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milanoscaloromana
pm
Commits
93e2646c
Commit
93e2646c
authored
5 years ago
by
Roberto Re
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PANDAS.py
parent
771a9535
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93e2646c
#!/usr/bin/python
# -*- coding: utf-8 -*-
from
__future__
import
division
__version__
=
20190626
import
re
from
time
import
time
,
sleep
import
datetime
from
datetime
import
datetime
,
timedelta
from
datetime
import
datetime
import
io
import
sys
from
datetime
import
date
import
csv
import
numpy
as
np
import
matplotlib
import
matplotlib.pyplot
as
plt
import
pandas
as
pd
from
pandas
import
read_csv
import
seaborn
as
sns
DEBUG
=
False
YOUR_PATH
=
"
/dir/your_path
"
def
PLOT
(
filepath_
=
None
,
title_
=
None
,
FLAG_
=
None
):
df
=
read_csv
(
filepath_
,
sep
=
"
,
"
,
index_col
=
[
'
daily
'
],
parse_dates
=
[
'
daily
'
],
na_values
=
[
'
-999
'
])
#No,date,daily,year,month,day,hour,min,PM1,PM2.5,PM10,TEMP,Hum,PRES
if
(
FLAG_
==
"
PM
"
):
df
=
df
.
drop
([
'
No
'
,
'
date
'
,
'
year
'
,
'
month
'
,
'
day
'
,
'
hour
'
,
'
min
'
,
'
TEMP
'
,
'
Hum
'
,
'
PRES
'
],
axis
=
1
)
if
(
FLAG_
==
"
RH
"
):
df
=
df
.
drop
([
'
No
'
,
'
date
'
,
'
year
'
,
'
month
'
,
'
day
'
,
'
hour
'
,
'
min
'
,
'
PM1
'
,
'
PM2_5
'
,
'
PM10
'
,
'
TEMP
'
,
'
PRES
'
],
axis
=
1
)
if
(
FLAG_
==
"
TEMP
"
):
df
=
df
.
drop
([
'
No
'
,
'
date
'
,
'
year
'
,
'
month
'
,
'
day
'
,
'
hour
'
,
'
min
'
,
'
PM1
'
,
'
PM2_5
'
,
'
PM10
'
,
'
Hum
'
,
'
PRES
'
],
axis
=
1
)
df
=
df
.
groupby
(
'
daily
'
).
mean
()
if
(
FLAG_
==
"
PM
"
):
pd
.
DataFrame
({
'
PM2_5
'
:
100
,
'
PM10
'
:
100
},
index
=
[
'
PM2_5
'
])
ax
=
df
.
plot
()
ax
.
grid
()
ratio
=
0.6
xleft
,
xright
=
ax
.
get_xlim
()
ybottom
,
ytop
=
ax
.
get_ylim
()
ax
.
set_aspect
(
abs
((
xright
-
xleft
)
/
(
ybottom
-
ytop
))
*
ratio
)
t0_
=
'
2019-06-01
'
now
=
datetime
.
utcnow
().
strftime
(
'
%Y-%m-%d
'
)
ax
.
hlines
(
8.2
,
t0_
,
now
,
linestyles
=
'
dashed
'
,
color
=
'
darkorange
'
)
ax
.
annotate
(
'
Very good
'
,(
t0_
,
6.0
),
color
=
'
darkorange
'
)
ax
.
hlines
(
16.4
,
t0_
,
now
,
linestyles
=
'
dashed
'
,
color
=
'
darkorange
'
)
ax
.
annotate
(
'
Good
'
,(
t0_
,
14.2
),
color
=
'
darkorange
'
)
ax
.
hlines
(
25.1
,
t0_
,
now
,
linestyles
=
'
dashed
'
,
color
=
'
darkorange
'
)
ax
.
annotate
(
'
Fair
'
,(
t0_
,
23.0
),
color
=
'
darkorange
'
)
ax
.
hlines
(
37.4
,
t0_
,
now
,
linestyles
=
'
dashed
'
,
color
=
'
darkorange
'
)
ax
.
annotate
(
'
Poor
'
,(
t0_
,
35.3
),
color
=
'
darkorange
'
)
ax
.
annotate
(
'
Very Poor
'
,(
t0_
,
50.0
),
color
=
'
darkorange
'
)
fig
=
ax
.
get_figure
()
ax
.
set
(
xlabel
=
"
Date
"
,
ylabel
=
"
average 24h polveri sottili (g/m3)
"
,
title
=
"
Milano Scalo Romana - Air quality category (PM2.5 g/m3)
\n
Technical data: PM sensor PM1\PM2.5 10%, PM10 30%
"
);
now
=
datetime
.
utcnow
().
strftime
(
'
%Y%m
'
)
filename
=
YOUR_PATH
+
"
/PM/data/
"
+
now
+
"
.png
"
fig
.
savefig
(
filename
)
filepath_
=
YOUR_PATH
+
"
pandas.log
"
tmp
=
df
.
to_string
()
OBJ_file_write_avg
=
open
(
filepath_
,
'
w
'
)
OBJ_file_write_avg
.
write
(
tmp
+
"
\n
"
)
OBJ_file_write_avg
.
close
()
if
(
FLAG_
==
"
RH
"
):
pd
.
DataFrame
({
'
Hum
'
:
100
},
index
=
[
'
Hum
'
])
ax
=
df
.
plot
()
ax
.
grid
()
ratio
=
0.4
xleft
,
xright
=
ax
.
get_xlim
()
ybottom
,
ytop
=
ax
.
get_ylim
()
ax
.
set_aspect
(
abs
((
xright
-
xleft
)
/
(
ybottom
-
ytop
))
*
ratio
)
t0_
=
'
2019-06-01
'
now
=
datetime
.
utcnow
().
strftime
(
'
%Y-%m-%d
'
)
fig
=
ax
.
get_figure
()
ax
.
set
(
xlabel
=
"
Date
"
,
ylabel
=
"
Relative Humidity %
"
,
title
=
"
Milano Scalo Romana - Relative Humidity %
\n
0%100%(20%80% 3% @25C,0%20% 80%100% @25C 5%)
"
);
now
=
datetime
.
utcnow
().
strftime
(
'
%Y%m
'
)
filename
=
YOUR_PATH
+
"
/PM/data/RH_
"
+
now
+
"
.png
"
fig
.
savefig
(
filename
)
filepath_
=
YOUR_PATH
+
"
pandas.log
"
tmp
=
df
.
to_string
()
OBJ_file_write_avg
=
open
(
filepath_
,
'
a
'
)
OBJ_file_write_avg
.
write
(
tmp
+
"
\n
"
)
OBJ_file_write_avg
.
close
()
if
(
FLAG_
==
"
TEMP
"
):
pd
.
DataFrame
({
'
TEMP
'
:
100
},
index
=
[
'
TEMP
'
])
ax
=
df
.
plot
()
ax
.
grid
()
ratio
=
0.4
xleft
,
xright
=
ax
.
get_xlim
()
ybottom
,
ytop
=
ax
.
get_ylim
()
ax
.
set_aspect
(
abs
((
xright
-
xleft
)
/
(
ybottom
-
ytop
))
*
ratio
)
t0_
=
'
2019-06-01
'
now
=
datetime
.
utcnow
().
strftime
(
'
%Y-%m-%d
'
)
fig
=
ax
.
get_figure
()
ax
.
set
(
xlabel
=
"
Date
"
,
ylabel
=
"
Temperature C
"
,
title
=
"
Milano Scalo Romana - Temperature C
\n
-30 70C (0 65C 1.0C)
"
);
now
=
datetime
.
utcnow
().
strftime
(
'
%Y%m
'
)
filename
=
YOUR_PATH
+
"
/PM/data/TEMP_
"
+
now
+
"
.png
"
fig
.
savefig
(
filename
)
filepath_
=
YOUR_PATH
+
"
pandas.log
"
tmp
=
df
.
to_string
()
OBJ_file_write_avg
=
open
(
filepath_
,
'
a
'
)
OBJ_file_write_avg
.
write
(
tmp
+
"
\n
"
)
OBJ_file_write_avg
.
close
()
Str_Anno
=
datetime
.
utcnow
().
strftime
(
'
%Y
'
)
filepath_
=
YOUR_PATH
+
"
/PM/data/MILANO_SCALO_ROMANA_PARTICULATE_MATTER_annual_series_
"
+
Str_Anno
+
"
.csv
"
PLOT
(
filepath_
,
"
#FCUB
"
,
"
PM
"
)
PLOT
(
filepath_
,
"
#FCUB
"
,
"
RH
"
)
PLOT
(
filepath_
,
"
#FCUB
"
,
"
TEMP
"
)
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