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space weather dashboard build your own custom dashboard to analyze and predict weather
import sqlite3
conn = sqlite3.connect("space.db", isolation_level=None)
cur = conn.cursor()cur.execute('''
CREATE TABLE sunspots (
id INTEGER PRIMARY KEY AUTOINCREMENT,
date DATE,
sunspot_count INTEGER,
sunspot_sd REAL,
sunspot_obs_no INTEGER
);
''')
Source: www.analyticsvidhya.com
space weather dashboard build your own custom dashboard to analyze and predict weather
for file in file_list:
station = ''
lat = 0
long = 0
date_today = str(now.year) + str(now.strftime("%m")) + str(now.strftime("%d"))
if(date_today in file):
ftp.retrbinary("RETR " + file, open(file, 'wb').write)
temp=open(file, 'rb')
data_rows = 0 for line in temp:
if(data_rows == 1):
row_bytes = line.split()
date_time = row_bytes[0].decode("utf-8") + " " + row_bytes[1].decode("utf-8")[:8]
row_txt = [date_time, row_bytes[3].decode("utf-8"), row_bytes[4].decode("utf-8"), row_bytes[5].decode("utf-8"), row_bytes[6].decode("utf-8")]
a_series = pd.Series(row_txt, index = df.columns)
query = 'INSERT INTO geo_mag (station, lat, long, date_time, bx, by, bz, bf) VALUES ("%s", "%s", "%s", "%s", "%s", "%s", "%s", "%s")' % (station, lat, long, a_series["Date_time"], a_series["Bx"], a_series["By"], a_series["Bz"], a_series["Bf"]) cur.execute(query)else:
if('IAGA Code' in line.decode("utf-8") or 'IAGA CODE' in line.decode("utf-8")):
station = line.decode('utf-8').split()[2]
print(station)
elif('Latitude' in line.decode("utf-8")):
lat = line.decode('utf-8').split()[2]
elif('Longitude' in line.decode("utf-8")):
long = line.decode('utf-8').split()[2]
elif('DATE TIME' in line.decode("utf-8")):
data_rows = 1 conn.commit()
Source: www.analyticsvidhya.com
space weather dashboard build your own custom dashboard to analyze and predict weather
import ftplib#Open ftp connection
ftp = ftplib.FTP('ftp.seismo.nrcan.gc.ca', 'anonymous',
'user')#List the files in the current directory
print("File List:")
files = ftp.dir()import datetimenow=datetime.datetime.now()
ftp.cwd("intermagnet/minute/provisional/IAGA2002/" + str(now.year) + "/" + str(now.strftime("%m")))
# files = ftp.dir()
Source: www.analyticsvidhya.com
space weather dashboard build your own custom dashboard to analyze and predict weather
import json
import urlliburl_mag="https://services.swpc.noaa.gov/products/solar-wind/mag-7-day.json"
url_plasma="https://services.swpc.noaa.gov/products/solar-wind/plasma-7-day.json"mag=urllib.request.urlopen(url_mag)
plasma=urllib.request.urlopen(url_plasma)mag_json=json.loads(mag.read())
plasma_json=json.loads(plasma.read())
Source: www.analyticsvidhya.com
space weather dashboard build your own custom dashboard to analyze and predict weather
for line in fileobject:
row_bytes = line.split()
date = row_bytes[0].decode("utf-8") + "-" + row_bytes[1].decode("utf-8") + "-" + row_bytes[2].decode("utf-8") row_txt = [date, row_bytes[4].decode("utf-8"), row_bytes[5].decode("utf-8"), row_bytes[6].decode("utf-8")] a_series = pd.Series(row_txt, index = df.columns)
query = 'INSERT INTO sunspots (date, sunspot_count, sunspot_sd, sunspot_obs_no) VALUES ("%s", "%s", "%s", "%s")' % (a_series["Date"], a_series["Sunspot_count"], a_series["Sunspot_sd"], a_series["Observ_No"]) cur.execute(query)
Source: www.analyticsvidhya.com
space weather dashboard build your own custom dashboard to analyze and predict weather
import pandas as pd
import numpy as npdf = pd.DataFrame(columns = ["Date", "Sunspot_count", "Sunspot_sd", "Observ_No"])
Source: www.analyticsvidhya.com
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