
# co2ampel_plot.py

"""
This script gets data from the "fr_co2ampel_hft" LoRaWAN application by the
"Data Storage Integration".  The data is stored at the TTN server for 7 days.

Further information:

- https://www.thethingsnetwork.org/docs/applications/storage/api/index.html
- https://console.thethingsnetwork.org/applications/fr_co2ampel_hft/integrations
- https://fr_co2ampel_hft.data.thethingsnetwork.org
- https://fr_co2ampel_hft.data.thethingsnetwork.org/swagger.yaml

Authors:
Franz Refle, 2021 (original work)
Hubert Högl, 2021, <Hubert.Hoegl@hs-augsburg.de>
"""

import os
import requests
import sys
import pandas as pd
import numpy as np
from datetime import datetime
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator
from matplotlib.dates import DayLocator, HourLocator, DateFormatter, drange
import matplotlib.dates as mdates

url = "https://fr_co2ampel_hft.data.thethingsnetwork.org/api/v2/query/co2ampelbndlg_dev01"
args = '?last=7d'

# You need an access key to get the data. Go to 
# https://console.thethingsnetwork.org/applications/fr_co2ampel_hft
# at the bottom you see "ACCESS KEYS". Set environment variable ACCESSKEY to 
# this key with the command "export ACCESSKEY=..."
if "ACCESSKEY" not in os.environ:
    raise(ValueError('environment variable ACCESSKEY not set'))
else:
    access_key = os.environ["ACCESSKEY"]
    
# print(access_key)

headers = {'Accept': 'application/json', 'Authorization': 'key ' + access_key}

# Get data by Swagger UI (OpenAPI)
try:
    response = requests.get(url + args, headers=headers)
except OSError as e:
    print("Error: {0}".format(e))
    sys.exit(0)

if response.status_code == 200:
    print("Status 200, OK")
    data = response.json()
else:
    print("Error (response.status_code is {})".format(response.status_code))
    sys.exit(0)

# Time format in data: 2021-03-30T13:53:03.742880288Z 
# the 9 digit nsec fraction can not be parsed with strptime(). Cut off the last 
# four chars to get a microsecond fraction.
begin_date = data[0]['time'][:-4] 
end_date = data[-1]['time'][:-4]

fmt = "%Y-%m-%dT%H:%M:%S.%f" # %f is a 6 digit microsecond fraction
dt1 = datetime.strptime(begin_date, fmt)
dt2 = datetime.strptime(end_date, fmt)
d1 = dt1.date()
d2 = dt2.date()

df = pd.DataFrame(data)

# df DataFrame:
#    co2   device_id           hum   raw           time                   tmp
#0  1040  co2ampelbndlg_dev01  38.0  NJdM  2021-03-30T10:22:01.460562325Z 20.2
#1  1020  co2ampelbndlg_dev01  37.5  M5hL  2021-03-30T10:27:10.143553097Z 20.4
#2  1020  co2ampelbndlg_dev01  37.5  M5hL  2021-03-30T10:32:18.711297525Z 20.4
#...

# https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to_datetime.html
df['time'] = pd.to_datetime(df['time'])

ts = df.set_index('time')

cm = 1/2.54  # inch to cm

# Place 3 subplots in one large plot on a A4 page
fig, axes = plt.subplots(3, figsize=(18*cm, 26*cm)) 
# print("fig:", type(fig), fig)

# Put format placeholders in single {...}, LaTeX commands in double {{...}}! 
fig.suptitle(r"""CO2 Sensor von Franz Refle ({begin} - {end})
   ($\it{{co2ampel\_plot.py}}$)
""".format(begin=d1, end=d2))

# ~~~ snippet from https://matplotlib.org/stable/gallery/ticks_and_spines/date_concise_formatter.html
for nn, ax in enumerate(axes):
    locator = mdates.AutoDateLocator()
    formatter = mdates.ConciseDateFormatter(locator)
    formatter.formats = ['%y',  # ticks are mostly years
                         '%b',       # ticks are mostly months
                         '%d',       # ticks are mostly days
                         '%H:%M',    # hrs
                         '%H:%M',    # min
                         '%S.%f', ]  # secs
    # these are mostly just the level above...
    formatter.zero_formats = [''] + formatter.formats[:-1]
    # ...except for ticks that are mostly hours, then it is nice to have
    # month-day:
    formatter.zero_formats[3] = '%d-%b'

    formatter.offset_formats = ['',
                                '%Y',
                                '%b %Y',
                                '%d %b %Y',
                                '%d %b %Y',
                                '%d %b %Y %H:%M', ]
    ax.xaxis.set_major_locator(locator)
    ax.xaxis.set_major_formatter(formatter)

    # need minor ticks at multiples of 6 hours 
    # https://www.geeksforgeeks.org/matplotlib-axis-axis-set_minor_locator-function-in-python/
    ax.xaxis.set_minor_locator(HourLocator(range(0, 25, 6)))
# ~~~

axes[0].set_title("CO2 [ppm]")
axes[1].set_title("Luftfeuchtigkeit [%]")
axes[2].set_title("Temperatur [℃]")

ts.plot(subplots=True, ax=[axes[0], axes[1], axes[2]], grid=True)

Ticks = axes[0].get_yticks()
axes[0].set_yticks(np.arange(0, 1400, 200))
Ticks = axes[0].get_yticks()
# print("Ticks[0] =", Ticks)
axes[0].yaxis.set_minor_locator(MultipleLocator(50))
axes[0].set_xlabel("") # remove "time"
# rotation not needed for concise date format
# for label in axes[0].get_xticklabels():
#    label.set_rotation(20)
#    label.set_horizontalalignment('right')

Ticks = axes[1].get_yticks()
# print("Ticks[1] =", Ticks)
axes[1].yaxis.set_minor_locator(MultipleLocator(1.0))
axes[1].set_xlabel("") # remove "time"

Ticks = axes[2].get_yticks()
# print("Ticks[2] =", Ticks)
axes[2].yaxis.set_minor_locator(MultipleLocator(1.0))
axes[2].set_xlabel("") # remove "time"

# https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.subplots_adjust.html
# wspace = width reserved, hspace = height reserved
plt.subplots_adjust(wspace=0.5, hspace=0.5)

plotfile = "plot.jpg"
#plt.savefig(plotfile)
#print("see", plotfile)
plt.show()

Status 200, OK

IoT in der Praxis mit LoRaWAN¶

Akademie Dillingen¶

20. Nationaler Akademietag - 23. April 2021¶

Zeitplan¶

Vom Sensor zur Auswertung¶

Übersicht zu IoT und LoRaWAN¶

LPWAN¶

Vergleich einiger technischer Daten¶

Wie wenig Energie ist Low Energy?¶

LoRa¶

LoRaWAN¶

LoRaWAN Infrastruktur¶

MQTT¶

Integrations¶

Beispiel: Integration TTN Mapper¶

TTN Europa¶

TTN Community Augsburg¶

TTN Community Augsburg (2)¶

Sehr grosse TTN Communities¶

Praxisteil 1 - Hardware der CO2 Ampel¶

Praxisteil 2 - Software für CO2 Ampel kompilieren¶

Praxisteil 3 - Gerät auf LoRaWAN Server (TTN) eintragen¶

Praxisteil 4 - Auf die Daten zugreifen¶

Data Storage Integration mit Plot¶

MQTT / InfluxDB¶

Open-Source Werkzeuge für den Workshop¶

Literatur/Links¶