本文整理汇总了Python中numpy.lib.recfunctions.append_fields方法的典型用法代码示例。如果您正苦于以下问题:Python recfunctions.append_fields方法的具体用法?Python recfunctions.append_fields怎么用?Python recfunctions.append_fields使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类numpy.lib.recfunctions
的用法示例。
在下文中一共展示了recfunctions.append_fields方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: append_cols
# 需要导入模块: from numpy.lib import recfunctions [as 别名]
# 或者: from numpy.lib.recfunctions import append_fields [as 别名]
def append_cols(M, cols, col_names):
"""Append columns to an existing structured array
Parameters
----------
M : numpy.ndarray
structured array
cols : list of numpy.ndarray
col_names : list of str
names for new columns
Returns
-------
numpy.ndarray
structured array with new columns
"""
if is_nd(cols):
cols = [cols]
M = check_sa(M)
cols = [check_col(
col,
n_rows=M.shape[0],
argument_name='cols[{}]'.format(idx)) for idx, col in enumerate(cols)]
col_names = check_col_names(col_names, n_cols=len(cols))
return nprf.append_fields(M, col_names, data=cols, usemask=False)
示例2: do_flatten
# 需要导入模块: from numpy.lib import recfunctions [as 别名]
# 或者: from numpy.lib.recfunctions import append_fields [as 别名]
def do_flatten(arr, flatten):
if flatten is True:
warnings.warn(" The option flatten=True is deprecated. Please specify the branches you would like "
"to flatten in a list: flatten=['foo', 'bar']", FutureWarning)
arr_, idx = stretch(arr, return_indices=True)
else:
nonscalar = get_nonscalar_columns(arr)
fields = [x for x in arr.dtype.names if (x not in nonscalar or x in flatten)]
for col in flatten:
if col in nonscalar:
pass
elif col in fields:
raise ValueError("Requested to flatten {col} but it has a scalar type"
.format(col=col))
else:
raise ValueError("Requested to flatten {col} but it wasn't loaded from the input file"
.format(col=col))
arr_, idx = stretch(arr, fields=fields, return_indices=True)
arr = append_fields(arr_, '__array_index', idx, usemask=False, asrecarray=True)
return arr
示例3: __getattr__
# 需要导入模块: from numpy.lib import recfunctions [as 别名]
# 或者: from numpy.lib.recfunctions import append_fields [as 别名]
def __getattr__(self, attrname):
# See __init__ for column names
try:
return self.data[attrname]
except:
pass
# If we got here, stations column wasn't in file.
# Try getting it from station mask.
if attrname=='stations':
stations = self.hexMaskToStationCount()
# placing self.data in a list due to this bug
# http://stackoverflow.com/questions/36440557/typeerror-when-appending-fields-to-a-structured-array-of-size-one
self.data = append_fields([self.data], ('stations',), (stations,))
return stations
return None
#raise AttributeError, attrname
示例4: execute
# 需要导入模块: from numpy.lib import recfunctions [as 别名]
# 或者: from numpy.lib.recfunctions import append_fields [as 别名]
def execute(self, parameters, messages):
input_feature=parameters[0].valueAsText
id_field=parameters[1].valueAsText
dens_field=parameters[2].valueAsText
output_feature=parameters[3].valueAsText
calc_device=parameters[4].valueAsText
if '64 bit' not in sys.version and calc_device=='GPU':
arcpy.AddError('Platform is 32bit and has no support for GPU/CUDA.')
return
arcpy.SetProgressorLabel('Calculating Point with Higher Density ...')
arrays=arcpy.da.FeatureClassToNumPyArray(input_feature,[id_field,'SHAPE@X','SHAPE@Y',dens_field])
results=0
if calc_device=='GPU':
from section_gpu import calc_nrst_dist_gpu
results=calc_nrst_dist_gpu(arrays[id_field],arrays['SHAPE@X'],arrays['SHAPE@Y'],arrays[dens_field])
else:
from section_cpu import calc_nrst_dist_cpu
results=calc_nrst_dist_cpu(arrays[id_field],arrays['SHAPE@X'],arrays['SHAPE@Y'],arrays[dens_field],parameters[5].value)
struct_arrays=recfunctions.append_fields(recfunctions.append_fields(recfunctions.append_fields(arrays,'NRSTDIST',data=results[0])\
,'PARENTID',data=results[1])\
,'MULTIPLY',data=results[0]*arrays[dens_field],usemask=False)
# if '64 bit' in sys.version and id_field==arcpy.Describe(input_feature).OIDFieldName:
# sadnl=list(struct_arrays.dtype.names)
# sadnl[sadnl.index(id_field)]='OID@'
# struct_arrays.dtype.names=tuple(sadnl)
arcpy.da.NumPyArrayToFeatureClass(struct_arrays,output_feature,\
('SHAPE@X','SHAPE@Y'),arcpy.Describe(input_feature).spatialReference)
return
示例5: _get_data
# 需要导入模块: from numpy.lib import recfunctions [as 别名]
# 或者: from numpy.lib.recfunctions import append_fields [as 别名]
def _get_data():
filepath = dirname(abspath(__file__))
with open(filepath + '/anes96.csv', "rb") as f:
data = recfromtxt(f, delimiter="\t", names=True, dtype=float)
logpopul = log(data['popul'] + .1)
data = nprf.append_fields(data, 'logpopul', logpopul, usemask=False,
asrecarray=True)
return data
示例6: test_append_fields_dtype_list
# 需要导入模块: from numpy.lib import recfunctions [as 别名]
# 或者: from numpy.lib.recfunctions import append_fields [as 别名]
def test_append_fields_dtype_list(self):
"""Ticket #1676"""
from numpy.lib.recfunctions import append_fields
F = False
base = np.array([1, 2, 3], dtype=np.int32)
data = np.eye(3).astype(np.int32)
names = ['a', 'b', 'c']
dlist = [np.float64, np.int32, np.int32]
try:
a = append_fields(base, names, data, dlist)
except:
raise AssertionError()
示例7: add_slice_labels
# 需要导入模块: from numpy.lib import recfunctions [as 别名]
# 或者: from numpy.lib.recfunctions import append_fields [as 别名]
def add_slice_labels(
dataloader: DictDataLoader, base_task: Task, S: np.recarray
) -> None:
"""Modify a dataloader in-place, adding labels for slice tasks.
Parameters
----------
dataloader
A DictDataLoader whose dataset.Y_dict attribute will be modified in place
base_task
The Task for which we want corresponding slice tasks/labels
S
A recarray (output of SFApplier) containing data fields with slice
indicator information
"""
# Add the base task if it's missing
if "base" not in S.dtype.names:
# Create a new np.recarray with an additional "base" data field
S = rfn.append_fields(
[S], names=[("base")], data=[np.ones(S.shape)], asrecarray=True
)
slice_names = S.dtype.names
Y_dict: Dict[str, np.ndarray] = dataloader.dataset.Y_dict # type: ignore
labels = Y_dict[base_task.name]
for slice_name in slice_names:
# Gather ind labels
ind_labels = torch.LongTensor(S[slice_name]) # type: ignore
# Mask out "inactive" pred_labels as specified by ind_labels
pred_labels = labels.clone()
pred_labels[~ind_labels.bool()] = -1
ind_task_name = f"{base_task.name}_slice:{slice_name}_ind"
pred_task_name = f"{base_task.name}_slice:{slice_name}_pred"
# Update dataloaders
Y_dict[ind_task_name] = ind_labels
Y_dict[pred_task_name] = pred_labels
示例8: __setattr__
# 需要导入模块: from numpy.lib import recfunctions [as 别名]
# 或者: from numpy.lib.recfunctions import append_fields [as 别名]
def __setattr__(self, name, value):
listofattributes = self.__dict__.keys()
if isinstance(value, numpy.ndarray) and name != "_data" and name not in listofattributes :
if value.shape != self.data .shape:
raise ValueError('Arrays should have the same dimensions')
else:
from numpy.lib import recfunctions
detail = getattr(value, 'provenance', None)
data = recfunctions.append_fields(self.data , name, value, usemask=False)
self._data = data
self._provenance += Transformation("Array {0} has been created".format(name), detail)
self.__add_var(name)
else:
dict.__setattr__(self, name, value)
示例9: append_to_rec
# 需要导入模块: from numpy.lib import recfunctions [as 别名]
# 或者: from numpy.lib.recfunctions import append_fields [as 别名]
def append_to_rec(rec_array, data, name):
if hasattr(rec_array, name):
rec_array = remove_from_rec(rec_array, name)
return _append_fields(
rec_array,
name,
data,
dtypes=data.dtype,
usemask=False,
asrecarray=True,
)
return rec_array
示例10: append_fields
# 需要导入模块: from numpy.lib import recfunctions [as 别名]
# 或者: from numpy.lib.recfunctions import append_fields [as 别名]
def append_fields(base, names, data, dtypes=None, fill_value=-1,
usemask=False, # Different from recfunctions default
asrecarray=False):
"""Append fields to numpy structured array
If fields already exists in data, will overwrite
"""
if isinstance(names, (tuple, list)):
# Add multiple fields at once
if dtypes is None:
dtypes = [d.dtype for d in data]
# Convert to numpy arrays so we can use boolean index arrays
names = np.array(names)
data = np.array(data)
dtypes = np.array(dtypes)
not_yet_in_data = True ^ np.in1d(names, base.dtype.names)
# Append the fields that were not in the data
base = recfunctions.append_fields(base,
names[not_yet_in_data].tolist(),
data[not_yet_in_data].tolist(),
dtypes[not_yet_in_data].tolist(),
fill_value, usemask, asrecarray)
# Overwrite the fields that are already in the data
for i in np.where(True ^ not_yet_in_data)[0]:
base[names[i]] = data[i]
return base
else:
# Add single field
if names in base.dtype.names:
# Field already exists: overwrite data
base[names] = data
return base
else:
return recfunctions.append_fields(base, names, data, dtypes,
fill_value, usemask, asrecarray)
示例11: return_videos
# 需要导入模块: from numpy.lib import recfunctions [as 别名]
# 或者: from numpy.lib.recfunctions import append_fields [as 别名]
def return_videos(self, begtime, endtime):
vid = self._videos
begtime *= 1000
endtime *= 1000
# remove empty rows
DTYPE_MAX = iinfo(vid.dtype['duration']).max
vid = vid[vid['duration'] != DTYPE_MAX]
if vid.shape[0] == 0:
raise OSError('No videos for this dataset')
vid = append_fields(vid, 'absolute_end', vid['delay'] + vid['duration'], usemask=False)
# full name without number
i_vid = where((endtime - vid['delay'] >= 0) & (vid['absolute_end'] - begtime >= 0))[0]
mpgfiles = ['VID_' + str(vid['file_ext'][i]) + '.AVI' for i in i_vid]
mpgfiles = [str(self.filename.parent / mpg) for mpg in mpgfiles]
video_beg = (begtime - vid['delay'][i_vid[0]]) / 1000
video_end = (endtime - vid['delay'][i_vid[-1]]) / 1000
lg.debug('First Video (#{}) starts at {}'.format(mpgfiles[0], video_beg))
lg.debug('Last Video (#{}) ends at {}'.format(mpgfiles[-1], video_end))
return mpgfiles, video_beg, video_end
示例12: indexToZero
# 需要导入模块: from numpy.lib import recfunctions [as 别名]
# 或者: from numpy.lib.recfunctions import append_fields [as 别名]
def indexToZero(f, path, col, name="picopore.{}_index", dataColumn=None):
dataset = f[path]
name = name.format(col)
data = f[path].value
if not name in dataset.attrs.keys():
dataColumn = data[col] if dataColumn is None else dataColumn
start_index = min(dataColumn)
dataset.attrs.create(name, start_index, dtype=getDtype(start_index))
dataColumn = dataColumn-start_index
data = drop_fields(data, [col])
data = append_fields(data, [col], [dataColumn], [getDtype(dataColumn)])
return data
示例13: deepLosslessCompress
# 需要导入模块: from numpy.lib import recfunctions [as 别名]
# 或者: from numpy.lib.recfunctions import append_fields [as 别名]
def deepLosslessCompress(f, group):
paths = findDatasets(f, group, "Events")
paths = [path for path in paths if "Basecall" in path]
# index event detection
if "UniqueGlobalKey/channel_id" in f:
sampleRate = f["UniqueGlobalKey/channel_id"].attrs["sampling_rate"]
for path in paths:
if f[path].parent.parent.attrs.__contains__("event_detection"):
# index back to event detection
dataset = f[path].value
start = np.array([int(round(sampleRate * i)) for i in dataset["start"]])
dataset = indexToZero(f, path, "start", dataColumn=start)
move = dataset["move"] # rewrite move dataset because it's int64 for max 2
# otherwise, event by event
dataset = drop_fields(dataset, ["mean", "stdv", "length", "move"])
dataset = append_fields(dataset, ["move"], [move], [getDtype(move)])
rewriteDataset(f, path, compression="gzip", compression_opts=9, dataset=dataset)
# rewrite eventdetection too - start is also way too big here
eventDetectionPath = findDatasets(f, "all", entry_point=f[path].parent.parent.attrs.get("event_detection"))[0]
if "picopore.start_index" not in f[eventDetectionPath].attrs.keys():
eventData = indexToZero(f, eventDetectionPath, "start")
rewriteDataset(f, eventDetectionPath, compression="gzip", compression_opts=9, dataset=eventData)
if __basegroup_name__ not in f:
f.create_group(__basegroup_name__)
for name, group in f.items():
if name != __basegroup_name__:
recursiveCollapseGroups(f, __basegroup_name__, name, group)
return losslessCompress(f, group)
示例14: __init__
# 需要导入模块: from numpy.lib import recfunctions [as 别名]
# 或者: from numpy.lib.recfunctions import append_fields [as 别名]
def __init__(self, filename, date_sep='-', time_sep=':', format='stroke_DC3'):
""" Load NLDN data from a file, into a numpy named array stored in the
*data* attribute. *data*['time'] is relative to the *basedate* datetime
attribute
"""
self.format=format
dtype_specs = getattr(self, format)
nldn_initial = np.genfromtxt(filename, dtype=dtype_specs['columns'])
date_part = np.genfromtxt(nldn_initial['date'],
delimiter=date_sep, dtype=dtype_specs['date_dtype'])
time_part = np.genfromtxt(nldn_initial['time'],
delimiter=time_sep, dtype=dtype_specs['time_dtype'])
dates = [datetime(a['year'], a['month'], a['day'], b['hour'], b['minute'])
for a, b in zip(date_part, time_part)]
min_date = min(dates)
min_date = datetime(min_date.year, min_date.month, min_date.day)
t = np.fromiter( ((d-min_date).total_seconds() for d in dates), dtype='float64')
t += time_part['second']
self.basedate = min_date
data = drop_fields(nldn_initial, ('date', 'time'))
data = append_fields(data, 'time', t)
self.data = data
示例15: execute
# 需要导入模块: from numpy.lib import recfunctions [as 别名]
# 或者: from numpy.lib.recfunctions import append_fields [as 别名]
def execute(self, parameters, messages):
#get params
input_feature=parameters[0].valueAsText
id_field=parameters[1].valueAsText
weight_field=parameters[2].valueAsText
output_feature=parameters[3].valueAsText
kernel_type=parameters[4].valueAsText
calc_device=parameters[7].valueAsText
if '64 bit' not in sys.version and calc_device=='GPU':
arcpy.AddError('Platform is 32bit and has no support for GPU/CUDA.')
return
arcpy.SetProgressorLabel('Calculating Density...')
#calculation
arrays=arcpy.da.FeatureClassToNumPyArray(input_feature,[id_field,'SHAPE@X','SHAPE@Y',weight_field])
densities=0
if calc_device=='GPU':
from section_gpu import calc_density_gpu
densities=calc_density_gpu(arrays['SHAPE@X'],arrays['SHAPE@Y'],\
arrays[weight_field],kernel_type,\
cutoffd=parameters[5].value,sigma=parameters[6].value)
else:
from section_cpu import calc_density_cpu
densities=calc_density_cpu(arrays['SHAPE@X'],arrays['SHAPE@Y'],\
arrays[weight_field],kernel_type,\
parameters[8].value,cutoffd=parameters[5].value,sigma=parameters[6].value)
result_struct=recfunctions.append_fields(recfunctions.drop_fields(arrays,weight_field),\
'DENSITY',data=densities,usemask=False)
# if '64 bit' in sys.version and id_field==arcpy.Describe(input_feature).OIDFieldName:
# sadnl=list(result_struct.dtype.names)
# sadnl[sadnl.index(id_field)]='OID@'
# result_struct.dtype.names=tuple(sadnl)
arcpy.da.NumPyArrayToFeatureClass(result_struct,output_feature,\
('SHAPE@X','SHAPE@Y'),arcpy.Describe(input_feature).spatialReference)
return