Plotting Sounding Data from University of Wyoming's Website

Plotting sounding data can be difficult, but it is made easy with this python module: https://pypi.python.org/pypi/SkewT

It requires a .txt file with data in the format of the University of Wyoming sounding data archive (example) or you can create your own dictionary.

Here I grab the sounding data from the University of Wyoming's website and process it with the following steps:

1. Use urllib2 to open the url and read the data
2. Parse out the html tags using BeautifulSoup
3. Separate the text by a new line "\n"
4. Write each line of text to a new .txt file reinserting the new line with +"\n" and skipping the first three lines of the file (this puts it in the same format as the University of Wyoming website)
5. Draw the Skew-T Plot (NOTE: requires the SkewT package here)

Here is the download function on github. 
As always, please share other solutions you may come up with :)

# Brian Blaylock

# July 6, 2015

# Download, process, and Plot Sounding Data from University of Wyoming

import urllib2
from bs4 import BeautifulSoup
from skewt import SkewT

stn = '72572' #72572 is ID for SLC
year= '2015'
month = '06'
day = '12'
hour = '12' #either 12 or 00

# 1)
# Wyoming URL to download Sounding from
url = 'http://weather.uwyo.edu/cgi-bin/sounding?region=naconf&TYPE=TEXT%3ALIST&YEAR='+year+'&MONTH='+month+'&FROM='+day+hour+'&TO='+day+hour+'&STNM='+stn
content = urllib2.urlopen(url).read()

# 2)
# Remove the html tags
soup = BeautifulSoup(content)
data_text = soup.get_text()

# 3)
# Split the content by new line.
splitted = data_text.split("\n",data_text.count("\n"))

# 4)
# Write this splitted text to a .txt document
Sounding_filename = str(stn)+'.'+str(year)+str(month)+str(day)+str(hour)+'.txt'
f = open(Sounding_filename,'w')
for line in splitted[4:]:
    f.write(line+'\n')
f.close()

# 5)
sounding = SkewT.Sounding(filename=Sounding_filename)
sounding.plot_skewt()


And if you wish to plot more than one sounding on the same plot do this...
S = SkewT.Sounding(filename=Sounding_filename)
T = SkewT.Sounding(filename="72572.2015061212.txt")
S.make_skewt_axes(tmax=55)
S.add_profile(color='r',linewidth=5,bloc=0)
S.soundingdata=T.soundingdata
S. add_profile(color="b",bloc=1)

Python and MesoWest API

Uses the MesoWest API to get ozone concentration data and plot them. An example of formatting plot datetime plot tick marks is also shown.

# Brian Blaylock
# June 9, 2015

# Uses the MesoWest API to get find the maximum ozone
# from in-situ stations during GSLSO3S

# Tips for creating API request:
# -look at MesoWest API documentation: http://mesowest.org/api/docs/
# -use JSON viewer to see request results: http://jsonviewer.stack.hu/


import json
import numpy as np
import urllib2
import matplotlib.pyplot as plt
from datetime import datetime
from matplotlib.dates import DateFormatter, YearLocator, MonthLocator, DayLocator, HourLocator

token = '1234567890' #contact mesowest@lists.utah.edu to get your own token.

station = 'mtmet'
# dateformat YYYYMMDDHHMM
start_time = '201506050000'
end_time = '201506060000'
variables = 'ozone_concentration'
time_option = 'local'
URL = 'http://api.mesowest.net/v2/stations/timeseries?stid='+station+'&start='+start_time+'&end='+end_time+'&vars='+variables+'&obtimezone='+time_option+'&token='+token

#Open URL and read the content
f = urllib2.urlopen(URL)
data = f.read()

# convert that json string into some python readable format
data = json.loads(data)

stn_name = data['STATION'][0]['NAME']
# get ozone data convert to numpy array
ozone = data["STATION"][0]["OBSERVATIONS"]["ozone_concentration_set_1"]
#convert ozone into a numpy array: setting dtype=float replaces None value with a np.nan
ozone = np.array(ozone,dtype=float)

# get date and times and convert to datetime and put into a numpy array
dates = data["STATION"][0]["OBSERVATIONS"]["date_time"]
DATES = np.array([]) # first make an empty array
for i in dates:
   if time_option=='utc':
      converted_time = datetime.strptime(i,'%Y-%m-%dT%H:%M:%SZ')
   else:
      converted_time = datetime.strptime(i,'%Y-%m-%dT%H:%M:%S-0600')
   DATES = np.append(DATES,converted_time)


# make a simple ozone time series
#----------------------------------------------------------
ax = plt.subplot(1,1,1)
plt.plot(DATES,ozone)
plt.title('Ozone Concentration (ppb) for '+stn_name)
plt.xlabel('date')
plt.xticks(rotation=30)
#Now we format the date ticks
# Format Ticks
# Find months
months = MonthLocator()
# Find days
days = DayLocator()

# Find each 0 and 12 hours
hours = HourLocator(byhour=[0,6,12,18])
# Find all hours
hours_each = HourLocator()
# Tick label format style
dateFmt = DateFormatter('%b %d\n%H:%M')
# Set the x-axis major tick marks
ax.xaxis.set_major_locator(hours)
# Set the x-axis labels
ax.xaxis.set_major_formatter(dateFmt)
# For additional, unlabeled ticks, set x-axis minor axis
ax.xaxis.set_minor_locator(hours_each)


plt.savefig(start_time+'_'+end_time+'.png', format='png')