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Copy DataFrame-derived arrays so SuperLink works under pandas 3 (Copy-on-Write) #75

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Copy DataFrame-derived arrays so SuperLink works under pandas 3 (Copy-on-Write) #75

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b948551
Make the storage-type masks writable copies for pandas 3 (Copy-on-Write)
stoiver Sep 18, 2026
585434f
Copy the remaining DataFrame-derived arrays that reach numba kernels
stoiver Sep 18, 2026
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11 changes: 7 additions & 4 deletions pipedream_solver/nsuperlink.py
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Original file line number Diff line number Diff line change
Expand Up @@ -428,8 +428,11 @@ def configure_storages(self):
_storage_js = np.array([])
_storage_codes = np.array([])
# Separate storages into functional and tabular
_functional = (_storage_type.str.lower() == 'functional').values
_tabular = (_storage_type.str.lower() == 'tabular').values
# np.array(...) copies: under pandas >= 3 (Copy-on-Write) .values is a
# read-only view, which the eagerly-compiled numba kernels (boolean[:])
# refuse to accept. A writable copy is accepted under pandas 2 and 3.
_functional = np.array((_storage_type.str.lower() == 'functional').values)
_tabular = np.array((_storage_type.str.lower() == 'tabular').values)
# All entries must either be function or tabular
assert (_tabular.sum() + _functional.sum()) == _storage_type.shape[0]
# Configure tabular storages
Expand Down Expand Up @@ -464,8 +467,8 @@ def configure_storages(self):
_storage_Vs = np.concatenate([_storage_Vs[i] for i in order])
_storage_inds = np.asarray(_storage_inds)[order]
_storage_lens = np.asarray(_storage_lens)[order]
_storage_js = sj_to_storage_ind.index.values
_storage_codes = sj_to_storage_ind.values
_storage_js = np.array(sj_to_storage_ind.index.values)
_storage_codes = np.array(sj_to_storage_ind.values)
# Export instance variables
self._storage_indices = _storage_indices
self._storage_factory = _storage_factory
Expand Down
29 changes: 16 additions & 13 deletions pipedream_solver/superlink.py
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Original file line number Diff line number Diff line change
Expand Up @@ -457,7 +457,7 @@ def __init__(self, superlinks, superjunctions,
assert _Sf_method.isin(Sf_map).all()
except:
raise ValueError('Friction method must be one of cm, hw, dw.')
self._Sf_method_ik = _Sf_method.map(Sf_map).astype(np.int64).values
self._Sf_method_ik = np.array(_Sf_method.map(Sf_map).astype(np.int64).values)
if 'roughness' in links:
self._n_ik = links['roughness'].values.astype(np.float64)
else:
Expand Down Expand Up @@ -1050,14 +1050,14 @@ def _configure_internals_variable(self, internal_links=4, mobile_elements=True):
x = np.zeros(NJ)
z = np.zeros(NJ)
dx = np.zeros(NL)
h = junctions['h_0'].values
Q = links['Q_0'].values
h = np.array(junctions['h_0'].values)
Q = np.array(links['Q_0'].values)
xx = x.reshape(-1, njunctions)
zz = z.reshape(-1, njunctions)
dxdx = dx.reshape(-1, nlinks)
hh = h.reshape(-1, njunctions)
QQ = Q.reshape(-1, nlinks)
dx_j = superlinks['dx'].values
dx_j = np.array(superlinks['dx'].values)
_z_inv_uk = self._z_inv_uk
_z_inv_dk = self._z_inv_dk
_dx_uk = self._dx_uk
Expand Down Expand Up @@ -1630,7 +1630,7 @@ def configure_hydraulic_geometry(self):
_geom_indices = pd.Series(_regular_shapes.index,
index=_regular_shapes.str.lower().values)
for geom in _unique_geom:
_ik_g = _geom_indices.loc[[geom]].values
_ik_g = np.array(_geom_indices.loc[[geom]].values)
_geom_factory[geom] = _ik_g
_uk_geom_factory[geom] = _ik_g[_link_start[_ik_g]]
_dk_geom_factory[geom] = _ik_g[_link_end[_ik_g]]
Expand All @@ -1656,7 +1656,7 @@ def configure_hydraulic_geometry(self):
_unique_geom_o = set(_shape_o.str.lower().unique())
_geom_indices_o = pd.Series(_shape_o.index, index=_shape_o.str.lower().values)
for geom in _unique_geom_o:
_o_g = _geom_indices_o.loc[[geom]].values
_o_g = np.array(_geom_indices_o.loc[[geom]].values)
_geom_factory_o[geom] = _o_g
_geom_codes_o = np.zeros(n_o, dtype=np.int64)
for geom, indices in _geom_factory_o.items():
Expand Down Expand Up @@ -1702,8 +1702,11 @@ def configure_storages(self):
_storage_factory = {}
_storage_indices = None
# Separate storages into functional and tabular
_functional = (_storage_type.str.lower() == 'functional').values
_tabular = (_storage_type.str.lower() == 'tabular').values
# np.array(...) copies: under pandas >= 3 (Copy-on-Write) .values is a
# read-only view, which the eagerly-compiled numba kernels (boolean[:])
# refuse to accept. A writable copy is accepted under pandas 2 and 3.
_functional = np.array((_storage_type.str.lower() == 'functional').values)
_tabular = np.array((_storage_type.str.lower() == 'tabular').values)
# All entries must either be function or tabular
assert (_tabular.sum() + _functional.sum()) == _storage_type.shape[0]
# Configure tabular storages
Expand Down Expand Up @@ -1762,7 +1765,7 @@ def link_hydraulic_geometry(self):
# Compute hydraulic geometry for irregular geometries
if _has_irregular:
for transect_name, generator in _transect_factory.items():
_ik_g = _transect_indices.loc[[transect_name]].values
_ik_g = np.array(_transect_indices.loc[[transect_name]].values)
_Ik_g = _Ik[_ik_g]
_Ip1k_g = _Ip1k[_ik_g]
_h_Ik_g = _h_Ik[_Ik_g]
Expand Down Expand Up @@ -1825,7 +1828,7 @@ def upstream_hydraulic_geometry(self, area='avg'):
# Compute hydraulic geometry for irregular geometries
if _uk_has_irregular:
for transect_name, generator in _transect_factory.items():
_ik_g = _uk_transect_indices.loc[[transect_name]].values
_ik_g = np.array(_uk_transect_indices.loc[[transect_name]].values)
_ki_g = _ki[_ik_g]
_Ik_g = _Ik[_ik_g]
_h_Ik_g = _h_Ik[_Ik_g]
Expand Down Expand Up @@ -1884,7 +1887,7 @@ def downstream_hydraulic_geometry(self, area='avg'):
# Compute hydraulic geometry for irregular geometries
if _dk_has_irregular:
for transect_name, generator in _transect_factory.items():
_ik_g = _dk_transect_indices.loc[[transect_name]].values
_ik_g = np.array(_dk_transect_indices.loc[[transect_name]].values)
_ki_g = _ki[_ik_g]
_Ip1k_g = _Ip1k[_ik_g]
_h_Ip1k_g = _h_Ik[_Ip1k_g]
Expand Down Expand Up @@ -1957,7 +1960,7 @@ def compute_storage_areas(self):
_storage_c[_functional])
if _tabular.any():
for storage_name, generator in _storage_factory.items():
_j_g = _storage_indices.loc[[storage_name]].values
_j_g = np.array(_storage_indices.loc[[storage_name]].values)
_A_sj[_j_g] = generator.A_sj(_h_j[_j_g])
# Export instance variables
self._A_sj = _A_sj
Expand Down Expand Up @@ -1988,7 +1991,7 @@ def compute_storage_volumes(self):
_storage_c[_functional])
if _tabular.any():
for storage_name, generator in _storage_factory.items():
_j_g = _storage_indices.loc[[storage_name]].values
_j_g = np.array(_storage_indices.loc[[storage_name]].values)
_V_sj[_j_g] = generator.V_sj(_h_j[_j_g])
# Export instance variables
self._V_sj = _V_sj
Expand Down