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  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 / 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-Dauer
#   trans1: Übergang 0 bis 1 vom Zoom bis zum vollen Zeitraum
#   trans2: Übergang 0 bis 1 vom reduzierten bis zum vollen Datensatz
#   showTrend: Trend (0: None, 1: Ausschnitt, 2: gesamte Historie, 3: gesamter Zeitraum)

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('Vermeintlicher Stillstand der Erwärmung')
    plt.xlabel('Jahr')
    plt.ylabel('Relative globale Temperatur (°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 = 'Vermeintlicher_Stillstand_der_globalen_Erwaermung.gif'
imageio.mimsave(fileOut, images, 'GIF', duration=[i*1000 for i in duration])

# Optimize GIF size
from pygifsicle import optimize
optimize(fileOut, colors=20)