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MetObs
AossTower
Commits
1a5f5e3e
Commit
1a5f5e3e
authored
5 years ago
by
William Roberts
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Begin making time interval more dynamic
parent
e74150d9
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aosstower/level_00/influxdb.py
+74
-38
74 additions, 38 deletions
aosstower/level_00/influxdb.py
with
74 additions
and
38 deletions
aosstower/level_00/influxdb.py
+
74
−
38
View file @
1a5f5e3e
...
...
@@ -15,6 +15,7 @@ from metobscommon.util.nc import calculate_wind_gust
import
numpy
as
np
import
pandas
as
pd
import
warnings
from
datetime
import
timedelta
LOG
=
logging
.
getLogger
(
__name__
)
# map station name to InfluxDB tags
...
...
@@ -63,44 +64,69 @@ class Updater(object):
averaging every 5 minutes of data added.
"""
def
__init__
(
self
):
# Counter that returns rolling average every 5 minutes. independent of
# self.data since self.data length can be 144 == 12 minutes.
self
.
counter
=
0
self
.
data
=
{}
self
.
start_time
=
None
def
rolling_average
(
self
,
record
):
self
.
counter
+=
1
# Add new data to dict.
for
key
in
record
:
self
.
data
.
setdefault
(
key
,
[]).
append
(
record
[
key
])
# If 5 minutes of data are ready, average current data in dict (up to 15 minutes until data is
# dropped for the first time, then up to 12 minutes thereafter are held). 60 * 5 seconds = 5 minutes.
if
self
.
counter
%
60
==
0
:
self
.
start_time
=
self
.
start_time
if
self
.
start_time
else
record
[
'
timestamp
'
]
time_interval
=
(
record
[
'
timestamp
'
]
-
self
.
start_time
).
total_seconds
()
# Add in 5 minutes for cutoff
start
=
record
[
'
timestamp
'
]
-
timedelta
(
seconds
=
time_interval
%
300
)
end
=
self
.
start_time
+
timedelta
(
minutes
=
5
)
if
time_interval
>
300
:
for
key
in
record
:
if
key
==
'
timestamp
'
:
self
.
data
[
key
]
=
np
.
append
(
self
.
data
[
key
],
end
)
else
:
self
.
data
[
key
]
=
np
.
append
(
self
.
data
[
key
],
np
.
nan
)
else
:
for
key
in
record
:
if
self
.
data
.
get
(
key
)
is
None
:
self
.
data
[
key
]
=
np
.
array
([])
self
.
data
[
key
]
=
np
.
append
(
self
.
data
[
key
],
record
[
key
])
# If 5 minutes of data are ready, average current data in dict. Holds up to 15 minutes.
# 60 * 5 seconds = 5 minutes.
if
time_interval
>=
300
:
# Appending to a DataFrame is slow. Instead, this adds to a dict in chunks and passes it to the DataFrame.
frame
=
self
.
_calculate_averages
()
frame
.
fillna
(
value
=
np
.
nan
,
inplace
=
True
)
print
(
frame
[
'
rel_hum
'
])
print
(
frame
.
resample
(
'
5T
'
,
closed
=
'
right
'
).
mean
()[
'
rel_hum
'
])
print
(
frame
.
asfreq
(
'
5T
'
)[
'
rel_hum
'
])
# Keep data set within minimum window to improve speed.
# Wind gusts looks at 10 minute intervals, including the first data point which needs 2 minutes of data
# before it, totalling 12 minutes. Since data is sent every 5 minutes, at 15+ minutes we should
# release old data. Data is in 5 second intervals, and 5 seconds * 180 = 15 minutes.
if
self
.
counter
==
180
:
for
key
,
val
in
self
.
data
.
items
():
# Keep last 7 minutes since 5 + 7 = 12: 5 seconds * 84 = 7 minutes. Optimises performance.
self
.
data
[
key
]
=
val
[
-
84
:]
self
.
counter
-=
60
# before it, totalling 12 minutes.
test
=
frame
.
asfreq
(
'
5T
'
)
if
len
(
test
.
index
)
>
3
:
new_frame
=
frame
.
copy
()
new_frame
[
'
timestamp
'
]
=
list
(
new_frame
.
index
)
data
=
new_frame
.
drop
(
new_frame
.
index
[:
new_frame
.
index
.
get_loc
(
test
.
index
[
-
3
])]).
to_dict
(
'
list
'
)
for
key
in
self
.
data
:
self
.
data
[
key
]
=
data
[
key
]
if
time_interval
>
300
:
for
key
in
record
:
if
key
==
'
timestamp
'
:
if
end
!=
start
:
self
.
data
[
key
]
=
np
.
array
([
start
])
else
:
if
end
!=
start
:
self
.
data
[
key
]
=
np
.
array
([
np
.
nan
])
self
.
start_time
=
self
.
data
[
'
timestamp
'
][
-
1
]
for
key
in
record
:
if
self
.
data
.
get
(
key
)
is
None
:
self
.
data
[
key
]
=
np
.
array
([])
self
.
data
[
key
]
=
np
.
append
(
self
.
data
[
key
],
record
[
key
])
else
:
# Make 10 minute gusts before 12 minutes nans because data is insufficient.
frame
[
'
gust_10m
'
].
mask
(
frame
[
'
gust_10m
'
]
>
-
1.
,
inplace
=
True
)
frame
.
fillna
(
np
.
nan
,
inplace
=
True
)
return
frame
self
.
start_time
=
self
.
data
[
'
timestamp
'
][
-
1
]
return
test
def
_calculate_averages
(
self
):
frame
=
pd
.
DataFrame
(
self
.
data
)
KNOTS_9
=
calc
.
knots_to_mps
(
9.
)
KNOTS_5
=
calc
.
knots_to_mps
(
5.
)
frame
=
pd
.
DataFrame
(
self
.
data
)
frame
.
set_index
(
'
timestamp
'
,
inplace
=
True
)
frame
.
mask
(
frame
==
-
99999.
,
inplace
=
True
)
frame
.
fillna
(
value
=
np
.
nan
,
inplace
=
True
)
# Add wind direction components so we can average wind direction properly
# Add wind direction components so we can average wind direction properly.
frame
[
'
wind_east
'
],
frame
[
'
wind_north
'
],
_
=
calc
.
wind_vector_components
(
frame
[
'
wind_speed
'
],
frame
[
'
wind_dir
'
])
frame
[
'
wind_dir
'
]
=
calc
.
wind_vector_degrees
(
frame
[
'
wind_east
'
],
frame
[
'
wind_north
'
])
...
...
@@ -110,18 +136,20 @@ class Updater(object):
"
it from air temp and relative humidity
"
)
frame
[
'
dewpoint
'
]
=
calc
.
dewpoint
(
frame
[
'
air_temp
'
],
frame
[
'
rh
'
])
# 2 minute rolling average
of 5 second data
.
# 2 minute rolling average.
winds_frame_2m
=
frame
[[
'
wind_speed
'
,
'
wind_east
'
,
'
wind_north
'
]].
rolling
(
'
2T
'
).
mean
()
frame
[
'
wind_speed_2m
'
]
=
winds_frame_2m
[
'
wind_speed
'
]
frame
[
'
wind_dir_2m
'
]
=
calc
.
wind_vector_degrees
(
winds_frame_2m
[
'
wind_east
'
],
winds_frame_2m
[
'
wind_north
'
])
# TODO: PEAK_DIR IS THE 5 SEC MAX FROM LAST MINUTE IF 5 KTS OVER LAST 2 MINUTE AVG.
# 1 minute rolling peaks
wind_peak_1m
=
frame
[
'
wind_speed
'
].
rolling
(
window
=
'
1T
'
,
center
=
False
).
max
()
# criteria for a fast wind to be considered a wind gust
gust_mask
=
(
winds_frame_2m
[
'
wind_speed
'
]
>=
KNOTS_9
)
&
\
(
wind_peak_1m
>=
winds_frame_2m
[
'
wind_speed
'
]
+
KNOTS_5
)
frame
[
'
cur_
gust
'
]
=
wind_peak_1m
.
mask
(
~
gust_mask
)
frame
[
'
gust
_1m
'
]
=
wind_peak_1m
.
mask
(
~
gust_mask
)
frame
[
'
gust_10m
'
]
=
calculate_wind_gust
(
frame
[
'
wind_speed
'
],
winds_frame_2m
[
'
wind_speed
'
])
# Make 10 minute gusts before 12 minutes nans because data is insufficient.
if
(
self
.
data
[
'
timestamp
'
][
-
1
]
-
self
.
data
[
'
timestamp
'
][
0
]).
total_seconds
()
<
720
:
frame
[
'
gust_10m
'
].
mask
(
frame
[
'
gust_10m
'
]
>
-
1.
,
inplace
=
True
)
return
frame
...
...
@@ -136,6 +164,9 @@ def convert_to_influx_frame(record_gen, symbols, debug=False):
def
construct_url
(
data
):
for
key
,
val
in
data
.
items
():
if
val
is
None
or
isinstance
(
val
,
float
)
and
np
.
isnan
(
val
):
data
[
key
]
=
''
return
(
'
http://weatherstation.wunderground.com/weatherstation/updateweatherstation.php?
'
'
ID={ID}&
'
'
PASSWORD={PASSWORD}&
'
...
...
@@ -161,26 +192,25 @@ def get_url_data(avg, wu_id, wu_pw):
# Information on what paramaters that can be sent:
# https://feedback.weather.com/customer/en/portal/articles/2924682-pws-upload-protocol?b_id=17298
# For timestamp, want YYYY-MM-DD+hh:mm:ss of last dataset that was averaged, rounded up to nearest minute.
timestamp
=
avg
.
index
[
-
1
].
round
(
'
1T
'
).
isoformat
(
'
+
'
)
timestamp
=
avg
.
index
[
-
1
].
isoformat
(
'
+
'
)
wind_dir
=
avg
[
'
wind_dir
'
][
-
1
]
wind_dir_2m
=
avg
[
'
wind_dir_2m
'
][
-
1
]
rel_hum
=
avg
[
'
rel_hum
'
][
-
1
]
solar_flux
=
avg
[
'
solar_flux
'
][
-
1
]
precip
=
avg
[
'
precip
'
][
-
1
]
accum_precip
=
avg
[
'
accum_precip
'
][
-
1
]
# Converts from m/s to mph.
wind_speed
=
avg
[
'
wind_speed
'
][
-
1
]
*
2.23694
wind_speed_2m
=
avg
[
'
wind_speed_2m
'
][
-
1
]
*
2.23694
cur_
gust
=
avg
[
'
cur_
gust
'
][
-
1
]
*
2.23694
gust
_1m
=
avg
[
'
gust
_1m
'
][
-
1
]
*
2.23694
gust_10m
=
avg
[
'
gust_10m
'
][
-
1
]
*
2.23694
rel_hum
=
avg
[
'
rel_hum
'
][
-
1
]
# Converts degrees Celsius to degrees Fahrenheit
air_temp
=
avg
[
'
air_temp
'
][
-
1
]
*
9.
/
5.
+
32.
dewpoint
=
avg
[
'
dewpoint
'
][
-
1
]
*
9.
/
5.
+
32.
# hpa to barometric pressure inches
pressure
=
avg
[
'
pressure
'
][
-
1
]
*
0.02952998016471232
# degrees Celcus to degrees Fahrenheit.
dewpoint
=
avg
[
'
dewpoint
'
][
-
1
]
*
9.
/
5.
+
32.
solar_flux
=
avg
[
'
solar_flux
'
][
-
1
]
precip
=
avg
[
'
precip
'
][
-
1
]
accum_precip
=
avg
[
'
accum_precip
'
][
-
1
]
return
{
'
ID
'
:
wu_id
,
'
PASSWORD
'
:
wu_pw
,
'
dateutc
'
:
timestamp
,
'
winddir
'
:
wind_dir
,
'
winddir_avg2m
'
:
wind_dir_2m
,
'
windspeedmph
'
:
wind_speed
,
'
windspdmph_avg2m
'
:
wind_speed_2m
,
'
windgustmph
'
:
cur_
gust
,
'
windspeedmph
'
:
wind_speed
,
'
windspdmph_avg2m
'
:
wind_speed_2m
,
'
windgustmph
'
:
gust
_1m
,
'
windgustmph_10m
'
:
gust_10m
,
'
humidity
'
:
rel_hum
,
'
tempf
'
:
air_temp
,
'
baromin
'
:
pressure
,
'
dewptf
'
:
dewpoint
,
'
solarradiation
'
:
solar_flux
,
'
rainin
'
:
precip
,
'
dailyrainin
'
:
accum_precip
}
...
...
@@ -247,14 +277,17 @@ def main():
influx_gen
=
convert_to_influx_frame
(
record_gen
,
symbols
,
args
.
debug
)
influx_gen
=
influxdb
.
grouper
(
influx_gen
,
args
.
bulk
)
updater
=
Updater
()
import
time
t0
=
time
.
time
()
for
record
in
influx_gen
:
lines
=
influxdb
.
frame_records
(
record
,
**
station_tags
)
influxdb
.
insert
(
lines
,
host
=
args
.
host
,
port
=
args
.
port
,
dbname
=
args
.
dbname
)
#
lines = influxdb.frame_records(record, **station_tags)
#
influxdb.insert(lines, host=args.host, port=args.port, dbname=args.dbname)
# Record is in a list of size 1, but want just the record.
avg
=
updater
.
rolling_average
(
record
[
0
])
# Once every 5 minutes: 0 through 295 seconds inclusive in 5 second intervals.
if
avg
is
not
None
:
url
=
construct_url
(
get_url_data
(
avg
,
args
.
wu_id
,
wu_pw
))
print
(
url
)
if
wu_pw
and
args
.
ldmp
:
resp
=
requests
.
post
(
url
)
if
resp
.
status_code
!=
200
:
...
...
@@ -263,7 +296,10 @@ def main():
if
args
.
sleep_interval
:
time
.
sleep
(
args
.
sleep_interval
)
t1
=
time
.
time
()
print
(
t1
-
t0
)
except
(
RuntimeError
,
ValueError
,
KeyError
,
requests
.
RequestException
):
raise
if
hasattr
(
record_gen
,
'
close
'
):
record_gen
.
close
()
...
...
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