'''script to scrape and plot the temperatures for the year to date (2017)''' from year import Year from visualisation.scattergl_plot import plot_large_scatter #plot temperatures of year to date (2017) #initiate a 'year object' and load the data from the web yeartwelve = Year('17') yeartwelve.load_all_data() #assign the temperature and date data into variables yeartemps and yeartimes yeartemps = yeartwelve.get_all_temps() yeartimes = yeartwelve.get_all_datetimes() #plot using plotly plot_large_scatter(yeartimes, yeartemps, 'Date', 'Temperature (C)', 'yeartemps')
import numpy as np from sklearn import linear_model from visualisation.scattergl_plot import plot_two_scatters from linear_regression_ttest import get_slope_t_statistic, get_two_sided_p_value #create list of dates in two character string format (corresponds to the format of the web files) YEARINTS = range(2, 17) #years from 2002 to 2016 inclusive YEARLIST = [str(yr).zfill(2) for yr in YEARINTS] # string format of years #load all years' data average_temperatures = [] year_names = [] for year_string in YEARLIST: print year_string year_object = Year(year_string) year_object.load_all_data() temperatures = year_object.get_all_temps() average_temperatures.append(np.average(temperatures)) year_names.append(float(year_string) + 2000) print average_temperatures # Create linear regression object regr = linear_model.LinearRegression() x_values = np.reshape(YEARINTS, (len(YEARINTS), 1)) # Fit the data regr.fit(x_values, average_temperatures) # make the best-fit line ("prediction") y_pred = regr.predict(x_values) print y_pred