%matplotlib inline

tugmeteo

TÜBİTAK National Observatory (TUG) Meteorology Library

A library where instant and historical meteorological data can be obtained.

Examples

import matplotlib.pyplot as plt
from tugmeteo import TugMeteo

Basic operations

met = TugMeteo()

met.get_last_meteo('T100')

{'timestamp': '2020-01-21T23:20:41',
 'telescope': 'T100',
 'TEMPERATURE': -6.0,
 'Inside Temperature': -4.2,
 'HUMIDITY': 32.0,
 'Inside Humidity': 59.0,
 'PRESSURE': 750.4,
 'WINDSPEED': 72.4,
 'WINDDIR': 61.0,
 'RAIN': 0.0,
 'UV': 0.0,
 'Solar Radiation': 0.0,
 'Wind Chill': -17.9,
 'Dew Point': -20.0,
 'High Temperature': -5.5,
 'Low Temperature': -13.4,
 'High Humidity': 89.0,
 'Low Humidity': 22.0,
 'High Barometer': 751.5,
 'Low Barometer': 743.1,
 'High Wind': 143.0,
 'Air Density': 0.978,
 'Est. Cumulus Base': 1751.0}

met.get_temperature()

{'timestamp': '2020-01-21T23:20:42',
 'info': 'Temperature',
 'unit': 'C',
 'RTT150': -6.4,
 'T100': -6.0,
 'T60': -6.6}

Getting meteo archive between ‘2020-01-18’ and ‘2020-01-22’ dates

t = met.get_meteo_archives(telescope='T100', start_date='2020-01-18', end_date='2020-01-22')

	Timestamp	Temp	Chill	HIndex	Humid	Dewpt	Wind	HiWind	WindDir	Rain	Barom	Solar	ET	UV
0	2020-01-18 00:05:00	-4.5	-9.0	-4.5	78	-7.7	11	16	315	0.0	748.1	0	0.000	0.0
1	2020-01-18 00:10:00	-4.4	-8.5	-4.4	78	-7.7	10	13	315	0.0	748.1	0	0.000	0.0
2	2020-01-18 00:15:00	-4.6	-8.6	-4.6	79	-7.6	10	13	315	0.0	748.1	0	0.000	0.0
3	2020-01-18 00:20:00	-4.6	-8.6	-4.6	80	-7.5	10	13	315	0.0	748.3	0	0.000	0.0
4	2020-01-18 00:25:00	-4.4	-8.9	-4.4	79	-7.5	11	13	315	0.0	748.2	0	0.000	0.0
…	…	…	…	…	…	…	…	…	…	…	…	…	…	…
1136	2020-01-21 23:00:00	-6.4	-18.4	-6.4	31	-20.7	72	82	68	0.0	750.4	0	0.025	0.0
1137	2020-01-21 23:05:00	-6.2	-18.0	-6.2	33	-19.8	71	82	68	0.0	750.2	0	0.000	0.0
1138	2020-01-21 23:10:00	-6.2	-18.1	-6.2	33	-19.8	71	84	68	0.0	750.4	0	0.000	0.0
1139	2020-01-21 23:15:00	-6.2	-18.3	-6.2	32	-20.2	74	87	68	0.0	750.3	0	0.000	0.0
1140	2020-01-21 23:20:00	-6.0	-17.8	-6.0	32	-20.0	71	84	68	0.0	750.4	0	0.000	0.0

1141 rows × 14 columns

Plotting

fig, ax = plt.subplots(figsize=(12, 9), dpi=72)
ax.grid()
ax.set_title('T100 - Weather Station')
ax.set_xlabel('Dates')
ax.set_ylabel('Barometer [mBar]')
press, = ax.plot(t.Timestamp, t.Barom, 'k.', ms=2)
ax1 = ax.twinx()
ax1.set_ylabel('Temperature [C]')
temp, = ax1.plot(t.Timestamp, t.Temp, 'r.', ms=1)
ax.legend(handles=[press, temp], labels=['Pressure', 'Temperature'], loc='upper right')

png

import numpy as np
from scipy.stats import gaussian_kde
temp = t.Temp
bar = t.Barom
xmin = temp.min()
xmax = temp.max()
ymin = bar.min()
ymax = bar.max()
X, Y = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]
positions = np.vstack([X.ravel(), Y.ravel()])
values = np.vstack([temp, bar])
kernel = gaussian_kde(values)
Z = np.reshape(kernel(positions).T, X.shape)
fig, ax = plt.subplots(figsize=(12, 9))
# ax.grid()
ax.set_title('Density Map Between 2020-01-18 and 2020-01-22', fontsize=17)
ax.set_xlabel('Temperature [C]', fontsize=15)
ax.set_ylabel('Barometer [mBar]', fontsize=15)
ax.imshow(np.rot90(Z), cmap=plt.cm.gist_earth, extent=[xmin, xmax, ymin, ymax])
ax.plot(temp, bar, 'k.', markersize=1)
ax.set_xlim([xmin, xmax])
ax.set_ylim([ymin, ymax])

(742.4, 751.5)

png