# This source code is public domainimport numpyimport matplotlib.pyplot as pltimport imageioyear_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  J-D (annual mean Temperature Jan to Dec)    1880: -16, 1881:  -8, 1882: -11, 1883: -17, 1884: -28,    1885: -33, 1886: -31, 1887: -36, 1888: -17, 1889: -10,    1890: -35, 1891: -22, 1892: -27, 1893: -31, 1894: -30,    1895: -22, 1896: -11, 1897: -11, 1898: -27, 1899: -17,    1900:  -8, 1901: -15, 1902: -28, 1903: -37, 1904: -47,    1905: -26, 1906: -22, 1907: -38, 1908: -43, 1909: -48,    1910: -43, 1911: -44, 1912: -36, 1913: -34, 1914: -15,    1915: -14, 1916: -36, 1917: -46, 1918: -30, 1919: -28,    1920: -27, 1921: -19, 1922: -29, 1923: -27, 1924: -27,    1925: -22, 1926: -11, 1927: -22, 1928: -20, 1929: -36,    1930: -16, 1931:  -9, 1932: -16, 1933: -29, 1934: -13,    1935: -20, 1936: -15, 1937:  -3, 1938:   0, 1939:  -2,    1940:  13, 1941:  19, 1942:   7, 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:  45, 1996:  33, 1997:  46, 1998:  61, 1999:  38,    2000:  39, 2001:  53, 2002:  63, 2003:  62, 2004:  53,    2005:  67, 2006:  63, 2007:  66, 2008:  54, 2009:  65,    2010:  72, 2011:  61, 2012:  65, 2013:  67, 2014:  74,    2015:  90, 2016: 101, 2017:  92, 2018:  85, 2019:  97,    2020: 102, 2021:  85, 2022:  89, 2023: 117    }    x, y = (numpy.array(list(x()), dtype='d') for x in (year_T.keys, year_T.values))y = y / 100xMinFocus, xMaxFocus = 1998, 2012i0 = x.tolist().index(xMinFocus)i1 = x.tolist().index(xMaxFocus) + 1nPoly = 4phi = numpy.array([x**i for i in range(nPoly)])A = phi @ phi.Tb = phi @ yc = numpy.linalg.solve(A, b)yPoly = c @ phiphiHist = phi[:,:i1]A = phiHist @ phiHist.Tb = phiHist @ y[:i1]c = numpy.linalg.solve(A, b)yPolyHist = c @ phinPoly = 3phiF = phi[:nPoly,i0:i1]A = phiF @ phiF.Tb = phiF @ y[i0:i1]c = numpy.linalg.solve(A, b)yPolyFocus = c @ phi[:nPoly]yMinTotal, yMaxTotal = numpy.min(y) - 0.02, numpy.max(y) + 0.02xMinTotal, xMaxTotal = numpy.min(x), numpy.max(x)yMinFocus, yMaxFocus = numpy.min(y[i0:i1]) - 0.02, numpy.max(y[i0:i1]) + 0.02plt.xlim(xMinFocus-0.1, xMaxFocus+0.1)# 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 animationfileOut = 'Global_warming_hiatus.gif'imageio.mimsave(fileOut, images, duration=duration)# Optimize GIF sizefrom pygifsicle import optimizeoptimize(fileOut, colors=20)