Home-IoT/craftbeerpi4-pione
1
0
Fork 0
mirror of https://github.com/PiBrewing/craftbeerpi4.git synced 2024-11-21 22:48:16 +01:00
Code Issues Projects Releases Packages Wiki Activity

limit log data transfer to not exceed 2 times max_rows = 1000 rows by removing every nth row. This keeps the user interface operable.

Browse source
This commit is contained in:
phylax2020 2022-07-09 22:23:26 +02:00
parent 4652b2b516
commit e95237eef6
1 changed files with 18 additions and 7 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

25
cbpi/controller/log_file_controller.py
View file

@ -116,28 +116,36 @@ class LogController:
for name in names:
# get all log names
all_filenames = glob.glob('./logs/sensor_%s.log*' % name)
# concat all logs
df = pd.concat([pd.read_csv(f, parse_dates=True, date_parser=dateparse, index_col='DateTime', names=['DateTime', name], header=None) for f in all_filenames])
logging.info("Read all files for {}".format(names))
# resample if rate provided
if sample_rate is not None:
df = df[name].resample(sample_rate).max()
logging.info("Sampled now for {}".format(names))
df = df.dropna()
# if sample_rate is not None:
# df = df[name].resample(sample_rate).max()
# logging.info("Sampled now for {}".format(names))
df = df[name].dropna()
# take every nth row so that total number of rows does not exceed max_rows * 2
max_rows = 500
total_rows = df.shape[0]
if (total_rows > 0) and (total_rows > max_rows):
nth = int(total_rows/max_rows)
if nth > 1:
df = df.iloc[::nth]
if result is None:
result = df
else:
result = pd.merge(result, df, how='outer', left_index=True, right_index=True)
data = {"time": df.index.tolist()}
if len(names) > 1:
for name in names:
data[name] = result[name].interpolate(limit_direction='both', limit=10).tolist()
else:
data[name] = result.interpolate().tolist()
logging.info("Send Log for {}".format(names))
return data
async def get_data2(self, ids) -> dict:
@ -146,7 +154,10 @@ class LogController:
result = dict()
for id in ids:
df = pd.read_csv("./logs/sensor_%s.log" % id, parse_dates=True, date_parser=dateparse, index_col='DateTime', names=['DateTime',"Values"], header=None)
# df = pd.read_csv("./logs/sensor_%s.log" % id, parse_dates=True, date_parser=dateparse, index_col='DateTime', names=['DateTime',"Values"], header=None)
# concat all logs
all_filenames = glob.glob('./logs/sensor_%s.log*' % id)
df = pd.concat([pd.read_csv(f, parse_dates=True, date_parser=dateparse, index_col='DateTime', names=['DateTime', 'Values'], header=None) for f in all_filenames])
result[id] = {"time": df.index.astype(str).tolist(), "value":df.Values.tolist()}
return result