Now, typing wttr in the terminal and pressing Enter should execute your custom command. Save and close ~/.bashrc and run the command below to source the new file. Go to the end and paste in alias wttr='curl wttr.in/CITY_NAME?YOUR_PARAMS' The thicker line is a nine-year weighted average. ![]() A value between -2 and -3 indicates moderate drought, -3 to -4 is severe drought, and -4 or below indicates extreme drought. I bought a screen printing kit from a local art supply store. Positive values represent wetter-than-average conditions, while negative values represent drier-than-average conditions. Basically I wanted to learn how to screen print too. I understand that it is probably the most difficult way but I think it looks the best. ![]() ![]() To do so, open ~/.bashrc with your favorite editor (that’s vim, terminal wizard). Download Step 2: Screen Printing the Graphics I could have used a variety of methods to print on the graphics but I decided to screen print them on. If you found some settings you enjoy and you find yourself using them frequently, you might want to add an alias. You can specify location and parameters like so: curl wttr.in/london?m Open up a terminal and install Curl in Ubuntu with this command: sudo apt install curl You can specify location (by default the app tries to detect your current location) and a few other parameters (eg. Wrap yarn about 25 times around the long side of the cardboard. This will help us prepare and analyze the data all along the experiment.If you really live in the terminal, this is the weather app for you. To start things off, we pulled the Excel file data into Alteryx, a data science tool to create end-to-end data pipelines. Now let’s try to simplify our dataset to make it easier to analyze. Let the analysis begin! Data Transformation We now have 370 measurements, which is not ideal, but sufficient to start doing some analysis. So we decided to repeat the exact same process for 4 other cities of northern California around San Jose which were subject to the same type of weather but were far enough not to have redundant data (taking San Francisco for instance, which is by the sea would have biased everything, and taking Milpitas, which is in San Jose suburbs wouldn’t have added any relevant data). The bottom will later be glued onto the lid. The lid will also house the electronics and has a separate bottom which separates the electronics from the cottonwool, so you cannot see the electronics when looking through the vase. So, even if we went back to 1945, that represented only 73 lines… which is far too little data to pretend to do any sort of reliable analysis or prediction ( a couple hundreds would be much better). A lid will cover the vase to give it a nicer appearance. La Crosse Technology 308-1414MB-INT Wireless Color Weather Station with Mold Indicator, Black. In fact, when you think about it, each line of our Excel sheet represented 1 year (average temp of summer + 3 days of May). Weather Station, 3 in 1 Yard Weather Indicator with Wind Direction Arrow, Rain Gauge,Garden Thermometer for Precipitation. The first 3 columns are the 3 days of May, and the last one is the average summer temperatureīut (there’s always a “but”), that was not enough. What our dataset looks like now (temperatures are in Fahrenheit). Amazing, the job is done then! Well, not exactly… The only source of information we found was on a website, the old Farmer’s Almanac, which listed the average temperatures of each day since 1945. Guess what? There were none that were either complete enough or simply available to us □. So we started looking for databases or archives of historical temperature data. What data do we want exactly? Remember our objective: predict the average summer temperature following the temperatures of 3 days in May (19th, 20th and 21st) for the city of San Jose. In any data science problem, the starting point of anything is data. Nevertheless, we got started and tried to understand how we could go about proving such an original statement. Frankly, we did not really believe in it and approached this task with great scepticism. You read that correctly, predict the average weather of 3 months based on 3 days. He told us that he was able to predict if the summer was going to be hot or not solely based on the temperatures of the 19th, 20th and 21st of May. ![]() So we asked our mentor Plamen Nedeltchev (Distinguished Engineer at Cisco) if he had anything in stock and he shared with us that he had a theory about the weather in San Jose, CA. My colleague Aouss Sbai (co-author of this article) and I were looking for a fun project to work on. The USA during a heat wave What are we trying to do?īefore describing what this experiment is all about, I need to give you some context.
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