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fix: warnings on NeaReaderGSF
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@brnovasco
brnovasco committed Apr 4, 2024
1 parent 806bae3 commit 9adad2f
Showing 1 changed file with 90 additions and 59 deletions.
149 changes: 90 additions & 59 deletions orangecontrib/spectroscopy/io/neaspec.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,7 @@
class NeaReader(FileFormat, SpectralFileFormat):

EXTENSIONS = (".nea", ".txt")
DESCRIPTION = 'NeaSPEC'
DESCRIPTION = "NeaSPEC"

def read_v1(self):

Expand All @@ -31,10 +31,14 @@ def read_v1(self):
f.seek(0)
next(f)
metacols = np.arange(0, 4)
meta = np.loadtxt(f,
dtype={'names': ('row', 'column', 'run', 'channel'),
'formats': (int, int, int, "S10")},
usecols=metacols)
meta = np.loadtxt(
f,
dtype={
"names": ("row", "column", "run", "channel"),
"formats": (int, int, int, "S10"),
},
usecols=metacols,
)

# ASSUMTION: runs start with 0
runs = np.unique(meta["run"])
Expand All @@ -55,7 +59,7 @@ def channel_type(a):
for a in channels:
if channel_type(a) in ("OA", "OP"):
maxn = max(maxn, int(a[1:-1]))
numharmonics = maxn+1
numharmonics = maxn + 1

rowcols = np.vstack((meta["row"], meta["column"])).T
uniquerc = set(map(tuple, rowcols))
Expand All @@ -66,10 +70,13 @@ def channel_type(a):

for i, (row, col, run, chan) in enumerate(meta):
if (row, col) not in di:
di[(row, col)] = \
{"M": np.zeros((len(runs), len(datacols))) * np.nan,
"OA": np.zeros((numharmonics, len(runs), len(datacols))) * np.nan,
"OP": np.zeros((numharmonics, len(runs), len(datacols))) * np.nan}
di[(row, col)] = {
"M": np.zeros((len(runs), len(datacols))) * np.nan,
"OA": np.zeros((numharmonics, len(runs), len(datacols)))
* np.nan,
"OP": np.zeros((numharmonics, len(runs), len(datacols)))
* np.nan,
}
if channel_type(chan) == "M":
di[(row, col)][channel_type(chan)][run] = data[i]
if min_intp is None: # we need the limits of common X for all
Expand Down Expand Up @@ -107,13 +114,18 @@ def channel_type(a):

final_data = np.vstack(final_data)

metas = [Orange.data.ContinuousVariable.make("row"),
Orange.data.ContinuousVariable.make("column"),
Orange.data.StringVariable.make("channel")]
metas = [
Orange.data.ContinuousVariable.make("row"),
Orange.data.ContinuousVariable.make("column"),
Orange.data.StringVariable.make("channel"),
]

domain = Orange.data.Domain([], None, metas=metas)
meta_data = Table.from_numpy(domain, X=np.zeros((len(final_data), 0)),
metas=np.asarray(final_metas, dtype=object))
meta_data = Table.from_numpy(
domain,
X=np.zeros((len(final_data), 0)),
metas=np.asarray(final_metas, dtype=object),
)
return X, final_data, meta_data

def read_v2(self):
Expand All @@ -124,21 +136,21 @@ def read_v2(self):
while f:
line = f.readline()
count = count + 1
if line[0] != '#':
if line[0] != "#":
break

file = np.loadtxt(f) # Slower part

# Find the Wavenumber column
line = line.strip().split('\t')
line = line.strip().split("\t")

for i, e in enumerate(line):
if e == 'Wavenumber':
if e == "Wavenumber":
index = i
break

# Channel need to have exactly 3 letters
Channel = line[index + 1:]
Channel = line[index + 1 :]
Channel = np.array(Channel)
# Extract other data #
Max_row = int(file[:, 0].max() + 1)
Expand All @@ -148,17 +160,19 @@ def read_v2(self):
N_cols = Max_omega

# Transform Actual Data
M = np.full((int(N_rows), int(N_cols)), np.nan, dtype='float')
M = np.full((int(N_rows), int(N_cols)), np.nan, dtype="float")

for j in range(int(Max_row * Max_col)):
row_value = file[j * (Max_omega):(j + 1) * (Max_omega), 0]
row_value = file[j * (Max_omega) : (j + 1) * (Max_omega), 0]
assert np.all(row_value == row_value[0])
col_value = file[j * (Max_omega):(j + 1) * (Max_omega), 1]
col_value = file[j * (Max_omega) : (j + 1) * (Max_omega), 1]
assert np.all(col_value == col_value[0])
for k in range(Channel.size):
M[k + Channel.size * j, :] = file[j * (Max_omega):(j + 1) * (Max_omega), k + 4]
M[k + Channel.size * j, :] = file[
j * (Max_omega) : (j + 1) * (Max_omega), k + 4
]

Meta_data = np.zeros((int(N_rows), 3), dtype='object')
Meta_data = np.zeros((int(N_rows), 3), dtype="object")

alpha = 0
beta = 0
Expand All @@ -168,25 +182,26 @@ def read_v2(self):
if beta == Max_row:
beta = 0
alpha = alpha + 1
Meta_data[i:i + Ch_n, 2] = Channel
Meta_data[i:i + Ch_n, 1] = alpha
Meta_data[i:i + Ch_n, 0] = beta
Meta_data[i : i + Ch_n, 2] = Channel
Meta_data[i : i + Ch_n, 1] = alpha
Meta_data[i : i + Ch_n, 0] = beta
beta = beta + 1

waveN = file[0:int(Max_omega), 3]
metas = [Orange.data.ContinuousVariable.make("row"),
Orange.data.ContinuousVariable.make("column"),
Orange.data.StringVariable.make("channel")]
waveN = file[0 : int(Max_omega), 3]
metas = [
Orange.data.ContinuousVariable.make("row"),
Orange.data.ContinuousVariable.make("column"),
Orange.data.StringVariable.make("channel"),
]

domain = Orange.data.Domain([], None, metas=metas)
meta_data = Table.from_numpy(domain, X=np.zeros((len(M), 0)),
metas=Meta_data)
meta_data = Table.from_numpy(domain, X=np.zeros((len(M), 0)), metas=Meta_data)
return waveN, M, meta_data

def read_spectra(self):
version = 1
with open(self.filename, "rt", encoding="utf8") as f:
if f.read(2) == '# ':
if f.read(2) == "# ":
version = 2
if version == 1:
return self.read_v1()
Expand All @@ -197,63 +212,79 @@ def read_spectra(self):
class NeaReaderGSF(FileFormat, SpectralFileFormat):

EXTENSIONS = (".gsf",)
DESCRIPTION = 'NeaSPEC raw files'
DESCRIPTION = "NeaSPEC raw files"

def __init__(self, filename):
super().__init__(filename)
self.channel_a = None
self.channel_p = None

def read_spectra(self):

file_channel = str(self.filename.split(' ')[-2]).strip()
file_channel = str(self.filename.split(" ")[-2]).strip()
folder_file = str(self.filename.split(file_channel)[-2]).strip()

if 'P' in file_channel:
if "P" in file_channel:
self.channel_p = file_channel
self.channel_a = file_channel.replace('P', 'A')
self.channel_a = file_channel.replace("P", "A")
file_gsf_p = self.filename
file_gsf_a = self.filename.replace('P raw.gsf', 'A raw.gsf')
file_html = folder_file + '.html'
elif 'A' in file_channel:
file_gsf_a = self.filename.replace("P raw.gsf", "A raw.gsf")
file_html = folder_file + ".html"
elif "A" in file_channel:
self.channel_a = file_channel
self.channel_p = file_channel.replace('A', 'P')
self.channel_p = file_channel.replace("A", "P")
file_gsf_a = self.filename
file_gsf_p = self.filename.replace('A raw.gsf', 'P raw.gsf')
file_html = folder_file + '.html'
file_gsf_p = self.filename.replace("A raw.gsf", "P raw.gsf")
file_html = folder_file + ".html"
else:
raise ValueError("Channel not found in file name")

data_gsf_a = self._gsf_reader(file_gsf_a)
data_gsf_p = self._gsf_reader(file_gsf_p)
info = self._html_reader(file_html)

final_data, parameters, final_metas = self._format_file(data_gsf_a, data_gsf_p, info)
final_data, parameters, final_metas = self._format_file(
data_gsf_a, data_gsf_p, info
)

metas = [Orange.data.ContinuousVariable.make("column"),
Orange.data.ContinuousVariable.make("row"),
Orange.data.ContinuousVariable.make("run"),
Orange.data.StringVariable.make("channel")]
metas = [
Orange.data.ContinuousVariable.make("column"),
Orange.data.ContinuousVariable.make("row"),
Orange.data.ContinuousVariable.make("run"),
Orange.data.StringVariable.make("channel"),
]

domain = Orange.data.Domain([], None, metas=metas)
meta_data = Table.from_numpy(domain, X=np.zeros((len(final_data), 0)),
metas=np.asarray(final_metas, dtype=object))
meta_data = Table.from_numpy(
domain,
X=np.zeros((len(final_data), 0)),
metas=np.asarray(final_metas, dtype=object),
)

meta_data.attributes = parameters

depth = np.arange(0, int(parameters['Pixel Area (X, Y, Z)'][3]))
depth = np.arange(0, int(parameters["Pixel Area (X, Y, Z)"][3]))

return depth, final_data, meta_data

def _format_file(self, gsf_a, gsf_p, parameters):

info = {}
for row in parameters:
key = row[0].strip(':')
key = row[0].strip(":")
value = [v for v in row[1:] if len(v)]
if len(value) == 1:
value = value[0]
info.update({key: value})

info.update({'Reader': 'NeaReaderGSF'}) # key used in confirmation for complex fft calculation
info.update(
{"Reader": "NeaReaderGSF"}
) # key used in confirmation for complex fft calculation

averaging = int(info['Averaging'])
px_x = int(info['Pixel Area (X, Y, Z)'][1])
px_y = int(info['Pixel Area (X, Y, Z)'][2])
px_z = int(info['Pixel Area (X, Y, Z)'][3])
averaging = int(info["Averaging"])
px_x = int(info["Pixel Area (X, Y, Z)"][1])
px_y = int(info["Pixel Area (X, Y, Z)"][2])
px_z = int(info["Pixel Area (X, Y, Z)"][3])

data_complete = []
final_metas = []
Expand Down Expand Up @@ -310,4 +341,4 @@ def handle_data(self, data):

def _gsf_reader(self, path):
X, _, _ = reader_gsf(path)
return np.asarray(X)
return np.asarray(X)

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