def __init__(self, barDeg=60, spaceDeg=60, rotateDegHz=0) -> None:
if (barDeg < 0 or spaceDeg < 0):
warnings.war("bar size or space is negative")
if (360 % (barDeg + spaceDeg) != 0):
warnings.warn(
f"Spatial pattern is not seamless with bar {barDeg}° and space {spaceDeg}"
)
if (rotateDegHz is None):
warnings.warn("temporal components needs to be set.")
self.barDeg = barDeg
self.spaceDeg = spaceDeg
self.rotateDegHz = rotateDegHz
def k_nearest(data, predict, k=3):
if len(data) >= k:
warnings.war('k value less than something')
distances = []
for group in data:
for features in data[group]:
euclidean_distance = np.linalg.norm(
np.array(features) - np.array(predict))
distances.append([euclidean_distance, group])
votes = [i[1] for i in sorted(distances)[:k]]
vote_result = Counter(votes).most_common(1)[0][0]
confidence = Counter(votes).most_common(1)[0][1] / k
return vote_result, confidence
def ez_bolus_indices(
df: pd.DataFrame,
kind: Optional[str] = None) -> pd.core.indexes.datetimes.DatetimeIndex:
""""""
_kind_dict = {
"meal": ["BWZ Carb Input (grams)"],
"correction": ["BWZ Correction Estimate (U)"],
"both": ["BWZ Correction Estimate (U)", "BWZ Carb Input (grams)"],
}
if kind not in _kind_dict.keys():
warnings.warn(f"Invalid kind, use of {list(_kind_dict.keys())}.")
warnings.war("Defaulted to 'both'")
kind = "both"
columns = _kind_dict[kind]
_nonull = partial(nonull_indices, df)
indices_ls = list(map(_nonull, columns))
return reduce(lambda x, y: x.union(y), indices_ls)
def bolus_only(
df: pd.DataFrame,
column: str = "Sensor Glucose (mg/dL)",
time_window: Dict[str, int] = {
"hours": 2,
"minutes": 30
},
kind: Optional[str] = None,
) -> pd.DataFrame:
"""
kind: 'meal'
'correction'
'both'
defaults to 'both'
"""
bolus = df.copy()
bolus["save"] = False
_kind_dict = {
"meal": ["BWZ Carb Input (grams)"],
"correction": ["BWZ Correction Estimate (U)"],
"both": ["BWZ Correction Estimate (U)", "BWZ Carb Input (grams)"],
}
if kind not in _kind_dict.keys():
warnings.warn(f"Invalid kind, use of {list(_kind_dict.keys())}.")
warnings.war("Defaulted to 'both'")
kind = "both"
_td = dt.timedelta(**time_window)
for uid in bolus_indices_explicit(bolus, columns=_kind_dict[kind]):
real = uid + _td
closest = df.index[df.index.searchsorted(real) -
1] # Otherwise it goes out of bounds !
bolus.loc[uid:closest, "save"] = True
bolus.loc[bolus.save == False, "Sensor Glucose (mg/dL)"] = np.nan
return bolus
def from_cnv(fname):
"""
DataFrame constructor to open Seabird CTD CNV-ASCII format.
Examples
--------
>>> from pathlib import Path
>>> import ctd
>>> data_path = Path(__file__).parents[1].joinpath("tests", "data")
>>> cast = ctd.from_cnv(data_path.joinpath('CTD_big.cnv.bz2'))
>>> downcast, upcast = cast.split()
>>> ax = downcast['t090C'].plot_cast()
"""
f = _read_file(fname)
metadata = _parse_seabird(f.readlines(), ftype="cnv")
f.seek(0)
df = pd.read_fwf(
f,
header=None,
index_col=None,
names=metadata["names"],
skiprows=metadata["skiprows"],
delim_whitespace=True,
widths=[11] * len(metadata["names"]),
)
f.close()
prkeys = ["prM ", "prE", "prDM", "pr50M", "pr50M1", "prSM", "prdM", "pr", "depSM"]
prkey = [key for key in prkeys if key in df.columns]
if len(prkey) != 1:
raise ValueError(f"Expected one pressure/depth column, got {prkey}.")
df.set_index(prkey, drop=True, inplace=True)
df.index.name = "Pressure [dbar]"
if prkey == "depSM":
lat = metadata.get("lat", None)
if lat is not None:
df.index = gsw.p_from_z(
df.index,
lat,
geo_strf_dyn_height=0,
sea_surface_geopotential=0,
)
else:
warnings.war(
f"Missing latitude information. Cannot compute pressure! Your index is {prkey}, "
"please compute pressure manually with `gsw.p_from_z` and overwrite your index.",
)
df.index.name = prkey
name = _basename(fname)[1]
dtypes = {"bpos": int, "pumps": bool, "flag": bool}
for column in df.columns:
if column in dtypes:
df[column] = df[column].astype(dtypes[column])
else:
try:
df[column] = df[column].astype(float)
except ValueError:
warnings.warn("Could not convert %s to float." % column)
metadata["name"] = str(name)
setattr(df, "_metadata", metadata)
return df
