:
#TODO! pass settings to reader
self._parse_fname()
self.reader = RawFileReader(self.data_fname(0,0))
self.reader = RawFileReader(self.data_fname(0,0), DetectorType.MOENCH_03, header = header)
elif self.master['Detector Type'] == 'ChipTestBoard' and self.master['Analog Samples'] == 5000:
self._parse_fname()
#Do we read analog or analog+digital
if self.master['Digital Flag'] == 1:
dt = DetectorType.MOENCH_04_AD
else:
dt = DetectorType.MOENCH_04_AD
self.reader = RawFileReader(self.data_fname(0,0), dt, header = header)
else:
raise ValueError('unsupported file')
@@ -35,20 +51,128 @@ class RawFile:
except:
raise ValueError(f"Could not parse master file name: {self.fname}")
def _load_raw_master(self, f):
self.master = {}
lines = f.readlines()
it = iter(lines)
for line in it:
if line.startswith("#Frame"):
break
if line == "\n":
continue
if line.startswith("Scan Parameters"):
while not line.endswith("]\n"):
line += next(it)
field, value = line.split(":", 1)
self.master[field.strip(" ")] = value.strip(" \n")
frame_header = {}
for line in it:
field, value = line.split(":", 1)
frame_header[field.strip()] = value.strip(" \n")
self.master["Frame Header"] = frame_header
self.master["Version"] = float(self.master["Version"])
self._parse_values()
def _parse_values(self):
int_fields = set(
(
"Analog Samples",
"Analog Flag",
"Digital Flag",
"Digital Samples",
"Max Frames Per File",
"Image Size",
"Frame Padding",
"Total Frames",
"Dynamic Range",
"Ten Giga",
"Quad",
"Number of Lines read out",
"Number of UDP Interfaces"
)
)
time_fields = set((
"Exptime",
"Exptime1",
"Exptime2",
"Exptime3",
"GateDelay1",
"GateDelay2",
"GateDelay3",
"SubExptime",#Eiger
"SubPeriod", #Eiger
"Period"
))
#some fields might not exist for all detectors
#hence using intersection
for field in time_fields.intersection(self.master.keys()):
self.master[field] = self.to_nanoseconds(self.master[field])
#Parse bothx .json and .raw master files
if self.json:
self.master['Image Size'] = self.master["Image Size in bytes"]
self.master['Pixels'] = (self.master['Pixels']['x'], self.master['Pixels']['y'])
self.master['nmod'] = int(self.master['Geometry']['x']*self.master['Geometry']['y'] )#ports not modules
if self.master['Detector Type'] == 'Eiger':
self.master['nmod'] = self.master['nmod'] // 2
else:
for field in int_fields.intersection(self.master.keys()):
self.master[field] = int(self.master[field].split()[0])
self.master["Pixels"] = tuple(
int(i) for i in self.master["Pixels"].strip("[]").split(",")
)
if "Rate Corrections" in self.master:
self.master["Rate Corrections"] = (
self.master["Rate Corrections"].strip("[]").split(",")
)
n = len(self.master["Rate Corrections"])
assert (
self.master["nmod"] == n
), f'nmod from Rate Corrections {n} differs from nmod {self.master["nmod"]}'
#Parse threshold for Mythen3 (if needed)
if "Threshold Energies" in self.master.keys():
th = self.master["Threshold Energies"]
if isinstance(th, str):
th = [int(i) for i in th.strip('[]').split(',')]
self.master["Threshold Energies"] = th
@staticmethod
def to_nanoseconds(t):
nanoseconds = {"s": 1000 * 1000 * 1000, "ms": 1000 * 1000, "us": 1000, "ns": 1}
try:
value = re.match(r"(\d+(?:\.\d+)?)", t).group()
unit = t[len(value) :]
value = int(float(value) * nanoseconds[unit])
value = np.timedelta64(value, 'ns')
except:
raise ValueError(f"Could not convert: {t} to nanoseconds")
return value
def data_fname(self, i, findex=0):
return Path(f"{self.base}_d{i}_f{findex}_{self.run_id}.raw")
def read(self):
return self.reader.read()
def read(self, *args):
return self.reader.read(*args)
# Support iteration
def __iter__(self):
return self
def __next__(self):
frame = self.reader.read()
if frame.shape[0] == 0:
res = self.reader.read()
if res.shape[0] == 0:
raise StopIteration
# Support with statement
creader/__init__.py
+3 -1
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@@ -2,4 +2,6 @@
from _creader import *
from .file_utils import open_file
from .ClusterFile import ClusterFile
from .ClusterFile import ClusterFile
from .enums import DetectorType
from .RawFile import RawFile
creader/enums.py
+8
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@@ -0,0 +1,8 @@
from enum import IntEnum
class DetectorType(IntEnum):
#TODO! Move defenition to C to avoid mismatch
GENERIC = 0
MOENCH_03 = 3
MOENCH_04_A = 4
MOENCH_04_AD = 5
examples/ClusterFile.ipynb
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@@ -0,0 +1,122 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "8e4c3dd9",
"metadata": {},
"source": [
"# Using ClusterFile"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "38941595",
"metadata": {},
"outputs": [],
"source": [
"from pathlib import Path\n",
"import numpy as np \n",
"import matplotlib.pyplot as plt\n",
"import boost_histogram as bh\n",
"import time\n",
"\n",
"#Python cluster reader\n",
"from creader import ClusterFile\n",
"path = Path('/mnt/sls_det_storage/moench_data/cluster_reader_test/')"
]
},
{
"cell_type": "markdown",
"id": "bc0ae735",
"metadata": {},
"source": [
"## Read clusters from file and plot a histogram\n",
"\n",
"- Passing chunk, to the cluster reader to set the default amount of clusters to be read\n",
"- Using the context manager to manage the file closing\n",
"- Looping over chunks in the file (reads chunksize until a zero read)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e69f82da",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7f4b6c136d10>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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truncated
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fname = 'beam_En700eV_-40deg_300V_10us_d0_f0_100.clust'\n",
"\n",
"# Create the histogram\n",
"n_bins = 100\n",
"xmin = -100\n",
"xmax = 1e4\n",
"hist1 = bh.Histogram(bh.axis.Regular(n_bins, xmin, xmax))\n",
"\n",
"# Read clusters and fill the histogram with pixel values\n",
"with ClusterFile(path/fname, chunk = 10000) as f:\n",
" for clusters in f:\n",
" hist1.fill(clusters['data'].flat)\n",
"\n",
"\n",
"#Plotting\n",
"fig, ax = plt.subplots()\n",
"ax.bar(hist1.axes[0].centers, hist1.values(), width=hist1.axes[0].widths, label = 'data')\n",
"ax.set_yscale('log')\n",
"ax.set_xlabel('ADU [1]')\n",
"ax.set_ylabel('Intensity [a.u.]')\n",
"ax.legend()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f7fd30b5",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
examples/RawFile.ipynb
+205
View File
@@ -0,0 +1,205 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "7c600ac0",
"metadata": {},
"source": [
"# Using RawFile"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "38912c9c",
"metadata": {},
"outputs": [],
"source": [
"from pathlib import Path\n",
"import numpy as np \n",
"import matplotlib.pyplot as plt\n",
"import boost_histogram as bh\n",
"import time\n",
"\n",
"#Python cluster reader\n",
"from creader import RawFile\n",
"path = Path('/mnt/sls_det_storage/moench_data/cluster_reader_test/')"
]
},
{
"cell_type": "markdown",
"id": "f06378a1",
"metadata": {},
"source": [
"## Moench03 data\n",
"\n",
"Fetches info from the master file\n",
"\n",
"### Frames only"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "218e2897",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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 truncated
"text/plain": [
"<Figure size 1000x500 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"with RawFile(path/'Moench03_master_0.json') as f:\n",
" frames = f.read(100)\n",
" \n",
"fig, ax = plt.subplots(1,3, figsize = (10,5))\n",
"im0 = ax[0].imshow(frames[0])\n",
"im1 = ax[1].imshow(frames[9])\n",
"im1 = ax[2].imshow(frames[0]-frames[9])\n",
"fig.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "3f92db90",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fc4ccea5d50>]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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 truncated
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots()\n",
"ax.plot(frames.sum(axis =1).sum(axis = 1))"
]
},
{
"cell_type": "markdown",
"id": "d25663bd",
"metadata": {},
"source": [
"### Frames and header"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "befafb31",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Frame Number, Timestamp \n",
"201 1107824650\n",
"202 1108024651\n",
"203 1108224652\n",
"204 1108424653\n",
"205 1108624654\n",
"206 1108824655\n",
"207 1109024656\n",
"208 1109224657\n",
"209 1109424658\n",
"210 1109624659\n"
]
}
],
"source": [
"with RawFile(path/'Moench03_master_0.json', header = True) as f:\n",
" frames, header = f.read(10)\n",
" \n",
" \n",
"print(f'{\"Frame Number,\":<15} {\"Timestamp\":<10}')\n",
"for h in header:\n",
" print(f\"{h['Frame Number']:<15} {h['Timestamp']:<10}\")"
]
},
{
"cell_type": "markdown",
"id": "991c2729",
"metadata": {},
"source": [
"## Moench 04 data"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "15fcde89",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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 truncated
"text/plain": [
"<Figure size 1000x1000 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#if the file has analog and digital data it's automatically read\n",
"\n",
"with RawFile(path/'Moench04_digital_master_0.raw', header = True) as f:\n",
" analog, digital, header = f.read(20)\n",
" \n",
"fig, ax = plt.subplots(2,2, figsize = (10,10))\n",
"im0 = ax[0][0].imshow(analog[0], origin = 'lower')\n",
"im1 = ax[0][1].imshow(digital[0], origin = 'lower')\n",
"im2 = ax[1][0].imshow(analog[9], origin = 'lower')\n",
"im3 = ax[1][1].imshow(digital[9], origin = 'lower')\n",
"\n",
"fig.tight_layout()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
pyproject.toml
+3
View File
@@ -0,0 +1,3 @@
[build-system]
requires = ["setuptools", "oldest-supported-numpy/python"]
build-backend = "setuptools.build_meta"
setup.py
+7 -1
View File
@@ -21,7 +21,13 @@ c_ext = setuptools.Extension("_creader",
c_ext.language = 'c'
setuptools.setup(
name= 'creader',
version = '2023.6.1',
version = '2023.10.17',
description = 'Reading cluster files',
packages=setuptools.find_packages(exclude=[
'tests',
]),
ext_modules=[c_ext],
install_requires=[
'numpy',
]
)
src/ClusterReader.c
+84 -56
View File
@@ -5,9 +5,9 @@
//clang-format off
typedef struct {
PyObject_HEAD
FILE *fp;
PyObject_HEAD FILE *fp;
int n_left;
Py_ssize_t chunk;
} ClusterFileReader;
//clang-format on
@@ -15,21 +15,40 @@ typedef struct {
// raises python exception if something goes wrong
// returned object should mean file is open and ready to read
static int ClusterFileReader_init(ClusterFileReader *self, PyObject *args,
PyObject *Py_UNUSED(kwds)) {
PyObject *kwds) {
// Parse file name, accepts string or pathlike objects
const char *fname = NULL;
PyObject* buf;
self->n_left = 0;
self->chunk = 0;
PyObject *fname_obj = NULL;
PyObject *fname_bytes = NULL;
Py_ssize_t len;
if (!PyArg_ParseTuple(args, "O&", PyUnicode_FSConverter, &buf))
static char *kwlist[] = {"fname", "chunk", NULL};
// if (!PyArg_ParseTuple(args, "O&", PyUnicode_FSConverter, &buf))
// return -1;
// PyBytes_AsStringAndSize(buf, &fname, &len);
if (!PyArg_ParseTupleAndKeywords(args, kwds, "O|n", kwlist, &fname_obj,
&self->chunk)) {
return -1;
PyBytes_AsStringAndSize(buf, &fname, &len);
}
if (fname_obj != Py_None)
if (!PyUnicode_FSConverter(fname_obj, &fname_bytes))
return -1;
PyBytes_AsStringAndSize(fname_bytes, &fname, &len);
#ifdef CR_VERBOSE
printf("Opening: %s\n chunk: %lu\n", fname, self->chunk);
#endif
self->fp = fopen(fname, "rb");
self->n_left = 0;
//Keep the return code to not return before releasing buffer
// Keep the return code to not return before releasing buffer
int rc = 0;
// Raise python exception using information from errno
@@ -37,8 +56,8 @@ static int ClusterFileReader_init(ClusterFileReader *self, PyObject *args,
PyErr_SetFromErrnoWithFilename(PyExc_OSError, fname);
rc = -1;
}
//Release buffer
Py_DECREF(buf);
// Release buffer
Py_DECREF(fname_bytes);
// Success or fail
return rc;
@@ -57,68 +76,76 @@ static void ClusterFileReader_dealloc(ClusterFileReader *self) {
}
// read method
static PyObject *ClusterFileReader_read(ClusterFileReader *self, PyObject *args) {
static PyObject *ClusterFileReader_read(ClusterFileReader *self,
PyObject *args) {
const int ndim = 1;
Py_ssize_t size = 0;
PyObject *noise_obj;
if (!PyArg_ParseTuple(args, "nO", &size,&noise_obj)) {
PyErr_SetString(
PyExc_TypeError,
"Could not parse args.");
return NULL;
}
PyObject *noise_obj = NULL;
PyObject *noise_array = NULL;
if (!PyArg_ParseTuple(args, "|nO", &size, &noise_obj)) {
PyErr_SetString(PyExc_TypeError, "Could not parse args.");
return NULL;
}
// Fall back on object default/config
if (size == 0)
size = self->chunk;
npy_intp dims[] = {size};
// Create two numpy arrays from the passed objects, if possible numpy will
// If possible numpy will
>>>>>>> e1f22fd014eb86d40e0ecb78bd65cb7a9bef71bc
// use the underlying buffer, otherwise it will create a copy, for example
// if data type is different or we pass in a list. The
// NPY_ARRAY_C_CONTIGUOUS flag ensures that we have contiguous memory.
PyObject *noise_array = PyArray_FROM_OTF(noise_obj, NPY_DOUBLE, NPY_ARRAY_C_CONTIGUOUS);
int nx=0,ny=0;
double *noise_map=NULL;
#ifdef CR_VERBOSE
printf("Getting ready to read: %lu clusters. Noise map: %p\n", size,
noise_obj);
#endif
// If the user passed a noise map we fetch a pointer to that array as well
int nx = 0, ny = 0;
double *noise_map = NULL;
if (noise_obj) {
noise_array =
PyArray_FROM_OTF(noise_obj, NPY_DOUBLE, NPY_ARRAY_C_CONTIGUOUS);
int ndim_noise = PyArray_NDIM((PyArrayObject *)(noise_array));
npy_intp *noise_shape = PyArray_SHAPE((PyArrayObject *)(noise_array));
// For the C++ function call we need pointers (or another C++ type/data
// structure)
noise_map = (double *)(PyArray_DATA((PyArrayObject *)(noise_array)));
// If parsing of a or b fails we throw an exception in Python
if (noise_array ) {
int ndim_noise = PyArray_NDIM((PyArrayObject *)(noise_array));
npy_intp *noise_shape = PyArray_SHAPE((PyArrayObject *)(noise_array));
// For the C++ function call we need pointers (or another C++ type/data
// structure)
noise_map = (double *)(PyArray_DATA((PyArrayObject *)(noise_array)));
/* for (int i=0; i< ndim_noise; i++) { */
/* printf("Dimension %d size %d pointer \n",i,noise_shape[i],
* noise_map); */
/* } */
if (ndim_noise == 2) {
/* for (int i=0; i< ndim_noise; i++) { */
/* printf("Dimension %d size %d pointer \n",i,noise_shape[i], noise_map); */
/* } */
nx = noise_shape[0];
ny = noise_shape[1];
if (ndim_noise==2) {
nx=noise_shape[0];
ny=noise_shape[1];
//printf("Noise map found size %d %d %d\n",nx,ny,noise_map);
// printf("Noise map found size %d %d %d\n",nx,ny,noise_map);
} else {
nx=0;
if (ndim_noise==1)
nx=noise_shape[0];
ny=0;
noise_map = NULL;
//printf("NO Noise map found %d %d %d %d\n",ndim_noise,nx,ny,noise_map);
} else {
nx = 0;
if (ndim_noise == 1)
nx = noise_shape[0];
ny = 0;
noise_map = NULL;
// printf("NO Noise map found %d %d %d
//%d\n",ndim_noise,nx,ny,noise_map);
}
}
}
// Create an uninitialized numpy array
PyObject *clusters = PyArray_SimpleNewFromDescr(ndim, dims, cluster_dt());
@@ -132,7 +159,8 @@ static PyObject *ClusterFileReader_read(ClusterFileReader *self, PyObject *args)
// Here goes the looping, removing frame numbers etc.
int n_read = 0;
if (noise_map)
read_clusters_with_cut(self->fp, size, buf, &self->n_left,noise_map, nx, ny);
n_read = read_clusters_with_cut(self->fp, size, buf, &self->n_left,
noise_map, nx, ny);
else
n_read = read_clusters(self->fp, size, buf, &self->n_left);
src/RawFileReader.c
+69 -25
View File
@@ -1,7 +1,7 @@
#include "RawFileReader.h"
#include "arr_desc.h"
#include "data_types.h"
#include "raw_reader.h"
#include "arr_desc.h"
#include <stdbool.h>
@@ -11,6 +11,7 @@ typedef struct {
// additional fields for size and decoder?
int dtype;
bool read_header;
int detector;
} RawFileReader;
//clang-format on
@@ -28,11 +29,12 @@ static int RawFileReader_init(RawFileReader *self, PyObject *args,
// Should we read the header
self->read_header = false;
self->detector = DT_GENERIC;
static char *kwlist[] = {"fname", "header", NULL};
static char *kwlist[] = {"fname", "detector_type", "header", NULL};
if (!PyArg_ParseTupleAndKeywords(args, kwds, "O|p", kwlist, &fname_obj,
&self->read_header)) {
if (!PyArg_ParseTupleAndKeywords(args, kwds, "Oi|p", kwlist, &fname_obj,
&self->detector, &self->read_header)) {
return -1;
}
@@ -41,11 +43,13 @@ static int RawFileReader_init(RawFileReader *self, PyObject *args,
return -1;
PyBytes_AsStringAndSize(fname_bytes, &fname, &len);
printf("%s\n read_header: %d\n", fname, self->read_header);
#ifdef CR_VERBOSE
printf("fname: %s\n read_header: %d detector type: %d\n", fname,
self->read_header, self->detector);
#endif
self->fp = fopen(fname, "rb");
// Keep the return code to not return before releasing buffer
int rc = 0;
@@ -76,27 +80,46 @@ static PyObject *RawFileReader_read(RawFileReader *self, PyObject *args) {
self->dtype = NPY_UINT16;
const int ndim = 3;
Py_ssize_t n_frames = 1; // default number of frames to read
// Py_ssize_t moench_version = 3;
// Py_ssize_t analog_digital = 0;
if (!PyArg_ParseTuple(args, "|n", &n_frames))
return NULL;
npy_intp dims[3] = {n_frames, 400, 400};
PyObject *frames = PyArray_SimpleNew(ndim, dims, self->dtype);
char *out_buf = PyArray_DATA((PyArrayObject *)frames);
PyObject *digital_frames = NULL;
char *digital_out = NULL;
PyArray_FILLWBYTE((PyArrayObject *)frames, 0);
//Optional return the header
// Optional return the header
PyObject *header = NULL;
char* header_out = NULL;
if(self->read_header){
char *header_out = NULL;
if (self->read_header) {
header = PyArray_SimpleNewFromDescr(1, dims, frame_header_dt());
header_out = PyArray_DATA((PyArrayObject *)header);
}
const size_t frame_size = 400 * 400 * 2;
char *out_buf = PyArray_DATA((PyArrayObject *)frames);
int64_t n_read =
read_raw(self->fp, n_frames, frame_size, out_buf, header_out);
// Both analog and digital data
if (self->detector == DT_MOENCH_04_AD) {
digital_frames = PyArray_SimpleNew(ndim, dims, NPY_UINT8);
PyArray_FILLWBYTE((PyArrayObject *)digital_frames, 0);
digital_out = PyArray_DATA((PyArrayObject *)digital_frames);
}
int64_t n_read = 0;
switch (self->detector) {
case DT_MOENCH_03:
n_read = read_raw_m03(self->fp, n_frames, out_buf, header_out);
break;
case DT_MOENCH_04_A:
case DT_MOENCH_04_AD:
n_read =
read_raw_m04(self->fp, n_frames, out_buf, digital_out, header_out);
break;
default:
break;
}
if (n_read != n_frames) {
// resize the array to match the number of read photons
@@ -113,20 +136,41 @@ static PyObject *RawFileReader_read(RawFileReader *self, PyObject *args) {
// resize the array to match the number of clusters read
PyArray_Resize((PyArrayObject *)frames, &new_shape, 1, NPY_ANYORDER);
//if we also read header we need to reshape the header
if(self->read_header){
new_shape.len = 1;
PyArray_Resize((PyArrayObject *)header, &new_shape, 1, NPY_ANYORDER);
if(digital_frames){
PyArray_Resize((PyArrayObject *)digital_frames, &new_shape, 1, NPY_ANYORDER);
}
// if we also read header we need to reshape the header
if (self->read_header) {
new_shape.len = 1;
PyArray_Resize((PyArrayObject *)header, &new_shape, 1,
NPY_ANYORDER);
}
}
PyObject *ret = frames;
if(self->read_header){
ret = PyTuple_Pack(2, frames, header);
Py_DECREF(header);
// Build up a tuple with the return values
PyObject *ret = PyTuple_Pack(1, frames);
if (self->detector == DT_MOENCH_04_AD){
_PyTuple_Resize(&ret, 2);
PyTuple_SET_ITEM(ret, 1, digital_frames);
}
if (self->read_header){
Py_ssize_t old_size = PyTuple_GET_SIZE(ret);
_PyTuple_Resize(&ret, old_size+1);
PyTuple_SET_ITEM(ret, old_size, header);
}
// if we only have one item in the tuple lets return it instead of the tuple
if(PyTuple_GET_SIZE(ret) == 1){
Py_DECREF(ret);
return frames;
}else{
Py_DECREF(frames);
return ret;
}
return ret;
}
src/cluster_reader.c
+138 -19
View File
@@ -26,7 +26,6 @@ int read_clusters(FILE *fp, int64_t n_clusters, Cluster *buf, int *n_left) {
// keep on reading frames and photons until reaching n_clusters
while (fread(&iframe, sizeof(iframe), 1, fp)) {
if (fread(&nph, sizeof(nph), 1, fp)) {
if (nph > n_clusters - nph_read)
nn = n_clusters - nph_read;
@@ -39,18 +38,14 @@ int read_clusters(FILE *fp, int64_t n_clusters, Cluster *buf, int *n_left) {
}
if (nph_read >= n_clusters)
break;
}
}
assert(nph_read <= n_clusters); // sanity check in debug mode
return nph_read;
}
int read_clusters_with_cut(FILE *fp, int64_t n_clusters, Cluster *buf, int *n_left, double *noise_map, int nx, int ny) {
int read_clusters_with_cut(FILE *fp, int64_t n_clusters, Cluster *buf,
int *n_left, double *noise_map, int nx, int ny) {
int iframe = 0;
int nph = *n_left;
@@ -58,11 +53,12 @@ int read_clusters_with_cut(FILE *fp, int64_t n_clusters, Cluster *buf, int *n_le
int32_t tot2[4], t2max, tot1;
int32_t val, tot3;
Cluster *ptr=buf;
int good=1;
Cluster *ptr = buf;
int good = 1;
double noise;
// read photons left from previous frame
if (nph) {
<<<<<<< HEAD
for (int iph=0; iph<nph; iph++) {
// read photons 1 by 1
fread((void *)(ptr), sizeof(Cluster), 1, fp);
@@ -89,11 +85,41 @@ int read_clusters_with_cut(FILE *fp, int64_t n_clusters, Cluster *buf, int *n_le
if (nph_read >= n_clusters)
break;
}
=======
for (int iph = 0; iph < nph; iph++) {
// read photons 1 by 1
fread((void *)(ptr), sizeof(Cluster), 1, fp);
good = 1;
if (noise_map) {
if (ptr->x >= 0 && ptr->x < nx && ptr->y >= 0 && ptr->y < ny) {
tot1 = ptr->data[4];
analyze_cluster(*ptr, &t2max, &tot3, NULL, NULL, NULL, NULL,
NULL, NULL, NULL, NULL, NULL);
noise = noise_map[ptr->y * nx + ptr->x];
if (tot1 > noise && t2max > 2 * noise && tot3 > 3 * noise) {
;
} else
good = 0;
} else {
printf("Bad pixel number %d %d\n", ptr->x, ptr->y);
good = 0;
}
}
if (good) {
ptr++;
nph_read++;
(*n_left)--;
}
if (nph_read >= n_clusters)
break;
}
>>>>>>> e1f22fd014eb86d40e0ecb78bd65cb7a9bef71bc
}
if (nph_read < n_clusters) {
// keep on reading frames and photons until reaching n_clusters
while (fread(&iframe, sizeof(iframe), 1, fp)) {
<<<<<<< HEAD
//printf("%d\n",nph_read);
if (fread(&nph, sizeof(nph), 1, fp)) {
@@ -131,12 +157,55 @@ int read_clusters_with_cut(FILE *fp, int64_t n_clusters, Cluster *buf, int *n_le
if (nph_read >= n_clusters)
break;
}
=======
// printf("%d\n",nph_read);
if (fread(&nph, sizeof(nph), 1, fp)) {
// printf("** %d\n",nph);
*n_left = nph;
for (int iph = 0; iph < nph; iph++) {
// read photons 1 by 1
fread((void *)(ptr), sizeof(Cluster), 1, fp);
good = 1;
if (noise_map) {
if (ptr->x >= 0 && ptr->x < nx && ptr->y >= 0 &&
ptr->y < ny) {
tot1 = ptr->data[4];
analyze_cluster(*ptr, &t2max, &tot3, NULL, NULL,
NULL, NULL, NULL, NULL, NULL, NULL,
NULL);
noise = noise_map[ptr->y * nx + ptr->x];
if (tot1 > noise && t2max > 2 * noise &&
tot3 > 3 * noise) {
;
} else
good = 0;
} else {
printf("Bad pixel number %d %d\n", ptr->x, ptr->y);
good = 0;
}
}
if (good) {
ptr++;
nph_read++;
(*n_left)--;
}
if (nph_read >= n_clusters)
break;
}
}
if (nph_read >= n_clusters)
break;
}
>>>>>>> e1f22fd014eb86d40e0ecb78bd65cb7a9bef71bc
}
//printf("%d\n",nph_read);
// printf("%d\n",nph_read);
assert(nph_read <= n_clusters); // sanity check in debug mode
return nph_read;
}
<<<<<<< HEAD
@@ -144,6 +213,9 @@ int read_clusters_with_cut(FILE *fp, int64_t n_clusters, Cluster *buf, int *n_le
int analyze_clusters(int64_t n_clusters, int32_t *cin, ClusterAnalysis *co, int csize) {
=======
int analyze_clusters(int64_t n_clusters, Cluster *cin, ClusterAnalysis *cout) {
>>>>>>> e1f22fd014eb86d40e0ecb78bd65cb7a9bef71bc
int32_t tot2[4], t2max;
char quad;
@@ -153,6 +225,7 @@ int analyze_clusters(int64_t n_clusters, int32_t *cin, ClusterAnalysis *co, int
//printf("csize is %d\n",csize);
int ret;
for (int ic = 0; ic < n_clusters; ic++) {
<<<<<<< HEAD
switch (csize) {
case 2:
@@ -175,10 +248,22 @@ int analyze_clusters(int64_t n_clusters, int32_t *cin, ClusterAnalysis *co, int
/* if (tot<=0) */
/* printf("%d %d %d %d %d %d\n",ic,(cin+ic)->x, (cin+ic)->y, */
/* (cout+ic)->c, (cout+ic)->tot2, (cout+ic)->tot3); */
=======
analyze_cluster(*(cin + ic), &t2max, &tot3, &quad, NULL, NULL, NULL,
NULL, NULL, NULL, NULL, NULL);
(cout + ic)->c = quad;
(cout + ic)->tot2 = t2max;
(cout + ic)->tot3 = tot3;
// printf("%d %d %d %d %d %d\n",ic,(cin+ic)->x, (cin+ic)->y,
// (cout+ic)->c, (cout+ic)->tot2, (cout+ic)->tot3);
>>>>>>> e1f22fd014eb86d40e0ecb78bd65cb7a9bef71bc
}
return nc;
}
<<<<<<< HEAD
int analyze_cluster(Cluster cl, int32_t *t2, int32_t *t3, char *quad, double *eta2x, double *eta2y, double *eta3x, double *eta3y) {
return analyze_data(cl.data, t2, t3, quad, eta2x, eta2y, eta3x, eta3y);
@@ -189,16 +274,25 @@ int analyze_data(int32_t *data, int32_t *t2, int32_t *t3, char *quad, double *et
int ok=1;
=======
int analyze_cluster(Cluster cin, int32_t *t2, int32_t *t3, char *quad,
double *eta2x, double *eta2y, double *eta3x, double *eta3y,
double *eta2Lx, double *eta2Ly, double *eta3Xx,
double *eta3Xy) {
int ok = 1;
>>>>>>> e1f22fd014eb86d40e0ecb78bd65cb7a9bef71bc
int32_t tot2[4], t2max;
char c;
int32_t val, tot3;
tot3 = 0;
for (int i = 0; i < 4; i++)
tot2[i] = 0;
tot2[i] = 0;
// t2max=0;
for (int ix = 0; ix < 3; ix++) {
<<<<<<< HEAD
for (int iy = 0; iy < 3; iy++) {
val = data[iy * 3 + ix];
// printf ("%d ",data[iy * 3 + ix]);
@@ -213,12 +307,25 @@ int analyze_data(int32_t *data, int32_t *t2, int32_t *t3, char *quad, double *et
tot2[cTopRight] += val;
}
// printf ("\n");
=======
for (int iy = 0; iy < 3; iy++) {
val = cin.data[iy * 3 + ix];
tot3 += val;
if (ix <= 1 && iy <= 1)
tot2[0] += val;
if (ix >= 1 && iy <= 1)
tot2[1] += val;
if (ix <= 1 && iy >= 1)
tot2[2] += val;
if (ix >= 1 && iy >= 1)
tot2[3] += val;
}
>>>>>>> e1f22fd014eb86d40e0ecb78bd65cb7a9bef71bc
}
//printf ("\n");
if (t2 || quad) {
<<<<<<< HEAD
t2max = -1000;
c = 0;
for (int i = 0; i < 4; i++) {
@@ -227,12 +334,23 @@ int analyze_data(int32_t *data, int32_t *t2, int32_t *t3, char *quad, double *et
c = i;
}
}
=======
t2max = tot2[0];
c = cBottomLeft;
for (int i = 1; i < 4; i++) {
if (tot2[i] > t2max) {
t2max = tot2[i];
c = i;
}
}
>>>>>>> e1f22fd014eb86d40e0ecb78bd65cb7a9bef71bc
}
if (quad)
*quad = c;
*quad = c;
if (t2)
*t2 = t2max;
*t2 = t2max;
if (t3)
<<<<<<< HEAD
*t3 = tot3;
if (eta2x || eta2y) {
@@ -290,8 +408,9 @@ int analyze_data(int32_t *data, int32_t *t2, int32_t *t3, char *quad, double *et
/* } */
=======
*t3 = tot3;
>>>>>>> e1f22fd014eb86d40e0ecb78bd65cb7a9bef71bc
return ok;
}
}
src/creader_module.c
+5 -4
View File
@@ -7,8 +7,8 @@
#include "RawFileReader.h"
#include "arr_desc.h"
#include "data_types.h"
#include "cluster_reader.h"
#include "data_types.h"
/* static PyObject *clusterize(PyObject *Py_UNUSED(self), PyObject *args) {
@@ -172,13 +172,14 @@ static PyObject *clusterize(PyObject *Py_UNUSED(self), PyObject *args) {
static PyObject *get_cluster_dt(PyObject *Py_UNUSED(self), PyObject *args) {
if (!PyArg_ParseTuple(args, ""))
return NULL;
return (PyObject*)cluster_dt();
return (PyObject *)cluster_dt();
}
static PyObject *get_frame_header_dt(PyObject *Py_UNUSED(self), PyObject *args) {
static PyObject *get_frame_header_dt(PyObject *Py_UNUSED(self),
PyObject *args) {
if (!PyArg_ParseTuple(args, ""))
return NULL;
return (PyObject*)frame_header_dt();
return (PyObject *)frame_header_dt();
}
// Module docstring, shown as a part of help(creader)
src/data_types.h
+7 -1
View File
@@ -35,7 +35,12 @@ typedef struct {
double etay;
} ClusterAnalysis;
enum Decoder { MOENCH_03 = 3 };
enum DetectorType {
DT_GENERIC = 0,
DT_MOENCH_03 = 3,
DT_MOENCH_04_A = 4,
DT_MOENCH_04_AD = 5
};
typedef struct
{
@@ -56,4 +61,5 @@ typedef struct
} Header;
#endif
src/raw_reader.c
+126 -31
View File
@@ -1,13 +1,14 @@
#include "raw_reader.h"
#include "data_types.h"
#include <stdbool.h>
#include <stdlib.h>
#include <string.h>
int64_t read_raw(FILE *fp, int64_t n_frames, size_t frame_size, char* out_buf, Header* header_out) {
int64_t read_raw_m03(FILE *fp, int64_t n_frames, char *frame_out,
Header *header_out) {
const size_t frame_size = 400 * 400 * 2;
char *tmp = malloc(frame_size);
Header h;
@@ -17,9 +18,9 @@ int64_t read_raw(FILE *fp, int64_t n_frames, size_t frame_size, char* out_buf, H
if (fread(&h, sizeof(Header), 1, fp) != 1) {
break;
}
if(header_out){
memcpy(header_out, &h, sizeof(Header));
header_out++;
if (header_out) {
memcpy(header_out, &h, sizeof(Header));
header_out++;
}
// Read frame to temporary buffer
@@ -28,9 +29,9 @@ int64_t read_raw(FILE *fp, int64_t n_frames, size_t frame_size, char* out_buf, H
}
// Decode frame and write to numpy output array
decode_moench03((uint16_t *)tmp, (uint16_t *)out_buf);
decode_moench03((uint16_t *)tmp, (uint16_t *)frame_out);
out_buf += frame_size;
frame_out += frame_size;
frames_read++;
}
@@ -38,29 +39,123 @@ int64_t read_raw(FILE *fp, int64_t n_frames, size_t frame_size, char* out_buf, H
return frames_read;
}
void decode_moench03(const uint16_t *buf, uint16_t *out_buf)
{
int adc_numbers[32] = {12, 13, 14, 15, 12, 13, 14, 15, 8, 9, 10, 11, 8, 9, 10, 11,
4, 5, 6, 7, 4, 5, 6, 7, 0, 1, 2, 3, 0, 1, 2, 3};
for (int n_pixel = 0; n_pixel < 5000; n_pixel++)
{
int64_t read_raw_m04(FILE *fp, int64_t n_frames, char *frame_out,
char *digital_out, Header *header_out) {
for (int i_sc = 0; i_sc < 32; i_sc++)
{
// analog part
int adc_nr = adc_numbers[i_sc];
int col = ((adc_nr * 25) + (n_pixel % 25));
int row = 0;
if (i_sc / 4 % 2 == 0)
{
row = 199 - (n_pixel / 25);
}
else
{
row = 200 + (n_pixel / 25);
}
int i_analog = n_pixel * 32 + i_sc;
out_buf[row * 400 + col] = buf[i_analog] & 0x3FFF;
const size_t frame_size = 400 * 400 * 2;
const size_t digital_frame_size = 5000 * 8;
char *tmp = malloc(frame_size);
char *digital_tmp = malloc(digital_frame_size);
Header h;
int64_t frames_read = 0;
#ifdef CR_VERBOSE
printf("MOENCH04: digital_out: %p\n", digital_out);
#endif
while (frames_read < n_frames) {
// Read header to temp buffer, return on fail
if (fread(&h, sizeof(Header), 1, fp) != 1) {
break;
}
if (header_out) {
memcpy(header_out, &h, sizeof(Header));
header_out++;
}
// Read frame to temporary buffer
if (fread(tmp, frame_size, 1, fp) != 1) {
break;
}
if (digital_out) {
if (fread(digital_tmp, digital_frame_size, 1, fp) != 1) {
break;
}
}
// Decode frame and write to numpy output array
// decode_moench03((uint16_t *)tmp, (uint16_t *)frame_out);
decode_moench04(tmp, digital_tmp, frame_out, digital_out);
frame_out += frame_size;
if (digital_out)
digital_out += 400*400; //one byte per pixel
frames_read++;
}
free(tmp);
free(digital_tmp);
return frames_read;
}
void decode_moench03(const uint16_t *buf, uint16_t *out_buf) {
int adc_numbers[32] = {12, 13, 14, 15, 12, 13, 14, 15, 8, 9, 10,
11, 8, 9, 10, 11, 4, 5, 6, 7, 4, 5,
6, 7, 0, 1, 2, 3, 0, 1, 2, 3};
for (int n_pixel = 0; n_pixel < 5000; n_pixel++) {
for (int i_sc = 0; i_sc < 32; i_sc++) {
// analog part
int adc_nr = adc_numbers[i_sc];
int col = ((adc_nr * 25) + (n_pixel % 25));
int row = 0;
if (i_sc / 4 % 2 == 0) {
row = 199 - (n_pixel / 25);
} else {
row = 200 + (n_pixel / 25);
}
int i_analog = n_pixel * 32 + i_sc;
out_buf[row * 400 + col] = buf[i_analog] & 0x3FFF;
}
}
}
void decode_moench04(const uint16_t *analog_data, const uint64_t *digital_data,
uint16_t *analog_frame, uint8_t *digital_frame) {
int adc_numbers[32] = {9, 8, 11, 10, 13, 12, 15, 14, 1, 0, 3,
2, 5, 4, 7, 6, 23, 22, 21, 20, 19, 18,
17, 16, 31, 30, 29, 28, 27, 26, 25, 24};
int dbits_bit[32] = {-1, -1, -1, -1, -1, -1, 1, 3, 5, 7, -1,
-1, -1, -1, -1, -1, 62, 60, 58, 56, 54, 52,
50, 48, 63, 61, 59, 57, 55, 53, 51, 49};
for (int n_pixel = 0; n_pixel < 5000; n_pixel++) {
uint64_t current_dbits = digital_data[n_pixel];
for (int i_sc = 0; i_sc < 32; i_sc++) {
// analog part
int adc_nr = adc_numbers[i_sc];
int col = ((adc_nr % 16) * 25) + (n_pixel % 25);
int row = 0;
if (i_sc < 16) {
row = 199 - (n_pixel / 25);
} else {
row = 200 + (n_pixel / 25);
}
int i_analog = n_pixel * 32 + i_sc;
analog_frame[row * 400 + col] = analog_data[i_analog] & 0x3FFF;
// if we pass digital data to be decoded
if (digital_frame) {
int bit_loc = dbits_bit[adc_nr];
if (bit_loc < 0) {
digital_frame[row * 400 + col] = 2;
} else {
uint8_t dbit = (current_dbits >> bit_loc) & 1U;
if ((6 <= adc_nr) & (adc_nr <= 9)) {
digital_frame[row * 400 + col] = dbit;
} else {
digital_frame[row * 400 + col] = 1 - dbit;
}
}
} else {
// digital_frame[row * 400 + col] = 2;
}
}
}
}
}
src/raw_reader.h
+21 -2
View File
@@ -1,8 +1,27 @@
#pragma once
#include <stdbool.h>
#include <stdint.h>
#include <stdio.h>
#include "data_types.h"
int64_t read_raw(FILE *fp, int64_t n_frames, size_t frame_size, char* out_buf, Header* header_out);
int64_t read_raw_m03(
FILE *fp,
int64_t n_frames,
char* frame_out,
Header* header_out
);
void decode_moench03(const uint16_t *buf, uint16_t *out_buf);
int64_t read_raw_m04(
FILE *fp,
int64_t n_frames,
char* frame_out,
char* digital_out,
Header* header_out
);
void decode_moench03(const uint16_t *buf, uint16_t *out_buf);
void decode_moench04(const uint16_t *analog_data,
const uint64_t *digital_data,
uint16_t *analog_frame,
uint8_t *digital_frame);
tests/test_ClusterReader.py
+32 -2
View File
@@ -85,10 +85,40 @@ def test_read_file_with_37_frames(data_path):
def test_read_file_with_37_frames_in_chunks(data_path):
#File shoud contain 37 frames with 5 clusters each
#Full spec in utils/write_test_data.py
fname= (data_path/'37frames_with_5_clusters.clust').as_posix()
fname= data_path/'37frames_with_5_clusters.clust'
r = ClusterFileReader(fname)
total_clusters = 0
while (clusters:=r.read(7)).size:
total_clusters += clusters.size
assert total_clusters == 185
def test_read_file_with_noise_mask(data_path):
#No mask
fname= data_path/'noise_test.clust'
r = ClusterFileReader(fname)
cl = r.read(85) #file contains 70 clusters
assert cl.size == 70
#noise mask with zeros
noise_cut = np.zeros((400,400))
r = ClusterFileReader(fname)
cl = r.read(85, noise_cut)
assert cl.size == 70
#only pixel 80, 133 above noise
noise_cut[:] = 100
#TODO! Agree on orientation of noise mask!
# noise_cut[80,133] = 0
noise_cut[133,80] = 0
r = ClusterFileReader(fname)
cl = r.read(85, noise_cut)
assert cl.size == 10
def test_chunk_config(data_path):
fname= data_path/'noise_test.clust'
#File contains total 70 clusters
r = ClusterFileReader(fname, chunk = 5)
assert r.read().size == 5
assert r.read(10).size == 10
tests/test_RawFileReader.py
+57 -10
View File
@@ -1,31 +1,78 @@
import pytest
import os, sys
from creader import RawFileReader
from creader import RawFileReader, DetectorType
from fixtures import data_path
import numpy as np
m03 = DetectorType.MOENCH_03
m04a= DetectorType.MOENCH_04_A
m04ad= DetectorType.MOENCH_04_AD
def test_references_on_read(data_path):
fname= data_path/'test_d0_f0_0.raw'
r = RawFileReader(fname)
fname= data_path/'Moench03_d0_f0_0.raw'
r = RawFileReader(fname, m03)
frames = r.read(10)
assert sys.getrefcount(frames) == 2 #Over counts by one due to call by reference
def test_references_on_read_with_header(data_path):
fname= data_path/'test_d0_f0_0.raw'
r = RawFileReader(fname, header = True)
fname= data_path/'Moench03_d0_f0_0.raw'
r = RawFileReader(fname, m03, header = True)
frames, header = r.read(100)
assert sys.getrefcount(frames) == 2 #Over counts by one due to call by reference
assert sys.getrefcount(header) == 2
def test_reading_frame_numbers(data_path):
fname= data_path/'test_d0_f0_0.raw'
r = RawFileReader(fname, header = True)
fname= data_path/'Moench03_d0_f0_0.raw'
r = RawFileReader(fname, header = True, detector_type = m03)
frames, header = r.read(1000)
assert (header['Frame Number'] == np.arange(201,1201, dtype = np.uint64)).all()
def test_reading_more_files_than_available(data_path):
fname= data_path/'test_d0_f0_0.raw'
r = RawFileReader(fname, header = True)
fname= data_path/'Moench03_d0_f0_0.raw'
r = RawFileReader(fname, m03, header = True)
frames, header = r.read(1500)
assert frames.shape == (1000,400, 400)
assert header.size == 1000
assert header.size == 1000
def test_references_on_m04_a(data_path):
fname= data_path/'Moench04_no_digital_d0_f0_0.raw'
r = RawFileReader(fname, m04a)
frames = r.read(3)
assert sys.getrefcount(frames) == 2 #Over counts by one due to call by reference
assert frames.shape == (3,400,400)
def test_references_on_m04_a_with_heaer(data_path):
fname= data_path/'Moench04_no_digital_d0_f0_0.raw'
r = RawFileReader(fname, m04a, header = True)
frames, header = r.read(7)
assert sys.getrefcount(frames) == 2 #Over counts by one due to call by reference
assert frames.shape == (7,400,400)
assert sys.getrefcount(header) == 2
assert header.size == 7
def test_references_on_m04_ad(data_path):
fname= data_path/'Moench04_digital_d0_f0_0.raw'
r = RawFileReader(fname, m04ad)
analog, digital = r.read(3)
assert sys.getrefcount(analog) == 2 #Over counts by one due to call by reference
assert analog.shape == (3,400,400)
assert sys.getrefcount(digital) == 2
def test_references_on_m04_ad_with_header(data_path):
fname= data_path/'Moench04_digital_d0_f0_0.raw'
r = RawFileReader(fname, m04ad, header = True)
analog, digital, header = r.read(3)
assert sys.getrefcount(analog) == 2 #Over counts by one due to call by reference
assert analog.shape == (3,400,400)
assert sys.getrefcount(digital) == 2
assert sys.getrefcount(header) == 2
def test_read_too_many_m04_frames(data_path):
fname= data_path/'Moench04_digital_d0_f0_0.raw'
r = RawFileReader(fname, m04ad, header = True)
analog, digital, header = r.read(150)
assert analog.shape == (100,400,400)
assert digital.shape == (100, 400, 400)
assert header.size == 100
assert sys.getrefcount(digital) == 2
utils/write_test_data.py
+18 -1
View File
@@ -40,4 +40,21 @@ with open(path/'37frames_with_5_clusters.clust', 'wb') as f:
data['data'] = np.arange(j,j+9)
print(data)
data.tofile(f)
#Writing out data to test noise cuts
header[1] = 7
with open(path/'noise_test.clust', 'wb') as f:
for i in range(10):
data['x'] = 50
data['y'] = 133
data['data'][:] = 50
header.tofile(f)
print(header)
header[0] += 1
for j in range(7):
print(data)
data.tofile(f)
data['x'] += 10