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Merged
nicHoch merged 1 commit into from
Nov 15, 2025
+23 −38
Merged

More update for numpy 2.0 casting changes in product __add__ #439

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15 changes: 0 additions & 15 deletions stixcore/products/common.py
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Original file line number Diff line number Diff line change
Expand Up @@ -242,21 +242,6 @@ def _get_energies_from_mask(date, mask=None):
return low, high


def get_min_uint(values):
"""
Find the smallest unsigned int that can represent max value.
"""
max_value = np.array(values).max()
if max_value < 256: # 2**8
return np.uint8
elif max_value < 65536: # 2**16
return np.uint16
elif max_value < 4294967296: # 2**32
return np.uint32
elif max_value < 18446744073709551616: # 2**64
return np.uint64


def rebin_proportional(y1, x1, x2):
x1 = np.asarray(x1)
y1 = np.asarray(y1)
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19 changes: 9 additions & 10 deletions stixcore/products/level0/quicklookL0.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,6 @@
_get_energy_bins,
_get_pixel_mask,
_get_sub_spectrum_mask,
get_min_uint,
rebin_proportional,
unscale_triggers,
)
Expand Down Expand Up @@ -203,12 +202,12 @@ def from_levelb(cls, levelb, parent="", keep_parse_tree=True):
data["time"] = time
data["timedel"] = duration
data.add_meta(name="timedel", nix="NIX00405", packets=packets)
data["triggers"] = triggers.astype(get_min_uint(triggers))
data["triggers"] = triggers.astype(np.min_scalar_type(triggers))
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@nicHoch nicHoch Nov 14, 2025

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nice that this is common numpy now

data.add_meta(name="triggers", nix="NIX00274", packets=packets)
data["triggers_comp_err"] = np.float32(np.sqrt(triggers_var))
data["rcr"] = np.hstack(packets.get_value("NIX00276")).flatten().astype(np.ubyte)
data.add_meta(name="rcr", nix="NIX00276", packets=packets)
data["counts"] = (counts.T * u.ct).astype(get_min_uint(counts))
data["counts"] = (counts.T * u.ct).astype(np.min_scalar_type(counts))
data.add_meta(name="counts", nix="NIX00272", packets=packets)
data["counts_comp_err"] = np.float32(np.sqrt(counts_var).T * u.ct)

Expand Down Expand Up @@ -316,10 +315,10 @@ def from_levelb(cls, levelb, parent="", keep_parse_tree=True):
data["time"] = time
data["timedel"] = duration
data.add_meta(name="timedel", nix="NIX00405", packets=packets)
data["triggers"] = triggers.astype(get_min_uint(triggers))
data["triggers"] = triggers.astype(np.min_scalar_type(triggers))
data.add_meta(name="triggers", nix="NIX00274", packets=packets)
data["triggers_comp_err"] = np.float32(np.sqrt(triggers_var))
data["counts"] = (counts.T * u.ct).astype(get_min_uint(counts))
data["counts"] = (counts.T * u.ct).astype(np.min_scalar_type(counts))
data.add_meta(name="counts", nix="NIX00278", packets=packets)
data["counts_comp_err"] = np.float32(np.sqrt(counts_var).T * u.ct)

Expand Down Expand Up @@ -438,10 +437,10 @@ def from_levelb(cls, levelb, parent="", keep_parse_tree=True):
data.add_meta(name="timedel", nix="NIX00405", packets=packets)
data["detector_index"] = detector_index.reshape(-1, 32).astype(np.ubyte)
data.add_meta(name="detector_index", nix="NIX00100", packets=packets)
data["spectra"] = (counts.reshape(-1, 32, num_energies) * u.ct).astype(get_min_uint(counts))
data["spectra"] = (counts.reshape(-1, 32, num_energies) * u.ct).astype(np.min_scalar_type(counts))
data["spectra"].meta = {"NIXS": "NIX00452", "PCF_CURTX": packets.get("NIX00452")[0].idb_info.PCF_CURTX}
data["spectra_comp_err"] = np.float32(np.sqrt(counts_var.reshape(-1, 32, num_energies)))
data["triggers"] = triggers.reshape(-1, num_energies).astype(get_min_uint(triggers))
data["triggers"] = triggers.reshape(-1, num_energies).astype(np.min_scalar_type(triggers))
data.add_meta(name="triggers", nix="NIX00484", packets=packets)
data["triggers_comp_err"] = np.float32(np.sqrt(triggers_var.reshape(-1, num_energies)))
data["num_integrations"] = num_integrations.reshape(-1, num_energies).astype(np.ubyte)[:, 0]
Expand Down Expand Up @@ -556,7 +555,7 @@ def from_levelb(cls, levelb, parent="", keep_parse_tree=True):
data["timedel"] = duration
data.add_meta(name="timedel", nix="NIX00405", packets=packets)
data["control_index"] = control_indices
data["variance"] = variance.astype(get_min_uint(variance))
data["variance"] = variance.astype(np.min_scalar_type(variance))
data.add_meta(name="variance", nix="NIX00281", packets=packets)
data["variance_comp_err"] = np.float32(np.sqrt(variance_var))

Expand Down Expand Up @@ -801,7 +800,7 @@ def from_levelb(cls, levelb, parent="", keep_parse_tree=True):
data["timedel"] = duration[unique_time_indices]
data.add_meta(name="timedel", nix="NIX00122", packets=packets)

data["counts"] = (full_counts * u.ct).astype(get_min_uint(full_counts))
data["counts"] = (full_counts * u.ct).astype(np.min_scalar_type(full_counts))
data.add_meta(name="counts", nix="NIX00158", packets=packets)
data["counts_comp_err"] = (np.sqrt(full_counts_var) * u.ct).astype(np.float32)
data["control_index"] = np.arange(len(control)).astype(np.uint16)
Expand Down Expand Up @@ -849,7 +848,7 @@ def from_levelb(cls, levelb, parent="", keep_parse_tree=True):
control = Control()
control["scet_coarse"] = packets.get("scet_coarse")
control["scet_fine"] = packets.get("scet_fine")
control["index"] = np.arange(len(control)).astype(get_min_uint(len(control)))
control["index"] = np.arange(len(control)).astype(np.min_scalar_type(len(control)))

# When the packets are parsed empty packets are dropped but in LB we don't parse so this
# is not known need to compare control and levelb.control and only use matching rows
Expand Down
17 changes: 8 additions & 9 deletions stixcore/products/level0/scienceL0.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,6 @@
_get_compression_scheme,
_get_detector_mask,
_get_pixel_mask,
get_min_uint,
unscale_triggers,
)
from stixcore.products.product import (
Expand Down Expand Up @@ -199,7 +198,7 @@ def from_levelb(cls, levelb, *, parent="", keep_parse_tree=True):

control["index"] = np.ubyte(0)
packet_ids = levelb.control["packet"].reshape(1, -1)
control["packet"] = packet_ids.astype(get_min_uint(packet_ids))
control["packet"] = packet_ids.astype(np.min_scalar_type(packet_ids))
control["raw_file"] = np.unique(levelb.control["raw_file"]).reshape(1, -1)
control["parent"] = parent

Expand Down Expand Up @@ -245,7 +244,7 @@ def from_levelb(cls, levelb, parent="", keep_parse_tree=True):
data.add_data("pixel_masks", _get_pixel_mask(packets, "NIXD0407"))
data.add_data("detector_masks", _get_detector_mask(packets))
triggers = np.array([packets.get_value(f"NIX00{i}") for i in range(408, 424)]).T
data["triggers"] = triggers.astype(get_min_uint(triggers))
data["triggers"] = triggers.astype(np.min_scalar_type(triggers))
data["triggers"].meta = {"NIXS": [f"NIX00{i}" for i in range(408, 424)]}
data.add_basic(name="num_samples", nix="NIX00406", packets=packets, dtype=np.uint16)

Expand Down Expand Up @@ -302,7 +301,7 @@ def from_levelb(cls, levelb, parent="", keep_parse_tree=True):
data = data[sub_index]
data["time"] = control["time_stamp"][0] + data["start_time"] + data["integration_time"] / 2
data["timedel"] = SCETimeDelta(data["integration_time"])
data["counts"] = (counts * u.ct).astype(get_min_uint(counts))
data["counts"] = (counts * u.ct).astype(np.min_scalar_type(counts))
# data.add_meta(name='counts', nix='NIX00065', packets=packets)
data["control_index"] = control["index"][0]

Expand Down Expand Up @@ -416,7 +415,7 @@ def from_levelb(cls, levelb, parent="", keep_parse_tree=True):
factor=factor,
)

data["triggers"] = triggers.T.astype(get_min_uint(triggers))
data["triggers"] = triggers.T.astype(np.min_scalar_type(triggers))
data["triggers"].meta = {"NIXS": [f"NIX00{i}" for i in range(242, 258)]}
data["triggers_comp_err"] = np.float32(np.sqrt(triggers_var).T)
# data.add_basic(name='num_energy_groups', nix='NIX00258', packets=packets, dtype=np.ubyte)
Expand Down Expand Up @@ -565,7 +564,7 @@ def from_levelb(cls, levelb, parent="", keep_parse_tree=True):

data["time"] = control["time_stamp"][0] + data["delta_time"] + data["integration_time"] / 2
data["timedel"] = data["integration_time"]
data["counts"] = (counts * u.ct).astype(get_min_uint(counts))
data["counts"] = (counts * u.ct).astype(np.min_scalar_type(counts))
data.add_meta(name="counts", nix="NIX00260", packets=packets)
data["counts_comp_err"] = np.float32(counts_var * u.ct)
data["control_index"] = control["index"][0]
Expand Down Expand Up @@ -875,7 +874,7 @@ def from_levelb(cls, levelb, parent="", keep_parse_tree=True):
data["timedel"] = deltas
data["timedel"].meta = {"NIXS": ["NIX00441", "NIX00269"]}

data["triggers"] = triggers.astype(get_min_uint(triggers))
data["triggers"] = triggers.astype(np.min_scalar_type(triggers))
data.add_meta(name="triggers", nix="NIX00267", packets=packets)
data["triggers_comp_err"] = np.float32(np.sqrt(triggers_var))
data.add_meta(name="triggers_comp_err", nix="NIX00267", packets=packets)
Expand All @@ -885,7 +884,7 @@ def from_levelb(cls, levelb, parent="", keep_parse_tree=True):
data["pixel_masks"] = pixel_masks
data.add_meta(name="pixel_masks", nix="NIXD0407", packets=packets)

data["counts"] = (counts * u.ct).astype(get_min_uint(counts))
data["counts"] = (counts * u.ct).astype(np.min_scalar_type(counts))
data.add_meta(name="counts", nix="NIX00268", packets=packets)
data["counts_comp_err"] = np.float32(np.sqrt(counts_var) * u.ct)
data["control_index"] = np.ubyte(0)
Expand Down Expand Up @@ -957,7 +956,7 @@ def from_levelb(cls, levelb, parent="", keep_parse_tree=True):
control["packet"] = levelb.control["packet"].reshape(1, -1)
control["parent"] = parent

control["index"] = np.arange(len(control)).astype(get_min_uint(len(control)))
control["index"] = np.arange(len(control)).astype(np.min_scalar_type(len(control)))

delta_time = ((control["summing_value"] * control["averaging_value"]) / 1000.0) * u.s
samples = packets.get_value("NIX00089")
Expand Down
10 changes: 6 additions & 4 deletions stixcore/products/product.py
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Original file line number Diff line number Diff line change
Expand Up @@ -38,7 +38,7 @@

from collections import defaultdict

from stixcore.products.common import _get_energies_from_mask, get_min_uint
from stixcore.products.common import _get_energies_from_mask
from stixcore.util.logging import get_logger

logger = get_logger(__name__)
Expand Down Expand Up @@ -420,7 +420,7 @@ def from_packets(cls, packets, NIX00405_offset=0):
control["integration_time"] = np.zeros_like(control["scet_coarse"], float) * u.s

# control = unique(control)
control["index"] = np.arange(len(control)).astype(get_min_uint(len(control)))
control["index"] = np.arange(len(control)).astype(np.min_scalar_type(len(control)))

return control

Expand Down Expand Up @@ -669,6 +669,8 @@ def __add__(self, other):
# update the control index in data to a new unique sequence
newids = dict()

data["control_index"] = data["control_index"].astype(np.min_scalar_type(len(control)))

for row in data:
oid = row["old_index"]

Expand Down Expand Up @@ -1027,7 +1029,7 @@ def from_levelb(cls, levelb, parent, keep_parse_tree=True):
control["scet_coarse"] = packets.get("scet_coarse")
control["scet_fine"] = packets.get("scet_fine")
control["integration_time"] = 0
control["index"] = np.arange(len(control)).astype(get_min_uint(len(control)))
control["index"] = np.arange(len(control)).astype(np.min_scalar_type(len(control)))

control["raw_file"] = levelb.control["raw_file"]
control["packet"] = levelb.control["packet"]
Expand All @@ -1049,7 +1051,7 @@ def from_levelb(cls, levelb, parent, keep_parse_tree=True):
name = param.idb_info.get_product_attribute_name()
data.add_basic(name=name, nix=nix, attr="value", packets=packets, reshape=reshape)

data["control_index"] = np.arange(len(control)).astype(get_min_uint(len(control)))
data["control_index"] = np.arange(len(control)).astype(np.min_scalar_type(len(control)))

return cls(
service_type=packets.service_type,
Expand Down
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