def __flex_load__(cls, header, data):
ext = MultipleDataExtension._parse(header, data)
wavelength = ext.data["wavelength"]
wavelength = wavelength << ext.header["wavelength_unit"]
flux = ext.data["flux"]
flux = flux << ext.header["flux_unit"]
if "uncertainty" in ext.data:
uncertainty = ext.data["uncertainty"]
uncertainty = uncertainty << ext.header["uncertainty_unit"]
uncertainty = StdDevUncertainty(uncertainty)
else:
uncertainty = None
exceptions = [
"__module__",
"__class__",
"__header__",
"uncertainty_unit",
"wavelength_unit",
"flux_unit",
]
meta = {k: v for k, v in ext.header.items() if k not in exceptions}
meta["spectral_axis"] = wavelength
meta["flux"] = flux
meta["uncertainty"] = uncertainty
return cls(**meta)
def test_json_multi(tmp_fname):
file = FlexFile()
ext = MultipleDataExtension(data={"bla": np.zeros(10)})
file.extensions["img"] = ext
file.to_json(tmp_fname)
del file
f2 = FlexFile.from_json(tmp_fname)
assert f2["img"].data["bla"].size == 10
assert np.all(f2["img"].data["bla"] == 0)
def __flex_save__(self):
header = self.meta
header["wavelength_unit"] = self.wavelength.unit
header["flux_unit"] = self.flux.unit
data = {
"wavelength": self.wavelength.to_value("AA"),
"flux": self.flux.to_value(1),
}
if self.uncertainty is not None:
header["uncertainty_unit"] = self.uncertainty.unit
data["uncertainty"] = self.uncertainty.array
module = self.__class__.__module__
cls = self.__class__.__name__
ext = MultipleDataExtension(header, data, cls=f"{module}.{cls}")
return ext
def _save(self):
data = {}
ext2 = self.abund._save()
header = ext2.header
data["abund"] = ext2.data
header["abund_format"] = header["type"]
del header["type"]
for name in self._names:
value = self[name]
if isinstance(value, (float, int, str)):
header[name] = value
elif isinstance(value, np.ndarray):
data[name] = value
elif value is None or isinstance(value, Abund):
pass
else:
raise ValueError("What is this? %s" % value)
ext = MultipleDataExtension(header, data)
return ext
def _save(self):
data = {}
ext2 = self.abund._save()
header = ext2.header
data["abund"] = ext2.data
header["abund_format"] = header["type"]
del header["type"]
for name in self._names:
value = self[name]
if isinstance(value, np.ndarray):
data[name] = value
elif value is None or isinstance(value, Abund):
pass
else:
# if isinstance(value, (np.floating, np.integer, np.str, float, int, str)):
header[name] = value
ext = MultipleDataExtension(header, data)
return ext
import numpy as np
import pandas as pd
from flex.flex import FlexFile
from flex.extensions.bindata import BinaryDataExtension, MultipleDataExtension
from flex.extensions.tabledata import TableExtension, JSONTableExtension
file = FlexFile()
ext_bin = BinaryDataExtension(data=np.linspace(0, 1, 4 * 3 * 2).reshape((4, 3,
2)))
ext = MultipleDataExtension()
ext.data["data"] = np.arange(12, dtype="i2").reshape((3, 2, 2))
tab = JSONTableExtension()
tab.data = pd.DataFrame(np.arange(20).reshape((10, 2)), columns=["A", "B"])
file.header["bla"] = np.float(3e10)
file.header["blub"] = np.nan
file.header["blrub"] = float("inf")
file.header["blurb"] = np.array([1, 2, 3])
file.header["blurb2"] = [1, 2, 3]
file.header["int"] = 1
file.header["np_int"] = np.int(1)
file.header["str"] = "Hello"
file.header["np_str"] = np.str("World")
# FITS can handle either long header keys, or long entries
# But not both at the same time...
file.header["really_long_entry"] = "bla"
file.header["really_l"] = "bla" * 100
file.header["really_long_text_with_HIERARCH"] = "bla" * 100
def _save(self):
data = {str(i): v for i, v in enumerate(self._values)}
ext = MultipleDataExtension(data=data)
return ext