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from glob import glob
import numpy as np
from collections import namedtuple
HeaderInfo = namedtuple('HeaderInfo', ['version', 'how', 'ncomp', 'nghost'])
_component_names = ['Ex', 'Ey', 'Ez', 'Bx', 'By', 'Bz', 'jx', 'jy', 'jz', 'rho']
def read_data(plt_file):
'''
This function reads the raw (i.e. not averaged to cell centers) data
from a WarpX plt file. The plt file must have been written with the
plot_raw_fields option turned on, so that it contains a raw_data
sub-directory. This is only really useful for single-level data.
Arguments:
plt_file : An AMReX plt_file file. Must contain a raw_data directory.
Returns:
A list of dictionaries where the keys are field name strings and the values
are numpy arrays. Each entry in the list corresponds to a different level.
Example:
>>> data = read_data("plt00016")
>>> print(data.keys())
>>> print(data['Ex'].shape)
'''
all_data = []
raw_files = glob(plt_file + "/raw_fields/Level_*/")
for raw_file in raw_files:
field_names = _get_field_names(raw_file)
data = {}
for field in field_names:
data[field] = _read_field(raw_file, field)
all_data.append(data)
return all_data
def read_lab_snapshot(snapshot):
'''
This reads the data from one of the lab frame snapshots generated when
WarpX is run with boosted frame diagnostics turned on. It returns a
dictionary of numpy arrays, where each key corresponds to one of the
data fields ("Ex", "By,", etc... ). These values are cell-centered.
'''
hdrs = glob(snapshot + "/Level_0/slice?????_H")
hdrs.sort()
boxes, file_names, offsets, header = _read_header(hdrs[0])
dom_lo, dom_hi = _combine_boxes(boxes)
domain_size = dom_hi - dom_lo + 1
space_dim = len(dom_lo)
data = {}
for i in range(header.ncomp):
if space_dim == 3:
data[component_names[i]] = np.zeros((domain_size[0], domain_size[1], len(hdrs)))
elif space_dim == 2:
data[component_names[i]] = np.zeros((domain_size[0], len(hdrs)))
for i, hdr in enumerate(hdrs):
slice_data = _read_slice(snapshot, hdr)
if data is None:
data = slice_data
else:
for k,v in slice_data.items():
if space_dim == 3:
data[k][:,:,i] = v[:,:,0]
elif space_dim == 2:
data[k][:,i] = v[:,0]
return data
def _get_field_names(raw_file):
header_files = glob(raw_file + "*_H")
return [hf.split("/")[-1][:-2] for hf in header_files]
def _string_to_numpy_array(s):
return np.array([int(v) for v in s[1:-1].split(",")], dtype=np.int64)
def _line_to_numpy_arrays(line):
lo_corner = _string_to_numpy_array(line[0][1:])
hi_corner = _string_to_numpy_array(line[1][:])
node_type = _string_to_numpy_array(line[2][:-1])
return lo_corner, hi_corner, node_type
def _read_header(header_file):
with open(header_file, "r") as f:
version = int(f.readline())
how = int(f.readline())
ncomp = int(f.readline())
nghost = int(f.readline())
header = HeaderInfo(version, how, ncomp, nghost)
# skip the next line
f.readline()
# read boxes
boxes = []
for line in f:
clean_line = line.strip().split()
if clean_line == [')']:
break
lo_corner, hi_corner, node_type = _line_to_numpy_arrays(clean_line)
boxes.append((lo_corner - nghost,
hi_corner + nghost,
node_type))
# read the file and offset position for the corresponding box
file_names = []
offsets = []
for line in f:
if line.startswith("FabOnDisk:"):
clean_line = line.strip().split()
file_names.append(clean_line[1])
offsets.append(int(clean_line[2]))
return boxes, file_names, offsets, header
def _combine_boxes(boxes):
lo_corners, hi_corners = zip(*[(box[0], box[1]) for box in boxes])
domain_lo = np.min(lo_corners, axis=0)
domain_hi = np.max(hi_corners, axis=0)
return domain_lo, domain_hi
def _read_field(raw_file, field_name):
header_file = raw_file + field + "_H"
boxes, file_names, offsets, header = _read_header(header_file)
ng = header.nghost
lo, hi = _combine_boxes(boxes)
data = np.zeros(hi - lo + 1)
for box, fn, offset in zip(boxes, file_names, offsets):
lo = box[0]
hi = box[1]
shape = hi - lo + 1
with open(raw_file + fn, "rb") as f:
f.seek(offset)
f.readline() # always skip the first line
arr = np.fromfile(f, 'float64', np.product(shape))
arr = arr.reshape(shape, order='F')
data[[slice(l,h+1) for l, h in zip(lo+ng, hi+ng)]] = arr
return data
def _read_slice(snapshot, header_fn):
boxes, file_names, offsets, header = _read_header(header_fn)
ng = header.nghost
dom_lo, dom_hi = _combine_boxes(boxes)
all_data = {}
for i in range(header.ncomp):
all_data[_component_names[i]] = np.zeros(dom_hi - dom_lo + 1)
for box, fn, offset in zip(boxes, file_names, offsets):
lo = box[0] - dom_lo
hi = box[1] - dom_lo
shape = hi - lo + 1
size = np.product(shape)
with open(snapshot + "/Level_0/" + fn, "rb") as f:
f.seek(offset)
f.readline() # always skip the first line
arr = np.fromfile(f, 'float64', header.ncomp*size)
for i in range(header.ncomp):
comp_data = arr[i*size:(i+1)*size].reshape(shape, order='F')
data = all_data[_component_names[i]]
data[[slice(l,h+1) for l, h in zip(lo, hi)]] = comp_data
all_data[_component_names[i]] = data
return all_data
if __name__ == "__main__":
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
data = read_data("plt00040")
# print the shapes of all the fields
for level in range(2):
for name, vals in data[level].items():
print(level, name, vals.shape, vals.min(), vals.max())
# make a projection along the z-axis of the 'jx' field for level 0
level = 0
plt.pcolormesh(data[level]['jx'].sum(axis=2))
plt.savefig('jx')
# make a projection along the x-axis of the 'Bx_cp' field for level 1
level = 1
plt.pcolormesh(data[level]['Bx_cp'].sum(axis=0))
plt.savefig('Bx_cp')
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