def get_light_data(back_period="None"):
# print "test:", back_period, type(back_period), repr(back_period)
if back_period != "None":
back_period = int(back_period)
else:
back_period = get_setting("ENVIRONMENT_BACK_PLOT_PERIOD", int).value
node_1_ldr_light = db.session.query(SensorData.date_time, SensorData.value).join(Sensors). \
filter(Sensors.id == SensorData.sensor_id).filter(Sensors.id == 15). \
order_by(SensorData.date_time.desc()).limit(back_period).all()
# print "Sensor_data:", node_1_ldr_light
scan_period = get_setting("ENVIRONMENT_SCAN_PERIOD", float)
node_1_ldr_light_data = []
timestamp = []
# timestamp_str = node_1_ldr_light[0][0]
for i in range(back_period): # enumerate(node_1_dht_temp):
try:
node_1_ldr_light_data.append(node_1_ldr_light[i][1] if node_1_ldr_light[i][1] is not None else None) # ????
except IndexError:
pass
try:
timestamp.append(time_since_epoch(node_1_ldr_light[i][0]) * 1000)
except IndexError:
pass
# print node_1_ldr_light_data
series = {"data": [
{
"name": "Node-1 LDR L [e]",
"type": "line",
"data": zip(timestamp, node_1_ldr_light_data)[::-1],
"color": "orange",
"tooltip": {
"valueSuffix": ' e'
}
}
],
"back_period": back_period,
"node_ldr_last_light": node_1_ldr_light_data[0],
"node_ldr_average_light": stats.mean(node_1_ldr_light_data),
"scan_period": scan_period.value
}
# print series["data"]
return jsonify(**series)
def view_server():
form = BackPeriodForm()
back_period = None
if form.validate_on_submit():
print "Form OK 22"
back_period = form.back_period.data
print back_period
table_data = read_temp_log(get_files(),
back_period=back_period if back_period is not None else
get_setting("SERVER_TEMP_PLOT_BACK_PERIOD", int).value)
temperature_chart_data = [[], [], [], []]
load_chart_data = [[], [], [], []]
all_date_time = []
for row in table_data:
if row[0].startswith("Tim"):
continue
date_time = datetime.datetime.strptime(row[0], "%H:%M:%S %m/%d/%y")
all_date_time.append(date_time)
temperature_chart_data[0].append(float(row[1]))
temperature_chart_data[1].append(float(row[2]))
temperature_chart_data[2].append(float(row[3]))
temperature_chart_data[3].append(float(row[4]))
load_chart_data[0].append(float(row[5]))
load_chart_data[1].append(float(row[6]))
load_chart_data[2].append(float(row[7]))
load_chart_data[3].append(float(row[8]))
update_time = all_date_time[0]
stat_data = [
{
"name": "Core 0",
"temp_mean": stats.mean(temperature_chart_data[0]),
"temp_st_dev": stats.st_dev(temperature_chart_data[0]),
"load_mean": stats.mean(load_chart_data[0]),
"load_st_dev": stats.st_dev(load_chart_data[0]),
"temperature_last_reading": temperature_chart_data[0][0]
},
{
"name": "Core 1",
"temp_mean": stats.mean(temperature_chart_data[1]),
"temp_st_dev": stats.st_dev(temperature_chart_data[1]),
"load_mean": stats.mean(load_chart_data[1]),
"load_st_dev": stats.st_dev(load_chart_data[1]),
"temperature_last_reading": temperature_chart_data[1][0]
},
{
"name": "Core 2",
"temp_mean": stats.mean(temperature_chart_data[2]),
"temp_st_dev": stats.st_dev(temperature_chart_data[2]),
"load_mean": stats.mean(load_chart_data[2]),
"load_st_dev": stats.st_dev(load_chart_data[2]),
"temperature_last_reading": temperature_chart_data[2][0]
},
{
"name": "Core 3",
"temp_mean": stats.mean(temperature_chart_data[3]),
"temp_st_dev": stats.st_dev(temperature_chart_data[3]),
"load_mean": stats.mean(load_chart_data[3]),
"load_st_dev": stats.st_dev(load_chart_data[3]),
"temperature_last_reading": temperature_chart_data[3][0]
},
]
proc_max_temp = get_setting("PROC_MAX_TEMP_LIMIT", float).value
limits = {"proc_max_temp": proc_max_temp}
print "lesko", limits
# pprint(stat_data)
return render_template("view_server.html",
form=form,
back_period=back_period,
stat_data=stat_data,
limits=limits,
update_time=update_time,
page_loc="server")
def get_server_data(back_period="None"):
print "get_server_data"
print back_period, type(back_period), repr(back_period)
if back_period != "None":
back_period = int(back_period)
else:
back_period = get_setting("SERVER_TEMP_PLOT_BACK_PERIOD", int).value
table_data = read_temp_log(get_files(),
back_period=back_period)
temperature_chart_data = [[], [], [], [], []]
load_chart_data = [[], [], [], [], []]
for row in table_data:
if row[0].startswith("Tim"):
continue
# print row[0]
date_time = datetime.datetime.strptime(row[0], "%H:%M:%S %m/%d/%y")
temperature_chart_data[0].append(float(row[1]))
temperature_chart_data[1].append(float(row[2]))
temperature_chart_data[2].append(float(row[3]))
temperature_chart_data[3].append(float(row[4]))
temperature_chart_data[4].append(time_since_epoch(date_time) * 1000)
load_chart_data[0].append(float(row[5]))
load_chart_data[1].append(float(row[6]))
load_chart_data[2].append(float(row[7]))
load_chart_data[3].append(float(row[8]))
load_chart_data[4].append(time_since_epoch(date_time) * 1000)
# print temperature_chart_data[3]
data = {
"chart_real_time_temperature": {"data": [
{
"name": "Core 0",
"data": zip(temperature_chart_data[4], temperature_chart_data[0])[::-1]
},
{
"name": "Core 1",
"data": zip(temperature_chart_data[4], temperature_chart_data[1])[::-1]
},
{
"name": "Core 2",
"data": zip(temperature_chart_data[4], temperature_chart_data[2])[::-1]
},
{
"name": "Core 3",
"data": zip(temperature_chart_data[4], temperature_chart_data[3])[::-1]
}
]},
"chart_real_time_load": {"data": [
{
"name": "Core 0",
"data": zip(load_chart_data[4], load_chart_data[0])[::-1]
},
{
"name": "Core 1",
"data": zip(load_chart_data[4], load_chart_data[1])[::-1]
},
{
"name": "Core 2",
"data": zip(load_chart_data[4], load_chart_data[2])[::-1]
},
{
"name": "Core 3",
"data": zip(load_chart_data[4], load_chart_data[3])[::-1]
}
]},
"stat_data": {"data": [
{
"name": "Core 0",
"temp_mean": stats.mean(temperature_chart_data[0]),
"temp_st_dev": stats.st_dev(temperature_chart_data[0]),
"load_mean": stats.mean(load_chart_data[0]),
"load_st_dev": stats.st_dev(load_chart_data[0])
},
{
"name": "Core 1",
"temp_mean": stats.mean(temperature_chart_data[1]),
"temp_st_dev": stats.st_dev(temperature_chart_data[1]),
"load_mean": stats.mean(load_chart_data[1]),
"load_st_dev": stats.st_dev(load_chart_data[1])
},
{
"name": "Core 2",
"temp_mean": stats.mean(temperature_chart_data[2]),
"temp_st_dev": stats.st_dev(temperature_chart_data[2]),
"load_mean": stats.mean(load_chart_data[2]),
"load_st_dev": stats.st_dev(load_chart_data[2])
},
{
"name": "Core 3",
"temp_mean": stats.mean(temperature_chart_data[3]),
"temp_st_dev": stats.st_dev(temperature_chart_data[3]),
"load_mean": stats.mean(load_chart_data[3]),
"load_st_dev": stats.st_dev(load_chart_data[3])
},
]}
}
return jsonify(**data)
def get_weather_arso_data(back_period="None"):
if back_period != "None":
back_period = int(back_period)
else:
back_period = get_setting("ENVIRONMENT_BACK_PLOT_PERIOD", int).value
temp = db.session.query(SensorData.date_time, SensorData.value).join(Sensors). \
filter(Sensors.id == SensorData.sensor_id).filter(Sensors.id == 44). \
order_by(SensorData.date_time.desc()).limit(back_period).all()
sun_radiance = db.session.query(SensorData.date_time, SensorData.value).join(Sensors). \
filter(Sensors.id == SensorData.sensor_id).filter(Sensors.id == 46). \
order_by(SensorData.date_time.desc()).limit(back_period).all()
hum = db.session.query(SensorData.date_time, SensorData.value).join(Sensors). \
filter(Sensors.id == SensorData.sensor_id).filter(Sensors.id == 45). \
order_by(SensorData.date_time.desc()).limit(back_period).all()
# print "Sensor_data:", data
scan_period = 30
temperature_data = []
sun_radiance_data = []
humidity_data = []
timestamp = []
timestamp_str = temp[0][0]
for i in range(back_period): # enumerate(node_1_dht_temp):
# Try - except every one because we don't know if all data is available for all sensors
try:
sun_radiance_data.append(
sun_radiance[i][1] if sun_radiance[i][1] is not None else None) # ????
except IndexError:
pass
try:
humidity_data.append(hum[i][1] if hum[i][1] is not None else None) # ????
except IndexError:
pass
try:
temperature_data.append(temp[i][1] if temp[i][1] is not None else None) # ????
except IndexError:
pass
try:
timestamp.append(time_since_epoch(sun_radiance[i][0]) * 1000)
except IndexError:
pass
series = {"data": [
{
"name": "Temperature [°C]",
"type": "line",
"data": zip(timestamp, temperature_data)[::-1],
# "color": "green",
"tooltip": {
"valueSuffix": ' °C'
}
},
{
"name": "Humidity [%]",
"type": "line",
"yAxis": 1,
"data": zip(timestamp, humidity_data)[::-1],
"color": "blue",
"tooltip": {
"valueSuffix": ' %'
}
}
],
"sun_radiance_data": [{
"name": "Sun Radiance [W/m2]",
"type": "line",
"data": zip(timestamp, sun_radiance_data)[::-1],
"color": "orange",
"tooltip": {
"valueSuffix": ' W/m2'
}
}],
"back_period": back_period,
"temperature_last_reading": temperature_data[0],
"sun_radiance_last_reading": sun_radiance_data[0],
"humidity_last_reading": humidity_data[0],
"temperature_average": stats.mean(temperature_data),
"sun_radiance_average": stats.mean(sun_radiance_data),
"humidity_average": stats.mean(humidity_data),
"last_update_time": datetime.datetime.strftime(timestamp_str, "%d.%m.%Y %H:%M:%S"),
"scan_period": scan_period
}
# print series["data"]
return jsonify(**series)
def get_environment_data(back_period="None"):
# print "test:", back_period, type(back_period), repr(back_period)
if back_period != "None":
back_period = int(back_period)
else:
back_period = get_setting("ENVIRONMENT_BACK_PLOT_PERIOD", int).value
ds_temp = db.session.query(SensorData.date_time, SensorData.value).join(Sensors). \
filter(Sensors.id == SensorData.sensor_id).filter(Sensors.id == 1). \
order_by(SensorData.date_time.desc()).limit(back_period).all()
node_1_ds_temp = db.session.query(SensorData.date_time, SensorData.value).join(Sensors). \
filter(Sensors.id == SensorData.sensor_id).filter(Sensors.id == 12). \
order_by(SensorData.date_time.desc()).limit(back_period).all()
node_1_dht_temp = db.session.query(SensorData.date_time, SensorData.value).join(Sensors). \
filter(Sensors.id == SensorData.sensor_id).filter(Sensors.id == 13). \
order_by(SensorData.date_time.desc()).limit(back_period).all()
node_1_dht_hum = db.session.query(SensorData.date_time, SensorData.value).join(Sensors). \
filter(Sensors.id == SensorData.sensor_id).filter(Sensors.id == 14). \
order_by(SensorData.date_time.desc()).limit(back_period).all()
# print "Sensor_data:", data
scan_period = get_setting("ENVIRONMENT_SCAN_PERIOD", float)
temperature_ds_data = []
node_1_temperature_ds_data = []
node_1_temperature_dht_data = []
node_1_humidity_dht_data = []
timestamp = []
timestamp_ds = []
node_timestamp_str = node_1_dht_temp[0][0]
timestamp_str = ds_temp[0][0]
for i in range(back_period): # enumerate(node_1_dht_temp):
# Try - except every one because we don't know if all data is available for all sensors
try:
node_1_temperature_ds_data.append(
node_1_ds_temp[i][1] if node_1_ds_temp[i][1] is not None else None) # ????
except IndexError:
pass
try:
node_1_temperature_dht_data.append(
node_1_dht_temp[i][1] if node_1_dht_temp[i][1] is not None else None) # ????
except IndexError:
pass
try:
node_1_humidity_dht_data.append(node_1_dht_hum[i][1] if node_1_dht_hum[i][1] is not None else None) # ????
except IndexError:
pass
try:
temperature_ds_data.append(ds_temp[i][1] if ds_temp[i][1] is not None else None) # ????
except IndexError:
pass
try:
timestamp_ds.append(time_since_epoch(ds_temp[i][0]) * 1000)
except IndexError:
pass
try:
timestamp.append(time_since_epoch(node_1_ds_temp[i][0]) * 1000)
except IndexError:
pass
series = {"data": [
{
"name": "DS18B20 T [°C]",
"type": "line",
"data": zip(timestamp_ds, temperature_ds_data)[::-1],
# "color": "green",
"tooltip": {
"valueSuffix": ' °C'
}
},
{
"name": "Node-1 DS18B20 T [°C]",
"type": "line",
"data": zip(timestamp, node_1_temperature_ds_data)[::-1],
# "color": "green",
"tooltip": {
"valueSuffix": ' °C'
}
},
{
"name": "Node-1 DHT22 T [°C]",
"type": "line",
"data": zip(timestamp, node_1_temperature_dht_data)[::-1],
# "color": "yellow",
"tooltip": {
"valueSuffix": ' °C'
}
},
{
"name": "Node-1 DHT22 H [%]",
"type": "line",
"yAxis": 1,
"data": zip(timestamp, node_1_humidity_dht_data)[::-1],
"color": "blue",
"tooltip": {
"valueSuffix": ' %'
}
}
],
"back_period": back_period,
"ds_last_reading": temperature_ds_data[0],
"node_ds_last_reading": node_1_temperature_ds_data[0],
"node_dht_temp_last_reading": node_1_temperature_dht_data[0],
"node_dht_hum_last_reading": node_1_humidity_dht_data[0],
"ds_average": stats.mean(temperature_ds_data),
"node_ds_average": stats.mean(node_1_temperature_ds_data),
"node_dht_temp_average": stats.mean(node_1_temperature_dht_data),
"node_dht_hum_average": stats.mean(node_1_humidity_dht_data),
"node_last_update_time": datetime.datetime.strftime(node_timestamp_str, "%d.%m.%Y %H:%M:%S"),
"ds_last_update_time": datetime.datetime.strftime(timestamp_str, "%d.%m.%Y %H:%M:%S"),
"scan_period": scan_period.value
}
# print series["data"]
return jsonify(**series)