def test_to_object_array_width(self):
# see gh-13320
rows = [[1, 2, 3], [4, 5, 6]]
expected = np.array(rows, dtype=object)
out = lib.to_object_array(rows)
tm.assert_numpy_array_equal(out, expected)
expected = np.array(rows, dtype=object)
out = lib.to_object_array(rows, min_width=1)
tm.assert_numpy_array_equal(out, expected)
expected = np.array([[1, 2, 3, None, None], [4, 5, 6, None, None]],
dtype=object)
out = lib.to_object_array(rows, min_width=5)
tm.assert_numpy_array_equal(out, expected)
def test_to_object_array_width(self):
# see gh-13320
rows = [[1, 2, 3], [4, 5, 6]]
expected = np.array(rows, dtype=object)
out = lib.to_object_array(rows)
tm.assert_numpy_array_equal(out, expected)
expected = np.array(rows, dtype=object)
out = lib.to_object_array(rows, min_width=1)
tm.assert_numpy_array_equal(out, expected)
expected = np.array([[1, 2, 3, None, None],
[4, 5, 6, None, None]], dtype=object)
out = lib.to_object_array(rows, min_width=5)
tm.assert_numpy_array_equal(out, expected)
def _list_to_arrays(data, columns, coerce_float=False, dtype=None):
if len(data) > 0 and isinstance(data[0], tuple):
content = list(lib.to_object_array_tuples(data).T)
else:
# list of lists
content = list(lib.to_object_array(data).T)
return _convert_object_array(content, columns, dtype=dtype,
coerce_float=coerce_float)
def _list_to_arrays(data, columns, coerce_float=False, dtype=None):
if len(data) > 0 and isinstance(data[0], tuple):
content = list(lib.to_object_array_tuples(data).T)
else:
# list of lists
content = list(lib.to_object_array(data).T)
return _convert_object_array(content, columns, dtype=dtype,
coerce_float=coerce_float)
def _list_to_arrays(data: list[tuple | list]) -> np.ndarray:
# Returned np.ndarray has ndim = 2
# Note: we already check len(data) > 0 before getting hre
if isinstance(data[0], tuple):
content = lib.to_object_array_tuples(data)
else:
# list of lists
content = lib.to_object_array(data)
return content
def getSpktSpkid(cellGids=[], timeRange=None, sim=None):
"""
Function to efficiently get a subset of spikes based on a timeRange and cellGids list
Parameters
----------
cellGids : list
A list of cells to include by global identifier (GID)
**Default:** ``[]``
timeRange : [start, stop]
A list of two floats specifying the time range of spikes to include
**Default:** ``None`` includes the entire simulation time range
sim : NetPyNE sim object
**Default:** ``None`` uses the current NetPyNE sim object
Returns
-------
(selection, spkt, spkid)
A tuple consisting of the subset in a Pandas dataframe, a list of spike times, and a list of spike GIDs
"""
if not sim:
from .. import sim
try: # Pandas 1.4.0
from pandas._libs import lib as pandaslib
except:
try: # Pandas 0.24 and later
from pandas import _lib as pandaslib
except: # Pandas 0.23 and earlier
from pandas import lib as pandaslib
df = pd.DataFrame(pandaslib.to_object_array(
[sim.allSimData['spkt'], sim.allSimData['spkid']]).transpose(),
columns=['spkt', 'spkid'])
if timeRange:
# binary search is faster than query
min, max = [
int(df['spkt'].searchsorted(timeRange[i])) for i in range(2)
]
else:
min, max = 0, len(df)
if len(cellGids) == 0:
sel = df[min:max]
else:
sel = df[min:max].query('spkid in @cellGids')
spktList = sel['spkt'].tolist()
spkidList = sel['spkid'].tolist()
return sel, spktList, spkidList
def _list_to_arrays(
data: List[Scalar],
columns: Union[Index, List],
) -> Tuple[List[Scalar], Union[Index, List[Axis]]]:
# Note: we already check len(data) > 0 before getting hre
if isinstance(data[0], tuple):
content = lib.to_object_array_tuples(data)
else:
# list of lists
content = lib.to_object_array(data)
return content, columns
def _list_to_arrays(data, columns, coerce_float=False, dtype=None):
if len(data) > 0 and isinstance(data[0], tuple):
content = list(lib.to_object_array_tuples(data).T)
else:
# list of lists
content = list(lib.to_object_array(data).T)
# gh-26429 do not raise user-facing AssertionError
try:
result = _convert_object_array(
content, columns, dtype=dtype, coerce_float=coerce_float
)
except AssertionError as e:
raise ValueError(e) from e
return result
def _list_to_arrays(
data: List[Scalar],
columns: Union[Index, List],
coerce_float: bool = False,
dtype: Optional[DtypeObj] = None,
) -> Tuple[List[Scalar], Union[Index, List[Axis]]]:
if len(data) > 0 and isinstance(data[0], tuple):
content = list(lib.to_object_array_tuples(data).T)
else:
# list of lists
content = list(lib.to_object_array(data).T)
# gh-26429 do not raise user-facing AssertionError
try:
columns = _validate_or_indexify_columns(content, columns)
result = _convert_object_array(content,
dtype=dtype,
coerce_float=coerce_float)
except AssertionError as e:
raise ValueError(e) from e
return result, columns
def _rows_to_cols(self, content: list[list[Scalar]]) -> list[np.ndarray]:
col_len = self.num_original_columns
if self._implicit_index:
col_len += len(self.index_col)
max_len = max(len(row) for row in content)
# Check that there are no rows with too many
# elements in their row (rows with too few
# elements are padded with NaN).
# error: Non-overlapping identity check (left operand type: "List[int]",
# right operand type: "Literal[False]")
if (max_len > col_len and
self.index_col is not False # type: ignore[comparison-overlap]
and self.usecols is None):
footers = self.skipfooter if self.skipfooter else 0
bad_lines = []
iter_content = enumerate(content)
content_len = len(content)
content = []
for (i, l) in iter_content:
actual_len = len(l)
if actual_len > col_len:
if callable(self.on_bad_lines):
new_l = self.on_bad_lines(l)
if new_l is not None:
content.append(new_l)
elif (self.on_bad_lines == self.BadLineHandleMethod.ERROR
or self.on_bad_lines
== self.BadLineHandleMethod.WARN):
row_num = self.pos - (content_len - i + footers)
bad_lines.append((row_num, actual_len))
if self.on_bad_lines == self.BadLineHandleMethod.ERROR:
break
else:
content.append(l)
for row_num, actual_len in bad_lines:
msg = (f"Expected {col_len} fields in line {row_num + 1}, saw "
f"{actual_len}")
if (self.delimiter and len(self.delimiter) > 1
and self.quoting != csv.QUOTE_NONE):
# see gh-13374
reason = ("Error could possibly be due to quotes being "
"ignored when a multi-char delimiter is used.")
msg += ". " + reason
self._alert_malformed(msg, row_num + 1)
# see gh-13320
zipped_content = list(
lib.to_object_array(content, min_width=col_len).T)
if self.usecols:
assert self._col_indices is not None
col_indices = self._col_indices
if self._implicit_index:
zipped_content = [
a for i, a in enumerate(zipped_content)
if (i < len(self.index_col) or i -
len(self.index_col) in col_indices)
]
else:
zipped_content = [
a for i, a in enumerate(zipped_content) if i in col_indices
]
return zipped_content
def _rows_to_cols(self, content):
col_len = self.num_original_columns
if self._implicit_index:
col_len += len(self.index_col)
max_len = max(len(row) for row in content)
# Check that there are no rows with too many
# elements in their row (rows with too few
# elements are padded with NaN).
if max_len > col_len and self.index_col is not False and self.usecols is None:
footers = self.skipfooter if self.skipfooter else 0
bad_lines = []
iter_content = enumerate(content)
content_len = len(content)
content = []
for (i, l) in iter_content:
actual_len = len(l)
if actual_len > col_len:
if self.error_bad_lines or self.warn_bad_lines:
row_num = self.pos - (content_len - i + footers)
bad_lines.append((row_num, actual_len))
if self.error_bad_lines:
break
else:
content.append(l)
for row_num, actual_len in bad_lines:
msg = (
f"Expected {col_len} fields in line {row_num + 1}, saw "
f"{actual_len}"
)
if (
self.delimiter
and len(self.delimiter) > 1
and self.quoting != csv.QUOTE_NONE
):
# see gh-13374
reason = (
"Error could possibly be due to quotes being "
"ignored when a multi-char delimiter is used."
)
msg += ". " + reason
self._alert_malformed(msg, row_num + 1)
# see gh-13320
zipped_content = list(lib.to_object_array(content, min_width=col_len).T)
if self.usecols:
assert self._col_indices is not None
col_indices = self._col_indices
if self._implicit_index:
zipped_content = [
a
for i, a in enumerate(zipped_content)
if (
i < len(self.index_col)
or i - len(self.index_col) in col_indices
)
]
else:
zipped_content = [
a for i, a in enumerate(zipped_content) if i in col_indices
]
return zipped_content
