def __read_dataset_and_convert_to_h5data(k, v, data_attrs, dflt_ax_unit,
timestamp, run_attrs):
ax_label, ax_off, g_spacing, ax_pos, unitsi = \
data_attrs.pop('axisLabels'), data_attrs.pop('gridGlobalOffset', 0), \
data_attrs.pop('gridSpacing'), data_attrs.pop('position',
0), data_attrs.pop(
'unitSI', 1.)
ax_min = (ax_off + ax_pos * g_spacing) * unitsi
ax_max = ax_min + v.shape * g_spacing * unitsi
# prepare the axes data
axes = []
for aln, an, amax, amin, anp in zip(ax_label, ax_label, ax_max, ax_min,
v.shape):
ax_attrs = {
'LONG_NAME': aln.decode('utf-8'),
'NAME': an.decode('utf-8'),
'UNITS': dflt_ax_unit
}
data_axis = DataAxis(amin, amax, anp, attrs=ax_attrs)
axes.append(data_axis)
return H5Data(v[()],
timestamp=timestamp,
data_attrs=data_attrs,
run_attrs=run_attrs,
axes=axes)
def read_zdf(filename, path=None):
"""
HDF reader for Osiris/Visxd compatible HDF files.
Returns: H5Data object.
"""
fname = filename if not path else path + '/' + filename
data, info = zdf.read(filename)
run_attrs, data_attrs = {}, {}
nx = list(reversed(info.grid.nx))
axes = [
DataAxis(ax.min,
ax.max,
nx[i],
attrs={
'LONG_NAME': ax.label,
'NAME': ax.label.replace('_', ''),
'UNITS': OSUnits(ax.units)
}) for i, ax in enumerate(reversed(info.grid.axis))
]
timestamp = fn_rule.findall(os.path.basename(filename))[0]
run_attrs['NX'] = info.grid.nx
run_attrs['TIME UNITS'] = OSUnits(info.iteration.tunits)
run_attrs['TIME'] = np.array([info.iteration.t])
run_attrs['TIMESTAMP'] = timestamp
data_attrs['LONG_NAME'] = info.grid.label
data_attrs['NAME'] = info.grid.label.replace('_', '')
data_attrs['UNITS'] = OSUnits(info.grid.units)
return H5Data(data,
timestamp=timestamp,
data_attrs=data_attrs,
run_attrs=run_attrs,
axes=axes)
def write_h5_openpmd(data,
filename=None,
path=None,
dataset_name=None,
overwrite=True,
axis_name=None,
time_to_si=1.0,
length_to_si=1.0,
data_to_si=1.0):
"""
Usage:
write_h5_openpmd(diag_data, '/path/to/filename.h5') # writes out Visxd compatible HDF5 data.
Since h5 format does not support python strings, we will convert all string data (units, names etc)
to bytes strings before writing.
see also read_h5() function in this file
"""
if isinstance(data, H5Data):
data_object = data
elif isinstance(data, np.ndarray):
data_object = H5Data(data)
else:
try: # maybe it's something we can wrap in a numpy array
data_object = H5Data(np.array(data))
except:
raise Exception(
"Invalid data type.. we need a 'hdf5_data', numpy array, or something that can go in a numy array"
)
# now let's make the H5Data() compatible with VisXd and such...
# take care of the NAME attribute.
if dataset_name is not None:
current_name_attr = dataset_name
elif data_object.name:
current_name_attr = data_object.name
else:
current_name_attr = "Data"
fname = path if path else ''
if filename is not None:
fname += filename
elif data_object.timestamp is not None:
fname += current_name_attr + '-' + data_object.timestamp + '.h5'
else:
raise Exception("You did not specify a filename!!!")
if os.path.isfile(fname):
if overwrite:
os.remove(fname)
else:
c = 1
while os.path.isfile(fname[:-3] + '.copy' + str(c) + '.h5'):
c += 1
fname = fname[:-3] + '.copy' + str(c) + '.h5'
h5file = h5py.File(fname, 'w')
# now put the data in a group called this...
# h5dataset = h5file.create_dataset(current_name_attr, data_object.shape, data=data_object.view(np.ndarray))
# these are required.. so make defaults ones...
# h5dataset.attrs['UNITS'], h5dataset.attrs['LONG_NAME'] = np.array([b'']), np.array([b''])
# convert osunit class back to ascii
data_attrs = data_object.data_attrs.copy()
try:
data_attrs['UNITS'] = np.array(
[str(data_object.data_attrs['UNITS']).encode('utf-8')])
except:
data_attrs['UNITS'] = np.array([b'a.u.'])
# copy over any values we have in the 'H5Data' object;
# for key, value in data_attrs.items():
# h5dataset.attrs[key] = np.array([value.encode('utf-8')]) if isinstance(value, str) else value
# these are required so we make defaults..
h5file.attrs['DT'] = [1.0]
h5file.attrs['ITER'] = [0]
h5file.attrs['MOVE C'] = [0]
h5file.attrs['PERIODIC'] = [0]
h5file.attrs['TIME'] = [0.0]
h5file.attrs['TIME UNITS'] = [b'']
h5file.attrs['TYPE'] = [b'grid']
h5file.attrs['XMIN'] = [0.0]
h5file.attrs['XMAX'] = [0.0]
h5file.attrs['openPMD'] = '1.0.0'
h5file.attrs['openPMDextension'] = 0
h5file.attrs['iterationEncoding'] = 'fileBased'
h5file.attrs['basePath'] = '/data/%T'
h5file.attrs['meshesPath'] = 'mesh/'
h5file.attrs['particlesPath'] = 'particles/'
# now make defaults/copy over the attributes in the root of the hdf5
baseid = h5file.create_group("data")
iterid = baseid.create_group(str(data.run_attrs['ITER'][0]))
meshid = iterid.create_group("mesh")
datasetid = meshid.create_dataset(data_attrs['NAME'],
data_object.shape,
data=data_object.view(np.ndarray))
# h5dataset = datasetid.create_dataset(current_name_attr, data_object.shape, data=data_object.view(np.ndarray))
# for key, value in data_object.run_attrs.items():
# h5file.attrs[key] = np.array([value.encode('utf-8')]) if isinstance(value, str) else value
iterid.attrs['dt'] = data.run_attrs['DT'][0]
iterid.attrs['time'] = data.run_attrs['TIME'][0]
iterid.attrs['timeUnitSI'] = time_to_si
number_axis_objects_we_need = len(data_object.axes)
deltax = data.run_attrs['XMAX'] - data.run_attrs['XMIN']
local_offset = np.arange(number_axis_objects_we_need, dtype=np.float32)
local_globaloffset = np.arange(number_axis_objects_we_need,
dtype=np.float64)
local_position = np.arange(number_axis_objects_we_need, dtype=np.float32)
local_position[0] = 0.0
local_position[1] = 0.0
local_gridspacing = np.arange(number_axis_objects_we_need,
dtype=np.float32)
if (number_axis_objects_we_need == 1):
local_axislabels = [b'x1']
deltax[0] = deltax[0] / data_object.shape[0]
local_gridspacing = np.float32(deltax)
local_globaloffset[0] = np.float32(0.0)
local_offset[0] = np.float32(0.0)
elif (number_axis_objects_we_need == 2):
local_axislabels = [b'x1', b'x2']
deltax[0] = deltax[0] / data_object.shape[0]
deltax[1] = deltax[1] / data_object.shape[1]
temp = deltax[0]
deltax[0] = deltax[1]
deltax[1] = temp
local_gridspacing = np.float32(deltax)
local_globaloffset[0] = np.float32(0.0)
local_globaloffset[1] = np.float32(0.0)
local_offset[0] = np.float32(0.0)
local_offset[1] = np.float32(0.0)
else:
local_axislabels = [b'x1', b'x2', b'x3']
deltax[0] = deltax[0] / data_object.shape[0]
deltax[1] = deltax[1] / data_object.shape[1]
deltax[2] = deltax[2] / data_object.shape[2]
temp = deltax[0]
deltax[0] = deltax[2]
deltax[2] = temp
local_gridspacing = np.float32(deltax)
local_globaloffset[0] = np.float32(0.0)
local_globaloffset[1] = np.float32(0.0)
local_globaloffset[2] = np.float32(0.0)
local_offset[0] = np.float32(0.0)
local_offset[1] = np.float32(0.0)
local_offset[2] = np.float32(0.0)
datasetid.attrs['dataOrder'] = 'F'
datasetid.attrs['geometry'] = 'cartesian'
datasetid.attrs['geometryParameters'] = 'cartesian'
datasetid.attrs['axisLabels'] = local_axislabels
datasetid.attrs['gridUnitSI'] = np.float64(length_to_si)
datasetid.attrs['unitSI'] = np.float64(data_to_si)
datasetid.attrs['position'] = local_position
datasetid.attrs['gridSpacing'] = local_gridspacing
datasetid.attrs['gridGlobalOffset'] = local_globaloffset
# # now go through and set/create our axes HDF entries.
# if not axis_name:
# axis_name = "AXIS/AXIS"
# for i in range(0, number_axis_objects_we_need):
# _axis_name = axis_name + str(number_axis_objects_we_need - i)
# if _axis_name not in h5file:
# axis_data = h5file.create_dataset(_axis_name, (2,), 'float64')
# else:
# axis_data = h5file[_axis_name]
# # set the extent to the data we have...
# axis_data[0] = data_object.axes[i].min
# axis_data[1] = data_object.axes[i].max
# # fill in any values we have stored in the Axis object
# for key, value in data_object.axes[i].attrs.items():
# if key == 'UNITS':
# try:
# axis_data.attrs['UNITS'] = np.array([str(data_object.axes[i].attrs['UNITS']).encode('utf-8')])
# except:
# axis_data.attrs['UNITS'] = np.array([b'a.u.'])
# else:
# axis_data.attrs[key] = np.array([value.encode('utf-8')]) if isinstance(value, str) else value
h5file.close()
def write_h5(data,
filename=None,
path=None,
dataset_name=None,
overwrite=True,
axis_name=None):
"""
Usage:
write(diag_data, '/path/to/filename.h5') # writes out Visxd compatible HDF5 data.
Since h5 format does not support python strings, we will convert all string data (units, names etc)
to bytes strings before writing.
see also read_h5() function in this file
"""
if isinstance(data, H5Data):
data_object = data
elif isinstance(data, np.ndarray):
data_object = H5Data(data)
else:
try: # maybe it's something we can wrap in a numpy array
data_object = H5Data(np.array(data))
except:
raise Exception(
"Invalid data type.. we need a 'hdf5_data', numpy array, or somehitng that can go in a numy array"
)
# now let's make the H5Data() compatible with VisXd and such...
# take care of the NAME attribute.
if dataset_name is not None:
current_name_attr = dataset_name
elif data_object.name:
current_name_attr = data_object.name
else:
current_name_attr = "Data"
fname = path if path else ''
if filename is not None:
fname += filename
elif data_object.timestamp is not None:
fname += current_name_attr + '-' + data_object.timestamp + '.h5'
else:
raise Exception("You did not specify a filename!!!")
if os.path.isfile(fname):
if overwrite:
os.remove(fname)
else:
c = 1
while os.path.isfile(fname[:-3] + '.copy' + str(c) + '.h5'):
c += 1
fname = fname[:-3] + '.copy' + str(c) + '.h5'
h5file = h5py.File(fname, 'w')
# now put the data in a group called this...
h5dataset = h5file.create_dataset(current_name_attr,
data_object.shape,
data=data_object.view(np.ndarray))
# these are required.. so make defaults ones...
h5dataset.attrs['UNITS'], h5dataset.attrs['LONG_NAME'] = np.array(
[b'']), np.array([b''])
# convert osunit class back to ascii
data_attrs = data_object.data_attrs.copy()
try:
data_attrs['UNITS'] = np.array(
[str(data_object.data_attrs['UNITS']).encode('utf-8')])
except:
data_attrs['UNITS'] = np.array([b'a.u.'])
# copy over any values we have in the 'H5Data' object;
for key, value in data_attrs.items():
h5dataset.attrs[key] = np.array([value.encode('utf-8')]) if isinstance(
value, str) else value
# these are required so we make defaults..
h5file.attrs['DT'] = [1.0]
h5file.attrs['ITER'] = [0]
h5file.attrs['MOVE C'] = [0]
h5file.attrs['PERIODIC'] = [0]
h5file.attrs['TIME'] = [0.0]
h5file.attrs['TIME UNITS'] = [b'']
h5file.attrs['TYPE'] = [b'grid']
h5file.attrs['XMIN'] = [0.0]
h5file.attrs['XMAX'] = [0.0]
# now make defaults/copy over the attributes in the root of the hdf5
for key, value in data_object.run_attrs.items():
h5file.attrs[key] = np.array([value.encode('utf-8')]) if isinstance(
value, str) else value
number_axis_objects_we_need = len(data_object.axes)
# now go through and set/create our axes HDF entries.
if not axis_name:
axis_name = "AXIS/AXIS"
for i in range(0, number_axis_objects_we_need):
_axis_name = axis_name + str(number_axis_objects_we_need - i)
if _axis_name not in h5file:
axis_data = h5file.create_dataset(_axis_name, (2, ), 'float64')
else:
axis_data = h5file[_axis_name]
# set the extent to the data we have...
axis_data[0] = data_object.axes[i].min
axis_data[1] = data_object.axes[i].max
# fill in any values we have stored in the Axis object
for key, value in data_object.axes[i].attrs.items():
if key == 'UNITS':
try:
axis_data.attrs['UNITS'] = np.array([
str(data_object.axes[i].attrs['UNITS']).encode('utf-8')
])
except:
axis_data.attrs['UNITS'] = np.array([b'a.u.'])
else:
axis_data.attrs[key] = np.array([
value.encode('utf-8')
]) if isinstance(value, str) else value
h5file.close()
def read_h5(filename, path=None, axis_name="AXIS/AXIS"):
"""
HDF reader for Osiris/Visxd compatible HDF files... This will slurp in the data
and the attributes that describe the data (e.g. title, units, scale).
Usage:
diag_data = read_hdf('e1-000006.h5') # diag_data is a subclass of numpy.ndarray with extra attributes
print(diag_data) # print the meta data
print(diag_data.view(numpy.ndarray)) # print the raw data
print(diag_data.shape) # prints the dimension of the raw data
print(diag_data.run_attrs['TIME']) # prints the simulation time associated with the hdf5 file
diag_data.data_attrs['UNITS'] # print units of the dataset points
list(diag_data.data_attrs) # lists all attributes related to the data array
list(diag_data.run_attrs) # lists all attributes related to the run
print(diag_data.axes[0].attrs['UNITS']) # prints units of X-axis
list(diag_data.axes[0].attrs) # lists all variables of the X-axis
diag_data[slice(3)]
print(rw.view(np.ndarray))
We will convert all byte strings stored in the h5 file to strings which are easier to deal with when writing codes
see also write_h5() function in this file
"""
fname = filename if not path else path + '/' + filename
data_file = h5py.File(fname, 'r')
n_data = scan_hdf5_file_for_main_data_array(data_file)
timestamp, name, run_attrs, data_attrs, axes, data_bundle= '', '', {}, {}, [], []
try:
timestamp = fn_rule.findall(os.path.basename(filename))[0]
except IndexError:
timestamp = '000000'
axis_number = 1
while True:
try:
# try to open up another AXIS object in the HDF's attribute directory
# (they are named /AXIS/AXIS1, /AXIS/AXIS2, /AXIS/AXIS3 ...)
axis_to_look_for = axis_name + str(axis_number)
axis = data_file[axis_to_look_for]
# convert byte string attributes to string
attrs = {}
for k, v in axis.attrs.items():
try:
attrs[k] = v[0].decode('utf-8') if isinstance(v[0],
bytes) else v
except IndexError:
attrs[k] = v.decode('utf-8') if isinstance(v, bytes) else v
axis_min = axis[0]
axis_max = axis[-1]
axis_numberpoints = n_data[0].shape[-axis_number]
data_axis = DataAxis(axis_min,
axis_max,
axis_numberpoints,
attrs=attrs)
axes.insert(0, data_axis)
except KeyError:
break
axis_number += 1
# we need a loop here primarily (I think) for n_ene_bin phasespace data
for the_data_hdf_object in n_data:
name = the_data_hdf_object.name[1:] # ignore the beginning '/'
# now read in attributes of the ROOT of the hdf5..
# there's lots of good info there. strip out the array if value is a string
for key, value in data_file.attrs.items():
try:
run_attrs[key] = value[0].decode('utf-8') if isinstance(
value[0], bytes) else value
except IndexError:
run_attrs[key] = value.decode('utf-8') if isinstance(
value, bytes) else value
# attach attributes assigned to the data array to
# the H5Data.data_attrs object, remove trivial dimension before assignment
for key, value in the_data_hdf_object.attrs.items():
try:
data_attrs[key] = value[0].decode('utf-8') if isinstance(
value[0], bytes) else value
except IndexError:
data_attrs[key] = value.decode('utf-8') if isinstance(
value, bytes) else value
# convert unit string to osunit object
try:
data_attrs['UNITS'] = OSUnits(data_attrs['UNITS'])
except KeyError:
data_attrs['UNITS'] = OSUnits('a.u.')
data_attrs['NAME'] = name
# data_bundle.data = the_data_hdf_object[()]
data_bundle.append(
H5Data(the_data_hdf_object,
timestamp=timestamp,
data_attrs=data_attrs,
run_attrs=run_attrs,
axes=axes))
data_file.close()
if len(data_bundle) == 1:
return data_bundle[0]
else:
return data_bundle
def read_h5_openpmd(filename, path=None):
"""
HDF reader for OpenPMD compatible HDF files... This will slurp in the data
and the attributes that describe the data (e.g. title, units, scale).
Usage:
diag_data = read_hdf_openpmd('EandB000006.h5') # diag_data is a subclass of numpy.ndarray with extra attributes
print(diag_data) # print the meta data
print(diag_data.view(numpy.ndarray)) # print the raw data
print(diag_data.shape) # prints the dimension of the raw data
print(diag_data.run_attrs['TIME']) # prints the simulation time associated with the hdf5 file
diag_data.data_attrs['UNITS'] # print units of the dataset points
list(diag_data.data_attrs) # lists all attributes related to the data array
list(diag_data.run_attrs) # lists all attributes related to the run
print(diag_data.axes[0].attrs['UNITS']) # prints units of X-axis
list(diag_data.axes[0].attrs) # lists all variables of the X-axis
diag_data[slice(3)]
print(rw.view(np.ndarray))
We will convert all byte strings stored in the h5 file to strings which are easier to deal with when writing codes
see also write_h5() function in this file
"""
fname = filename if not path else path + '/' + filename
with h5py.File(fname, 'r') as data_file:
try:
timestamp = fn_rule.findall(os.path.basename(filename))[0]
except IndexError:
timestamp = '00000000'
basePath = data_file.attrs['basePath'].decode('utf-8').replace(
'%T', timestamp)
meshPath = basePath + data_file.attrs['meshesPath'].decode('utf-8')
run_attrs = {
k.upper(): v
for k, v in data_file[basePath].attrs.items()
}
run_attrs.setdefault('TIME UNITS', r'1 / \omega_p')
# read field data
lname_dict, fldl = {
'E1': 'E_x',
'E2': 'E_y',
'E3': 'E_z',
'B1': 'B_x',
'B2': 'B_y',
'B3': 'B_z',
'jx': 'J_x',
'jy': 'J_y',
'jz': 'J_z',
'rho': r'\roh'
}, {}
# k is the field label and v is the field dataset
for k, v in data_file[meshPath].items():
# openPMD doesn't enforce attrs that are required in OSIRIS dataset
data_attrs, dflt_ax_unit = \
{'UNITS': OSUnits(r'm_e c \omega_p e^{-1} '),
'LONG_NAME': lname_dict.get(k, k), 'NAME': k}, r'c \omega_p^{-1}'
data_attrs.update({ia: va for ia, va in v.attrs.items()})
ax_label, ax_off, g_spacing, ax_pos, unitsi = \
data_attrs.pop('axisLabels'), data_attrs.pop('gridGlobalOffset'), \
data_attrs.pop('gridSpacing'), data_attrs.pop('position'), data_attrs.pop('unitSI')
ax_min = (ax_off + ax_pos * g_spacing) * unitsi
ax_max = ax_min + v.shape * g_spacing * unitsi
# prepare the axes data
axes = []
for aln, an, amax, amin, anp in zip(ax_label, ax_label, ax_max,
ax_min, v.shape):
ax_attrs = {
'LONG_NAME': aln.decode('utf-8'),
'NAME': an.decode('utf-8'),
'UNITS': dflt_ax_unit
}
data_axis = DataAxis(amin, amax, anp, attrs=ax_attrs)
axes.append(data_axis)
fldl[k] = H5Data(v[()],
timestamp=timestamp,
data_attrs=data_attrs,
run_attrs=run_attrs,
axes=axes)
return fldl
h5file.close()
if __name__ == '__main__':
import osh5utils as ut
a = np.arange(6.0).reshape(2, 3)
ax, ay = DataAxis(0, 3, 3,
attrs={'UNITS': '1 / \omega_p'
}), DataAxis(10,
11,
2,
attrs={'UNITS': 'c / \omega_p'})
da = {
'UNITS': 'n_0',
'NAME': 'test',
}
h5d = H5Data(a, timestamp='123456', data_attrs=da, axes=[ay, ax])
write_h5(h5d, './test-123456.h5')
rw = read_h5('./test-123456.h5')
h5d = read_h5(
'./test-123456.h5') # read from file to get all default attrs
print("rw is h5d: ", rw is h5d, '\n')
print(repr(rw))
# let's read/write a few times and see if there are mutations to the data
# you should also diff the output h5 files
for i in range(5):
write_h5(rw, './test' + str(i) + '-123456.h5')
rw = read_h5('./test' + str(i) + '-123456.h5')
assert (rw == a).all()
for axrw, axh5d in zip(rw.axes, h5d.axes):
assert axrw.attrs == axh5d.attrs
