import numpy import matplotlib.pyplot as plt import imageio  year_T = {     # https://data.giss.nasa.gov/gistemp/tabledata_v4/GLB.Ts+dSST.txt     # GLOBAL Land-Ocean Temperature Index in 0.01 degrees Celsius     # base period: 1951-1980     # sources:  GHCN-v4 1880-07/2021 + SST: ERSST v5 1880-07/2021     # using elimination of outliers and homogeneity adjustment     # Divide by 100 to get changes in degrees Celsius (deg-C).     # Year: Temperature (Column J-D, annual mean Temperature Jan to Dec)     1880: -18, 1881: -10, 1882: -12, 1883: -18, 1884: -29,     1885: -34, 1886: -32, 1887: -37, 1888: -18, 1889: -11,     1890: -36, 1891: -23, 1892: -28, 1893: -32, 1894: -31,     1895: -23, 1896: -12, 1897: -12, 1898: -28, 1899: -18,     1900:  -9, 1901: -16, 1902: -29, 1903: -38, 1904: -48,     1905: -27, 1906: -22, 1907: -39, 1908: -43, 1909: -49,     1910: -44, 1911: -44, 1912: -37, 1913: -35, 1914: -16,     1915: -15, 1916: -37, 1917: -46, 1918: -30, 1919: -28,      1920: -28, 1921: -19, 1922: -29, 1923: -27, 1924: -27,     1925: -22, 1926: -11, 1927: -22, 1928: -20, 1929: -36,     1930: -16, 1931: -10, 1932: -16, 1933: -28, 1934: -12,     1935: -20, 1936: -15, 1937:  -3, 1938:   0, 1939:  -2,     1940:  12, 1941:  18, 1942:   6, 1943:   9, 1944:  20,     1945:   9, 1946:  -7, 1947:  -3, 1948: -11, 1949: -11,     1950: -17, 1951:  -7, 1952:   1, 1953:   8, 1954: -13,     1955: -14, 1956: -19, 1957:   5, 1958:   6, 1959:   3,     1960: -3,  1961:   6, 1962:   3, 1963:   5, 1964: -20,     1965: -11, 1966:  -6, 1967:  -2, 1968:  -8, 1969:   5,     1970:   3, 1971:  -8, 1972:   1, 1973:  16, 1974:  -7,     1975:  -1, 1976: -10, 1977:  18, 1978:   7, 1979:  16,     1980:  26, 1981:  32, 1982:  14, 1983:  31, 1984:  16,     1985:  12, 1986:  18, 1987:  32, 1988:  39, 1989:  27,     1990:  45, 1991:  40, 1992:  22, 1993:  23, 1994:  31,     1995:  44, 1996:  33, 1997:  47, 1998:  61, 1999:  38,     2000:  39, 2001:  53, 2002:  63, 2003:  62, 2004:  53,     2005:  68, 2006:  64, 2007:  66, 2008:  54, 2009:  66,     2010:  73, 2011:  61, 2012:  65, 2013:  68, 2014:  75,     2015:  90, 2016: 101, 2017:  92, 2018:  85, 2019:  98,     2020: 101, 2021:  85, 2022:  89, 2023: 117, 2024: 128     }  x, y = (numpy.array(list(x()), dtype='d') for x in (year_T.keys, year_T.values)) y = y / 100  xMinFocus, xMaxFocus = 1998, 2012 i0 = x.tolist().index(xMinFocus) i1 = x.tolist().index(xMaxFocus) + 1  nPoly = 4 phi = numpy.array([x**i for i in range(nPoly)]) A = phi @ phi.T b = phi @ y c = numpy.linalg.solve(A, b) yPoly = c @ phi  phiHist = phi[:,:i1] A = phiHist @ phiHist.T b = phiHist @ y[:i1] c = numpy.linalg.solve(A, b) yPolyHist = c @ phi  nPoly = 3 phiF = phi[:nPoly,i0:i1] A = phiF @ phiF.T b = phiF @ y[i0:i1] c = numpy.linalg.solve(A, b) yPolyFocus = c @ phi[:nPoly] yMinTotal, yMaxTotal = numpy.min(y) - 0.02, numpy.max(y) + 0.02 xMinTotal, xMaxTotal = numpy.min(x), numpy.max(x) yMinFocus, yMaxFocus = numpy.min(y[i0:i1]) - 0.02, numpy.max(y[i0:i1]) + 0.02 plt.xlim(xMinFocus-0.1, xMaxFocus+0.5)  # Frame-Parameter: #   t: Frame duration #   trans1: transition 0 to 1 towards full time frame #   trans2: transition 0 to 1 towards full data set #   showTrend: Trend (0: None, 1: Zoom, 2: full history, 3: full time frame)  parameters = [ # (t, trans1, trans2, showTrend)     (1, 0.0, 0.0, 0),     (4, 0.0, 0.0, 1),     *[(0.1, t**2, 0.0, 1) for t in numpy.linspace(0,1,25)],     (1.0, 1.0, 0.0, 1),     (0.5, 1.0, 0.0, 0),     (1, 1.0, 0.0, 2),     *[(0.1, 1.0, t,2) for t in numpy.linspace(0,1,10)],     (0.5, 1.0, 1.0, 2),     (6, 1.0, 1.0, 3),     ]  images = [] duration = [] for t, trans1, trans2, showTrend in parameters:     duration.append(t)     zoom = 4*(1-trans1) + 1*trans1     fig = plt.figure(figsize=(5.1,3.7), dpi=100)     plt.rc('axes', titlesize=14, labelsize=12)     plt.rc('xtick', labelsize=11)     plt.rc('ytick', labelsize=11)     plt.rc('legend', fontsize=16)     if showTrend == 1: plt.plot(x[i0-15:], yPolyFocus[i0-15:], 'r--', label='Trend')     if showTrend == 2: plt.plot(x, yPolyHist, 'b--', label='Trend')     if showTrend == 3: plt.plot(x, yPoly, 'b--', label='Trend')     iMax = int(i1 + trans2*(len(x)-i1))     plt.plot(x[:iMax], y[:iMax], 'C0.-', alpha=0.8, linewidth=0.8*zoom, markersize=6*zoom)     plt.plot(x[i0:i1], y[i0:i1], 'C3.-', linewidth=0.805*zoom, markersize=6.05*zoom)     plt.grid(True, alpha=0.7)     yMax = yMaxFocus + trans1*(yMaxTotal-yMaxFocus)     xMax = xMaxFocus + trans1*(xMaxTotal-xMaxFocus)     xMin = xMinFocus*(1-trans1) + xMinTotal*trans1     plt.xlim(xMin-0.1, xMax+0.1+1*trans1)     plt.ylim(yMinFocus*(1-trans1) + yMinTotal*trans1, yMaxFocus*(1-trans1) + yMax*trans1+0.03*trans1)     plt.text(0.02, 0.89, '%i - %i'%(xMin, x[iMax-1]), transform=plt.gca().transAxes, fontsize=20)     plt.title('Global Warming Hiatus')     plt.xlabel('Year')     plt.ylabel('Relative Global Temperature (°C)')     plt.gca().yaxis.set_label_coords(-0.13, 0.5)     if showTrend: leg = plt.legend(frameon=False, loc='lower right')     fig.subplots_adjust(         top=0.9,         bottom=0.13,         left=0.15,         right=0.95,         hspace=0.2,         wspace=0.2     )     fig.canvas.draw()     s, (width, height) = fig.canvas.print_to_buffer()     images.append(numpy.array(list(s), numpy.uint8).reshape((height, width, 4)))     fig.clf()     plt.close('all')  # Save GIF animation fileOut = 'Global_warming_hiatus.gif' imageio.mimsave(fileOut, images, 'GIF', duration=[i*1000 for i in duration])  # Optimize GIF size from pygifsicle import optimize optimize(fileOut, colors=20, options=["--loop"])