The attention paid to the phalluses of these bison far outranks the attention paid to the rest of their bodies, and particular emphasis is placed on the dorsal artery. A handful of those paintings, which date back perhaps 40,000 years, have captivated and perplexed anthropologists for decades because of their intricately detailed phalluses. This gravesite is usually connected to the impressive nearby cave paintings portraying traveling bison. If the above example isn't enough, check out the documentation, where you can find more examples.The earliest known remains of WUnderground members were discovered in the 1920s by a team of French archeologists in north western Anatolia, buried in a paleolithic gravesite alongside a community of early modern humans. The data for the plot came directly from SQL and could easily modify the query to get out any type of data desired. Rap.connect(database='weather', user='chef')Īnd then to plot the data on a map of the good 'ole USA: My goal was to be able to grab the latest forecast from the Rapid Refresh model (your best bet if you want accurate info on current weather): from forecasting import Model The code is all open source - you can modify it to better meet your needs (maybe you're forecasting for Mars?) or pull out little snippets for your project. A great option for doing all of this is the python forecasting module.ĭisclosure: I put together the code behind the module.
I wanted this data in a SQL database so that I could easily slice and dice it. The above snippet will get you a time series (every three hours) of data for the entire month of January, 1979! If you needed multiple locations or all of the months, the above code would easily be modified to accommodate. Lon_index = 200 # you could tie this to tmp2m.lon Lat_index = 200 # you could tie this to tmp2m.lat To give an example of how to get all of the temperature data for a particular location from the nomads website, try the following in python: from pydap.client import open_url For no particular reason, I started playing around with pydap. I'm a big python user, and either pydap or NetCDF seemed like good tools to use. To give an idea of the amount of data, their data goes all the way back to 1979! If you're looking for Canada and the US, the North American Regional Reanalysis dataset is probably your best answer.
I haven't tested it yet, but if you're having issues, try that!) (Note: A commenter indicated that you must now use https rather than http. The best source for all of that data seemed to be either the NCEP or NCDC NOMADS servers: I found myself asking this same question, and will share my experience for future Googlers.
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