matt/pi-temp
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Cut out a bunch to speed up plot.

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mattbk
2017-11-17 04:01:24 +00:00
parent baa3520af6
commit 3210dd89a2
4 changed files with 95 additions and 187141 deletions
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89
pi_temp.py
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@ -70,19 +70,6 @@ def lab_temp():
@app.route("/history", methods=['GET']) #Add date limits in the URL #Arguments: from=2015-03-04&to=2015-03-05
def history():
temperatures, humidities, timezone, from_date_str, to_date_str = get_records()
# Create new record tables so that datetimes are adjusted back to the user browser's time zone.
time_adjusted_temperatures = []
time_adjusted_humidities = []
for record in temperatures:
local_timedate = arrow.get(record[0], "YYYY-MM-DD HH:mm").to(timezone)
time_adjusted_temperatures.append([local_timedate.format('YYYY-MM-DD HH:mm'), round(record[2],2)])
for record in humidities:
local_timedate = arrow.get(record[0], "YYYY-MM-DD HH:mm").to(timezone)
time_adjusted_humidities.append([local_timedate.format('YYYY-MM-DD HH:mm'), round(record[2],2)])
print "rendering history.html with: %s, %s, %s" % (timezone, from_date_str, to_date_str)
# Create new record tables so that datetimes are adjusted back to the user browser's time zone.
time_series_adjusted_temperatures = []
@ -134,17 +121,9 @@ def history():
fig = Figure(data=data, layout=layout)
graphJSON = json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)
return render_template("history.html", timezone = timezone,
temp = time_adjusted_temperatures,
hum = time_adjusted_humidities,
from_date = from_date_str,
to_date = to_date_str,
temp_items = len(temperatures),
query_string = request.query_string, #This query string is used
#by the Plotly link
hum_items = len(humidities),
graphJSON=graphJSON,
)
return render_template("history.html", timezone = timezone,
graphJSON = graphJSON,
)
def get_records():
from_date_str = request.args.get('from',time.strftime("%Y-%m-%d 00:00")) #Get the from date value from the URL
@ -196,68 +175,6 @@ def get_records():
return [temperatures, humidities, timezone, from_date_str, to_date_str]
@app.route("/to_plotly", methods=['GET']) #This method will send the data to ploty.
def to_plotly():
temperatures, humidities, timezone, from_date_str, to_date_str = get_records()
# Create new record tables so that datetimes are adjusted back to the user browser's time zone.
time_series_adjusted_tempreratures = []
time_series_adjusted_humidities = []
time_series_temprerature_values = []
time_series_humidity_values = []
for record in temperatures:
local_timedate = arrow.get(record[0], "YYYY-MM-DD HH:mm").to(timezone)
time_series_adjusted_tempreratures.append(local_timedate.format('YYYY-MM-DD HH:mm'))
time_series_temprerature_values.append(round(record[2],2))
for record in humidities:
local_timedate = arrow.get(record[0], "YYYY-MM-DD HH:mm").to(timezone)
time_series_adjusted_humidities.append(local_timedate.format('YYYY-MM-DD HH:mm')) #Best to pass datetime in text
#so that Plotly respects it
time_series_humidity_values.append(round(record[2],2))
temp = Scatter(
x=time_series_adjusted_tempreratures,
y=time_series_temprerature_values,
name='Temperature'
)
hum = Scatter(
x=time_series_adjusted_humidities,
y=time_series_humidity_values,
name='Humidity',
yaxis='y2'
)
data = Data([temp, hum])
layout = Layout(
title="Temperature and Humidity in Clayton's Apartment",
xaxis=XAxis(
type='date',
autorange=True
),
yaxis=YAxis(
title='Fahrenheit',
type='linear',
autorange=True
),
yaxis2=YAxis(
title='Percent',
type='linear',
autorange=True,
overlaying='y',
side='right'
)
)
fig = Figure(data=data, layout=layout)
#plot_url = py.plot(fig, filename='lab_temp_hum')
graphJSON = json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)
return graphJSON
def validate_date(d):
try:
datetime.datetime.strptime(d, '%Y-%m-%d %H:%M')