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algos_groupby_helper.pxi.in
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algos_groupby_helper.pxi.in
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"""
Template for each `dtype` helper function using groupby
WARNING: DO NOT edit .pxi FILE directly, .pxi is generated from .pxi.in
"""
cdef extern from "numpy/npy_math.h":
double NAN "NPY_NAN"
_int64_max = np.iinfo(np.int64).max
#----------------------------------------------------------------------
# group_add, group_prod, group_var, group_mean, group_ohlc
#----------------------------------------------------------------------
{{py:
# name, c_type, dest_type, dest_dtype
dtypes = [('float64', 'float64_t', 'float64_t', 'np.float64'),
('float32', 'float32_t', 'float32_t', 'np.float32')]
def get_dispatch(dtypes):
for name, c_type, dest_type, dest_dtype in dtypes:
dest_type2 = dest_type
dest_type = dest_type.replace('_t', '')
yield name, c_type, dest_type, dest_type2, dest_dtype
}}
{{for name, c_type, dest_type, dest_type2, dest_dtype in get_dispatch(dtypes)}}
@cython.wraparound(False)
@cython.boundscheck(False)
def group_add_{{name}}(ndarray[{{dest_type2}}, ndim=2] out,
ndarray[int64_t] counts,
ndarray[{{c_type}}, ndim=2] values,
ndarray[int64_t] labels):
"""
Only aggregates on axis=0
"""
cdef:
Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
{{dest_type2}} val, count
ndarray[{{dest_type2}}, ndim=2] sumx, nobs
if not len(values) == len(labels):
raise AssertionError("len(index) != len(labels)")
nobs = np.zeros_like(out)
sumx = np.zeros_like(out)
N, K = (<object> values).shape
with nogil:
if K > 1:
for i in range(N):
lab = labels[i]
if lab < 0:
continue
counts[lab] += 1
for j in range(K):
val = values[i, j]
# not nan
if val == val:
nobs[lab, j] += 1
sumx[lab, j] += val
else:
for i in range(N):
lab = labels[i]
if lab < 0:
continue
counts[lab] += 1
val = values[i, 0]
# not nan
if val == val:
nobs[lab, 0] += 1
sumx[lab, 0] += val
for i in range(ncounts):
for j in range(K):
if nobs[i, j] == 0:
out[i, j] = NAN
else:
out[i, j] = sumx[i, j]
@cython.wraparound(False)
@cython.boundscheck(False)
def group_prod_{{name}}(ndarray[{{dest_type2}}, ndim=2] out,
ndarray[int64_t] counts,
ndarray[{{c_type}}, ndim=2] values,
ndarray[int64_t] labels):
"""
Only aggregates on axis=0
"""
cdef:
Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
{{dest_type2}} val, count
ndarray[{{dest_type2}}, ndim=2] prodx, nobs
if not len(values) == len(labels):
raise AssertionError("len(index) != len(labels)")
nobs = np.zeros_like(out)
prodx = np.ones_like(out)
N, K = (<object> values).shape
with nogil:
if K > 1:
for i in range(N):
lab = labels[i]
if lab < 0:
continue
counts[lab] += 1
for j in range(K):
val = values[i, j]
# not nan
if val == val:
nobs[lab, j] += 1
prodx[lab, j] *= val
else:
for i in range(N):
lab = labels[i]
if lab < 0:
continue
counts[lab] += 1
val = values[i, 0]
# not nan
if val == val:
nobs[lab, 0] += 1
prodx[lab, 0] *= val
for i in range(ncounts):
for j in range(K):
if nobs[i, j] == 0:
out[i, j] = NAN
else:
out[i, j] = prodx[i, j]
@cython.wraparound(False)
@cython.boundscheck(False)
@cython.cdivision(True)
def group_var_{{name}}(ndarray[{{dest_type2}}, ndim=2] out,
ndarray[int64_t] counts,
ndarray[{{dest_type2}}, ndim=2] values,
ndarray[int64_t] labels):
cdef:
Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
{{dest_type2}} val, ct, oldmean
ndarray[{{dest_type2}}, ndim=2] nobs, mean
if not len(values) == len(labels):
raise AssertionError("len(index) != len(labels)")
nobs = np.zeros_like(out)
mean = np.zeros_like(out)
N, K = (<object> values).shape
out[:, :] = 0.0
with nogil:
for i in range(N):
lab = labels[i]
if lab < 0:
continue
counts[lab] += 1
for j in range(K):
val = values[i, j]
# not nan
if val == val:
nobs[lab, j] += 1
oldmean = mean[lab, j]
mean[lab, j] += (val - oldmean) / nobs[lab, j]
out[lab, j] += (val - mean[lab, j]) * (val - oldmean)
for i in range(ncounts):
for j in range(K):
ct = nobs[i, j]
if ct < 2:
out[i, j] = NAN
else:
out[i, j] /= (ct - 1)
# add passing bin edges, instead of labels
@cython.wraparound(False)
@cython.boundscheck(False)
def group_mean_{{name}}(ndarray[{{dest_type2}}, ndim=2] out,
ndarray[int64_t] counts,
ndarray[{{dest_type2}}, ndim=2] values,
ndarray[int64_t] labels):
cdef:
Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
{{dest_type2}} val, count
ndarray[{{dest_type2}}, ndim=2] sumx, nobs
if not len(values) == len(labels):
raise AssertionError("len(index) != len(labels)")
nobs = np.zeros_like(out)
sumx = np.zeros_like(out)
N, K = (<object> values).shape
with nogil:
if K > 1:
for i in range(N):
lab = labels[i]
if lab < 0:
continue
counts[lab] += 1
for j in range(K):
val = values[i, j]
# not nan
if val == val:
nobs[lab, j] += 1
sumx[lab, j] += val
else:
for i in range(N):
lab = labels[i]
if lab < 0:
continue
counts[lab] += 1
val = values[i, 0]
# not nan
if val == val:
nobs[lab, 0] += 1
sumx[lab, 0] += val
for i in range(ncounts):
for j in range(K):
count = nobs[i, j]
if nobs[i, j] == 0:
out[i, j] = NAN
else:
out[i, j] = sumx[i, j] / count
@cython.wraparound(False)
@cython.boundscheck(False)
def group_ohlc_{{name}}(ndarray[{{dest_type2}}, ndim=2] out,
ndarray[int64_t] counts,
ndarray[{{dest_type2}}, ndim=2] values,
ndarray[int64_t] labels):
"""
Only aggregates on axis=0
"""
cdef:
Py_ssize_t i, j, N, K, lab
{{dest_type2}} val, count
Py_ssize_t ngroups = len(counts)
if len(labels) == 0:
return
N, K = (<object> values).shape
if out.shape[1] != 4:
raise ValueError('Output array must have 4 columns')
if K > 1:
raise NotImplementedError("Argument 'values' must have only "
"one dimension")
out.fill(np.nan)
with nogil:
for i in range(N):
lab = labels[i]
if lab == -1:
continue
counts[lab] += 1
val = values[i, 0]
if val != val:
continue
if out[lab, 0] != out[lab, 0]:
out[lab, 0] = out[lab, 1] = out[lab, 2] = out[lab, 3] = val
else:
out[lab, 1] = max(out[lab, 1], val)
out[lab, 2] = min(out[lab, 2], val)
out[lab, 3] = val
{{endfor}}
#----------------------------------------------------------------------
# group_nth, group_last
#----------------------------------------------------------------------
{{py:
# name, c_type, dest_type2, nan_val
dtypes = [('float64', 'float64_t', 'float64_t', 'NAN'),
('float32', 'float32_t', 'float32_t', 'NAN'),
('int64', 'int64_t', 'int64_t', 'iNaT')]
def get_dispatch(dtypes):
for name, c_type, dest_type2, nan_val in dtypes:
yield name, c_type, dest_type2, nan_val
}}
{{for name, c_type, dest_type2, nan_val in get_dispatch(dtypes)}}
@cython.wraparound(False)
@cython.boundscheck(False)
def group_last_{{name}}(ndarray[{{dest_type2}}, ndim=2] out,
ndarray[int64_t] counts,
ndarray[{{c_type}}, ndim=2] values,
ndarray[int64_t] labels):
"""
Only aggregates on axis=0
"""
cdef:
Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
{{dest_type2}} val, count
ndarray[{{dest_type2}}, ndim=2] resx
ndarray[int64_t, ndim=2] nobs
if not len(values) == len(labels):
raise AssertionError("len(index) != len(labels)")
nobs = np.zeros((<object> out).shape, dtype=np.int64)
resx = np.empty_like(out)
N, K = (<object> values).shape
with nogil:
for i in range(N):
lab = labels[i]
if lab < 0:
continue
counts[lab] += 1
for j in range(K):
val = values[i, j]
# not nan
if val == val and val != {{nan_val}}:
nobs[lab, j] += 1
resx[lab, j] = val
for i in range(ncounts):
for j in range(K):
if nobs[i, j] == 0:
out[i, j] = {{nan_val}}
else:
out[i, j] = resx[i, j]
@cython.wraparound(False)
@cython.boundscheck(False)
def group_nth_{{name}}(ndarray[{{dest_type2}}, ndim=2] out,
ndarray[int64_t] counts,
ndarray[{{c_type}}, ndim=2] values,
ndarray[int64_t] labels, int64_t rank):
"""
Only aggregates on axis=0
"""
cdef:
Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
{{dest_type2}} val, count
ndarray[{{dest_type2}}, ndim=2] resx
ndarray[int64_t, ndim=2] nobs
if not len(values) == len(labels):
raise AssertionError("len(index) != len(labels)")
nobs = np.zeros((<object> out).shape, dtype=np.int64)
resx = np.empty_like(out)
N, K = (<object> values).shape
with nogil:
for i in range(N):
lab = labels[i]
if lab < 0:
continue
counts[lab] += 1
for j in range(K):
val = values[i, j]
# not nan
if val == val and val != {{nan_val}}:
nobs[lab, j] += 1
if nobs[lab, j] == rank:
resx[lab, j] = val
for i in range(ncounts):
for j in range(K):
if nobs[i, j] == 0:
out[i, j] = {{nan_val}}
else:
out[i, j] = resx[i, j]
{{endfor}}
#----------------------------------------------------------------------
# group_min, group_max
#----------------------------------------------------------------------
{{py:
# name, c_type, dest_type2, nan_val
dtypes = [('float64', 'float64_t', 'NAN', 'np.inf'),
('float32', 'float32_t', 'NAN', 'np.inf'),
('int64', 'int64_t', 'iNaT', '_int64_max')]
def get_dispatch(dtypes):
for name, dest_type2, nan_val, inf_val in dtypes:
yield name, dest_type2, nan_val, inf_val
}}
{{for name, dest_type2, nan_val, inf_val in get_dispatch(dtypes)}}
@cython.wraparound(False)
@cython.boundscheck(False)
def group_max_{{name}}(ndarray[{{dest_type2}}, ndim=2] out,
ndarray[int64_t] counts,
ndarray[{{dest_type2}}, ndim=2] values,
ndarray[int64_t] labels):
"""
Only aggregates on axis=0
"""
cdef:
Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
{{dest_type2}} val, count
ndarray[{{dest_type2}}, ndim=2] maxx, nobs
if not len(values) == len(labels):
raise AssertionError("len(index) != len(labels)")
nobs = np.zeros_like(out)
maxx = np.empty_like(out)
maxx.fill(-{{inf_val}})
N, K = (<object> values).shape
with nogil:
if K > 1:
for i in range(N):
lab = labels[i]
if lab < 0:
continue
counts[lab] += 1
for j in range(K):
val = values[i, j]
# not nan
if val == val and val != {{nan_val}}:
nobs[lab, j] += 1
if val > maxx[lab, j]:
maxx[lab, j] = val
else:
for i in range(N):
lab = labels[i]
if lab < 0:
continue
counts[lab] += 1
val = values[i, 0]
# not nan
if val == val and val != {{nan_val}}:
nobs[lab, 0] += 1
if val > maxx[lab, 0]:
maxx[lab, 0] = val
for i in range(ncounts):
for j in range(K):
if nobs[i, j] == 0:
out[i, j] = {{nan_val}}
else:
out[i, j] = maxx[i, j]
@cython.wraparound(False)
@cython.boundscheck(False)
def group_min_{{name}}(ndarray[{{dest_type2}}, ndim=2] out,
ndarray[int64_t] counts,
ndarray[{{dest_type2}}, ndim=2] values,
ndarray[int64_t] labels):
"""
Only aggregates on axis=0
"""
cdef:
Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
{{dest_type2}} val, count
ndarray[{{dest_type2}}, ndim=2] minx, nobs
if not len(values) == len(labels):
raise AssertionError("len(index) != len(labels)")
nobs = np.zeros_like(out)
minx = np.empty_like(out)
minx.fill({{inf_val}})
N, K = (<object> values).shape
with nogil:
if K > 1:
for i in range(N):
lab = labels[i]
if lab < 0:
continue
counts[lab] += 1
for j in range(K):
val = values[i, j]
# not nan
if val == val and val != {{nan_val}}:
nobs[lab, j] += 1
if val < minx[lab, j]:
minx[lab, j] = val
else:
for i in range(N):
lab = labels[i]
if lab < 0:
continue
counts[lab] += 1
val = values[i, 0]
# not nan
if val == val and val != {{nan_val}}:
nobs[lab, 0] += 1
if val < minx[lab, 0]:
minx[lab, 0] = val
for i in range(ncounts):
for j in range(K):
if nobs[i, j] == 0:
out[i, j] = {{nan_val}}
else:
out[i, j] = minx[i, j]
{{endfor}}
#----------------------------------------------------------------------
# other grouping functions not needing a template
#----------------------------------------------------------------------
def group_median_float64(ndarray[float64_t, ndim=2] out,
ndarray[int64_t] counts,
ndarray[float64_t, ndim=2] values,
ndarray[int64_t] labels):
"""
Only aggregates on axis=0
"""
cdef:
Py_ssize_t i, j, N, K, ngroups, size
ndarray[int64_t] _counts
ndarray data
float64_t* ptr
ngroups = len(counts)
N, K = (<object> values).shape
indexer, _counts = groupsort_indexer(labels, ngroups)
counts[:] = _counts[1:]
data = np.empty((K, N), dtype=np.float64)
ptr = <float64_t*> data.data
take_2d_axis1_float64_float64(values.T, indexer, out=data)
for i in range(K):
# exclude NA group
ptr += _counts[0]
for j in range(ngroups):
size = _counts[j + 1]
out[j, i] = _median_linear(ptr, size)
ptr += size
@cython.boundscheck(False)
@cython.wraparound(False)
def group_cumprod_float64(float64_t[:, :] out,
float64_t[:, :] values,
int64_t[:] labels,
float64_t[:, :] accum):
"""
Only transforms on axis=0
"""
cdef:
Py_ssize_t i, j, N, K, size
float64_t val
int64_t lab
N, K = (<object> values).shape
accum = np.ones_like(accum)
with nogil:
for i in range(N):
lab = labels[i]
if lab < 0:
continue
for j in range(K):
val = values[i, j]
if val == val:
accum[lab, j] *= val
out[i, j] = accum[lab, j]
@cython.boundscheck(False)
@cython.wraparound(False)
def group_cumsum(numeric[:, :] out,
numeric[:, :] values,
int64_t[:] labels,
numeric[:, :] accum):
"""
Only transforms on axis=0
"""
cdef:
Py_ssize_t i, j, N, K, size
numeric val
int64_t lab
N, K = (<object> values).shape
accum = np.zeros_like(accum)
with nogil:
for i in range(N):
lab = labels[i]
if lab < 0:
continue
for j in range(K):
val = values[i, j]
if val == val:
accum[lab, j] += val
out[i, j] = accum[lab, j]
@cython.boundscheck(False)
@cython.wraparound(False)
def group_shift_indexer(int64_t[:] out, int64_t[:] labels,
int ngroups, int periods):
cdef:
Py_ssize_t N, i, j, ii
int offset, sign
int64_t lab, idxer, idxer_slot
int64_t[:] label_seen = np.zeros(ngroups, dtype=np.int64)
int64_t[:, :] label_indexer
N, = (<object> labels).shape
if periods < 0:
periods = -periods
offset = N - 1
sign = -1
elif periods > 0:
offset = 0
sign = 1
if periods == 0:
with nogil:
for i in range(N):
out[i] = i
else:
# array of each previous indexer seen
label_indexer = np.zeros((ngroups, periods), dtype=np.int64)
with nogil:
for i in range(N):
## reverse iterator if shifting backwards
ii = offset + sign * i
lab = labels[ii]
# Skip null keys
if lab == -1:
out[ii] = -1
continue
label_seen[lab] += 1
idxer_slot = label_seen[lab] % periods
idxer = label_indexer[lab, idxer_slot]
if label_seen[lab] > periods:
out[ii] = idxer
else:
out[ii] = -1
label_indexer[lab, idxer_slot] = ii