.. _news-meteoinfo_4.2.0:


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MeteoInfo 4.2.0 was released (2026-3-21)
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  - Add `loc` and `sel` functions in `DimVariable` and `DimArray` classes for selecting data using label-based indexing
  - Including new developed `symjy` toolbox for symbol calculation
  - Add `lifted_index` and `precipitable_water` functions in `meteolib` package
  - Add `cross` function in `linalg` package
  - Add `clip`, `bitwise_and`, `bitwise_or` and `bitwise_xor` functions in `numeric.core` package
  - Add faces and vertices data support in `patch` plot function
  - Add `PolyCollection` class in `plotlib` package
  - Support micaps mdfs 125 type data file
  - Improve `slice3` function to support 2D data array
  - Improve geotiff data reading for predictor tag
  - Update FlatlLaf to version 3.6.1
  - Update JOGL to version 2.6.0
  - Some bug fixed


select data with `sel` function
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::

    fn = 'D:/Temp/nc/sst.mon.anom.nc'
    f = addfile(fn)
    print(f.time[0])
    dt = f.dims.time.dt
    syear = 1979; eyear = 2020
    tidx = (dt.year >= syear) & (dt.year <= eyear) & (dt.month == 1)
    sst = f.sst.sel(time=tidx, lat=slice(-30,30), lon=slice(160,300))
    lats = f.lat.sel(lat=slice(-30,30))
    lons = f.lon.sel(lon=slice(160,300))

    # Create an EOF solver to do the EOF analysis. Square-root of cosine of
    # latitude weights are applied before the computation of EOFs.
    coslat = np.cos(np.deg2rad(lats))
    wgts = np.sqrt(coslat)[..., np.newaxis]
    solver = meteolib.EOF(sst, weights=wgts)

    # Retrieve the leading EOF, expressed as the correlation between the leading
    # PC time series and the input SST anomalies at each grid point, and the
    # leading PC time series itself.
    eof1 = solver.eofs_correlation(neofs=1)
    pc1 = solver.pcs(npcs=1, pcscaling=1)
    e1 = solver.variance_fraction(1)[0]

    #Plot
    subplot(2,1,1,axestype='map')
    geoshow('continent', facecolor='w')
    levs = arange(-0.8, 1, 0.2)
    layer = contourf(lons, lats, eof1.squeeze(), levs, extend='both',
        cmap='matlab_jet', smooth=False, zorder=0)
    yticks(arange(-20, 61, 40))
    colorbar(layer)
    title('EOF mode 1 expressed as correlation (%.1f%%)' % (e1*100))

    subplot(2,1,2)
    years = range(syear, eyear+1)
    lines = plot(years, pc1, color='b', linewidth=2, antialias=True)
    y = zeros(len(years))
    plot(years, y, color='k')
    xlim(syear-1, eyear+1)
    ylim(-3,3)
    xticks(arange(1970,2021,10))
    xlabel('Year')
    ylabel('Normalized Units')
    title('PC1 Time Series')

.. image:: ../_static/eof_sel_example.png


usage examples for symjy toolbox
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::

    from symjy import *

    # Define symbols
    x, y, z = symbols('x y z')
    a, b, c = symbols('a b c', real=True)

    # Basic examples
    # Example 1: Basic symbolic expressions
    expr1 = x**2 + 2*x + 1
    print('Expression: {}'.format(expr1))
    print("# Expected: x**2 + 2*x + 1")
    print

    # Example 2: Expansion
    expr2 = (x + 1)**3
    expanded = expand(expr2)
    print("Expansion of (x+1)^3: {}".format(expanded))
    print("# Expected: x**3 + 3*x**2 + 3*x + 1")
    print

    # Example 3: Factorization
    expr3 = x**2 - 4
    factored = factor(expr3)
    print("Factorization of x^2 - 4: {}".format(factored))
    print("# Expected: (x - 2)*(x + 2)")
    print

    # Example 4: Simplification
    expr4 = (x**2 - 1)/(x - 1)
    simplified = simplify(expr4)
    print("Simplification of (x^2-1)/(x-1): {}".format(simplified))
    print("# Expected: x + 1")
    print

    # Example 5: Partial fraction decomposition
    expr5 = 1/(x**2 - 1)
    partial_fractions = apart(expr5)
    print("Partial fractions of 1/(x^2-1): {}".format(partial_fractions))
    print("# Expected: -1/(2*(x + 1)) + 1/(2*(x - 1))")
    print

    # Example 6: Collect terms
    expr6 = x**2 + 2*a*x + a**2 + 3*b*x + b
    collected = collect(expr6, x)
    print("Collect terms in x of {}:".format(expr6))
    print("Result: {}".format(collected))
    print("# Expected: x**2 + x*(2*a + 3*b) + a**2 + b")
    print

    # Calculus examples
    # Example 1: Differentiation
    f = x**3 + 2*x**2 + x + 1
    df_dx = diff(f, x)
    print("Derivative of {} w.r.t x: {}".format(f, df_dx))
    print("# Expected: 3*x**2 + 4*x + 1")
    print

    # Example 2: Higher-order derivatives
    d2f_dx2 = diff(f, x, 2)  # Second derivative
    print("Second derivative of {}: {}".format(f, d2f_dx2))
    print("# Expected: 6*x + 4")
    print

    # Example 3: Partial derivatives
    g = x**2 * y + x * y**2
    dg_dx = diff(g, x)
    dg_dy = diff(g, y)
    print("Function: {}".format(g))
    print("Partial derivative w.r.t x: {}".format(dg_dx))
    print("Partial derivative w.r.t y: {}".format(dg_dy))
    print("# Expected: ∂g/∂x = 2*x*y + y**2, ∂g/∂y = x**2 + 2*x*y")
    print

    # Example 4: Integration
    h = x**2 + 2*x + 1
    integral = integrate(h, x)
    print("Integral of {} w.r.t x: {}".format(h, integral))
    print("# Expected: x**3/3 + x**2 + x")
    print

    # Example 5: Definite integration
    definite_integral = integrate(h, (x, 0, 1))
    print("Definite integral of {} from 0 to 1: {}".format(h, definite_integral))
    print("# Expected: 7/3")
    print

    # Example 6: Limits
    limit_expr = sin(x) / x
    limit_result = limit(limit_expr, x, 0)
    print("Limit of sin(x)/x as x->0: {}".format(limit_result))
    print("# Expected: 1")
    print

    # Example 7: Series expansion
    series_expansion = series(sin(x), x, 0, 6)  # 6 terms around x=0
    print("Series expansion of sin(x) around 0: {}".format(series_expansion))
    print("# Expected: x - x**3/6 + x**5/120 + O(x**6)")
    print