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import csv
import logging
import os
import numpy as np
from rsciio._docstrings import FILENAME_DOC, LAZY_UNSUPPORTED_DOC, RETURNS_DOC
_logger = logging.getLogger(__name__)
# At some point, if there is another readerw, whith also use csv file, it will
# be necessary to mention the other reader in this message (and to add an
# argument in the load function to specify the correct reader)
invalid_file_error = (
"The csv reader can't import the file, please"
" make sure, that this is a valid Impulse log file."
)
invalid_filenaming_error = {
"The filename does not match Impulse naming, please"
" make sure that the filenames for the logfile and metadata file are unchanged."
}
def file_reader(filename, lazy=False):
"""
Read a DENSsolutions Impulse logfile.
Parameters
----------
%s
%s
%s
"""
if lazy is not False:
raise NotImplementedError("Lazy loading is not supported.")
csv_file = ImpulseCSV(filename)
return _impulseCSV_log_reader(csv_file)
file_reader.__doc__ %= (FILENAME_DOC, LAZY_UNSUPPORTED_DOC, RETURNS_DOC)
def _impulseCSV_log_reader(csv_file):
csvs = []
for key in csv_file.logged_quantity_name_list:
csvs.append(csv_file.get_dictionary(key))
return csvs
class ImpulseCSV:
def __init__(self, filename):
self.filename = filename
self._parse_header()
self._read_data()
def _parse_header(self):
with open(self.filename, "r") as f:
s = f.readline()
self.column_names = s.strip().split(",")
if not self._is_impulse_csv_file():
raise IOError(invalid_file_error)
self._read_metadatafile()
self.logged_quantity_name_list = self.column_names[2:]
def _is_impulse_csv_file(self):
return "TimeStamp" in self.column_names and len(self.column_names) >= 3
def get_dictionary(self, quantity):
return {
"data": self._data_dictionary[quantity],
"axes": self._get_axes(),
"metadata": self._get_metadata(quantity),
"original_metadata": {"Impulse_header": self.original_metadata},
}
def _get_metadata(self, quantity):
return {
"General": {
"original_filename": os.path.split(self.filename)[1],
"title": "%s" % quantity,
"date": self.original_metadata["Experiment_date"],
"time": self.original_metadata["Experiment_time"],
},
"Signal": {
"quantity": self._parse_quantity_units(quantity),
},
}
def _parse_quantity_units(self, quantity):
quantity_split = quantity.strip().split(" ")
if (
len(quantity_split) > 1
and quantity_split[-1][0] == "("
and quantity_split[-1][-1] == ")"
):
return quantity_split[-1].replace("(", "").replace(")", "")
else:
return ""
def _read_data(self):
names = [
name.replace(" ", "_")
.replace("°C", "C")
.replace("#", "No")
.replace("(", "")
.replace(")", "")
.replace("/", "_")
.replace("%", "Perc")
for name in self.column_names
]
data = np.genfromtxt(
self.filename,
delimiter=",",
dtype=None,
names=names,
skip_header=1,
encoding="latin1",
)
self._data_dictionary = dict()
for i, (name, name_dtype) in enumerate(zip(self.column_names, names)):
if name == "Experiment time":
self.time_axis = data[name_dtype]
elif name == "MixValve":
mixvalvedatachanged = data[name_dtype]
for index, item in enumerate(data[name_dtype]):
mixvalvedatachanged[index] = (
int(int(item.split(";")[0]) + 2) * 100
+ (int(item.split(";")[1]) + 2) * 10
+ (int(item.split(";")[2]) + 2)
)
mixvalvedatachangedint = np.array(mixvalvedatachanged, dtype=np.int32)
self._data_dictionary[name] = mixvalvedatachangedint
else:
self._data_dictionary[name] = data[name_dtype]
def _read_metadatafile(self):
# Locate the experiment metadata file
self.original_metadata = {}
notes = []
notes_section = False
if "_Synchronized data" in str(self.filename) or "raw" in str(
self.filename
): # Check if Impulse filename formatting is intact
metadata_file = (
"_".join(str(self.filename).split("_")[:-1]) + "_Metadata.log"
).replace("\\", "/")
if os.path.isfile(metadata_file):
with open(metadata_file, newline="") as csvfile:
metadata_file_reader = csv.reader(csvfile, delimiter=",")
for row in metadata_file_reader:
if notes_section:
notes.append(row[0])
elif row[0] == "Live notes":
notes_section = True
notes = [row[1].strip()]
else:
self.original_metadata[row[0].replace(" ", "_")] = row[
1
].strip()
self.original_metadata["Notes"] = notes
else:
_logger.warning("No metadata file found in folder")
else:
raise IOError(invalid_filenaming_error)
def _get_axes(self):
return [
{
"size": self.time_axis.shape[0],
"index_in_array": 0,
"name": "Time",
"scale": np.diff(self.time_axis[1:-1]).mean(),
"offset": 0,
"units": "Seconds",
"navigate": False,
}
]
|
