More update for numpy2.0 casting changes (#439)

Author: samaloney

stixcore/products/common.py

@@ -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)

stixcore/products/level0/quicklookL0.py

@@ -15,7 +15,6 @@
     _get_energy_bins,
     _get_pixel_mask,
     _get_sub_spectrum_mask,
-    get_min_uint,
     rebin_proportional,
     unscale_triggers,
 )
@@ -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))
         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)
 
@@ -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)
 
@@ -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]
@@ -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))
 
@@ -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)
@@ -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

stixcore/products/level0/scienceL0.py

@@ -11,7 +11,6 @@
     _get_compression_scheme,
     _get_detector_mask,
     _get_pixel_mask,
-    get_min_uint,
     unscale_triggers,
 )
 from stixcore.products.product import (
@@ -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
 
@@ -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)
 
@@ -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]
 
@@ -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)
@@ -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]
@@ -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)
@@ -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)
@@ -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")

stixcore/products/product.py

@@ -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__)
@@ -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
 
@@ -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"]
 
@@ -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"]
@@ -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,