Python plotting.plot函数代码示例

def plotgrid_and_save(name):
    tmp_file = TmpFileManager.tmp_file

    x = Symbol('x')
    y = Symbol('y')
    z = Symbol('z')
    p1 = plot(x)
    p2 = plot_parametric((sin(x), cos(x)), (x, sin(x)), show=False)
    p3 = plot_parametric(cos(x), sin(x), adaptive=False, nb_of_points=500, show=False)
    p4 = plot3d_parametric_line(sin(x), cos(x), x, show=False)
    # symmetric grid
    p = PlotGrid(2, 2, p1, p2, p3, p4)
    p.save(tmp_file('%s_grid1' % name))
    p._backend.close()

    # grid size greater than the number of subplots
    p = PlotGrid(3, 4, p1, p2, p3, p4)
    p.save(tmp_file('%s_grid2' % name))
    p._backend.close()

    p5 = plot(cos(x),(x, -pi, pi), show=False)
    p5[0].line_color = lambda a: a
    p6 = plot(Piecewise((1, x > 0), (0, True)), (x, -1, 1), show=False)
    p7 = plot_contour((x**2 + y**2, (x, -5, 5), (y, -5, 5)), (x**3 + y**3, (x, -3, 3), (y, -3, 3)), show=False)
    # unsymmetric grid (subplots in one line)
    p = PlotGrid(1, 3, p5, p6, p7)
    p.save(tmp_file('%s_grid3' % name))
    p._backend.close()

def main():
    '''
    legendre_polynomial.py : ルジャンドルの多項式
    '''
    x = symbols("x")
    psi = [1,x,x**2,x**3,x**100]
    phy = schmidt(psi)
    plot(phy[0],phy[1],phy[2],phy[3],phy[4],(x, -1.5, 1.5),ylim=(-1.5,1.5),ylabel='',xlabel='')

def solve_plot_equations(eq1, eq2, x, y):
    # Solve
    solution = solve((eq1, eq2), dict=True)
    if solution:
        print('x: {0} y: {1}'.format(solution[0][x], solution[0][y]))
    else:
        print('No solution found')
    # Plot
    eq1_y = solve(eq1,'y')[0]
    eq2_y = solve(eq2, 'y')[0]
    plot(eq1_y, eq2_y, legend=True)

def plot():
    e = Symbol('e')
    y = Symbol('y')
    n = Symbol('n')
    generalized_vc_bounds = (original_vc_bound, rademacher_penalty_bound)
    growth_function_bound = generate_growth_function_bound(50)
    p1 = plot(original_vc_bound(n, 0.05, growth_function_bound), (n,100, 15000), show=False, line_color = 'black')
    p2 = plot(rademacher_penalty_bound(n, 0.05, growth_function_bound), (n,100, 15000), show=False, line_color = 'blue')
    plot_implicit(Eq(e, parrondo_van_den_broek_right(e, n, 0.05, growth_function_bound)), (n,100, 15000), (e,0,5))
    # plot_implicit(Eq(e, devroye(e, n, 0.05, growth_function_bound)), (n,100, 1000), (e,0,5))
    p1.extend(p2)
    p1.show()

def test_issue_15265():
    from sympy.core.sympify import sympify
    from sympy.core.singleton import S

    matplotlib = import_module('matplotlib', min_module_version='1.1.0', catch=(RuntimeError,))
    if not matplotlib:
        skip("Matplotlib not the default backend")

    x = Symbol('x')
    eqn = sin(x)

    p = plot(eqn, xlim=(-S.Pi, S.Pi), ylim=(-1, 1))
    p._backend.close()

    p = plot(eqn, xlim=(-1, 1), ylim=(-S.Pi, S.Pi))
    p._backend.close()

    p = plot(eqn, xlim=(-1, 1), ylim=(sympify('-3.14'), sympify('3.14')))
    p._backend.close()

    p = plot(eqn, xlim=(sympify('-3.14'), sympify('3.14')), ylim=(-1, 1))
    p._backend.close()

    raises(ValueError,
        lambda: plot(eqn, xlim=(-S.ImaginaryUnit, 1), ylim=(-1, 1)))

    raises(ValueError,
        lambda: plot(eqn, xlim=(-1, 1), ylim=(-1, S.ImaginaryUnit)))

    raises(ValueError,
        lambda: plot(eqn, xlim=(-S.Infinity, 1), ylim=(-1, 1)))

    raises(ValueError,
        lambda: plot(eqn, xlim=(-1, 1), ylim=(-1, S.Infinity)))

def test_append_issue_7140():
    x = Symbol('x')
    p1 = plot(x)
    p2 = plot(x**2)
    p3 = plot(x + 2)

    # append a series
    p2.append(p1[0])
    assert len(p2._series) == 2

    with raises(TypeError):
        p1.append(p2)

    with raises(TypeError):
        p1.append(p2._series)

def plot_and_save_4(name):
    tmp_file = TmpFileManager.tmp_file

    x = Symbol('x')
    y = Symbol('y')
    z = Symbol('z')

    ###
    # Examples from the 'advanced' notebook
    ###

    # XXX: This raises the warning "The evaluation of the expression is
    # problematic. We are trying a failback method that may still work. Please
    # report this as a bug." It has to use the fallback because using evalf()
    # is the only way to evaluate the integral. We should perhaps just remove
    # that warning.

    with warnings.catch_warnings(record=True) as w:
        i = Integral(log((sin(x)**2 + 1)*sqrt(x**2 + 1)), (x, 0, y))
        p = plot(i, (y, 1, 5))
        p.save(tmp_file('%s_advanced_integral' % name))
        p._backend.close()
        # Make sure no other warnings were raised
        for i in w:
            assert issubclass(i.category, UserWarning)
            assert "The evaluation of the expression is problematic" in str(i.message)

def plot_and_save_5(name):
    tmp_file = TmpFileManager.tmp_file

    x = Symbol('x')
    y = Symbol('y')
    z = Symbol('z')

    s = Sum(1/x**y, (x, 1, oo))
    p = plot(s, (y, 2, 10))
    p.save(tmp_file('%s_advanced_inf_sum' % name))
    p._backend.close()

    p = plot(Sum(1/x, (x, 1, y)), (y, 2, 10), show=False)
    p[0].only_integers = True
    p[0].steps = True
    p.save(tmp_file('%s_advanced_fin_sum' % name))
    p._backend.close()

def main():
    x = symbols("x")
    rho = (1 - x**2)**(-1/2)
    color = ["b","r","g","k","c","m","y"]

    T = rodrigues_formula(rho)
    p = []


    for i in range(7):
        t = simplify(T[i])
        print ("T(",i,") = ", t)
        if i > 0 :
            p.append( plot(T[i],(x,-1.1,1.1),ylim=(-1.1,1.1),show=False,line_color=color[i]) )
            p[0].extend(p[i])
        else:
            p.append( plot((T[i],(x,-1.1,1.1)),ylim=(-1.1,1.1),show=False,line_color=color[i]) )
    p[0].show()

def plot_all_Ant_fits( AntEqtbl_split ):
    """
    plot_all_Ant_fits = plot_all_Ant_fits( AntEqtbl_split )

    EXAMPLES of USAGE:
    propane_dat = cleaned_Phase_data("propane")
    propane_plts = plot_all_Ant_fits( propane_dat.AntEqParams )
    """
    fits = []
    for row in AntEqtbl_split:
        fit = AntoineEqn.subs(dict(zip([A,B,C], [float(no) for no in row[2:5]])))
        fits.append( fit )
    to_plot = []
    for fit, row in zip(fits, AntEqtbl_split):
        range = (T, float(row[0]), float(row[1]))
        to_plot.append( (fit.rhs, range ) )
    plot( *to_plot )
    return to_plot

def test_append_issue_7140():
    matplotlib = import_module('matplotlib', min_module_version='1.1.0', catch=(RuntimeError,))
    if not matplotlib:
        skip("Matplotlib not the default backend")

    x = Symbol('x')
    p1 = plot(x)
    p2 = plot(x**2)
    p3 = plot(x + 2)

    # append a series
    p2.append(p1[0])
    assert len(p2._series) == 2

    with raises(TypeError):
        p1.append(p2)

    with raises(TypeError):
        p1.append(p2._series)

def plot_and_save_6(name):
    tmp_file = TmpFileManager.tmp_file

    x = Symbol('x')
    y = Symbol('y')
    z = Symbol('z')

    ###
    # Test expressions that can not be translated to np and generate complex
    # results.
    ###
    plot(sin(x) + I*cos(x)).save(tmp_file())
    plot(sqrt(sqrt(-x))).save(tmp_file())
    plot(LambertW(x)).save(tmp_file())
    plot(sqrt(LambertW(x))).save(tmp_file())

    #Characteristic function of a StudentT distribution with nu=10
    plot((meijerg(((1 / 2,), ()), ((5, 0, 1 / 2), ()), 5 * x**2 * exp_polar(-I*pi)/2)
            + meijerg(((1/2,), ()), ((5, 0, 1/2), ()),
                5*x**2 * exp_polar(I*pi)/2)) / (48 * pi), (x, 1e-6, 1e-2)).save(tmp_file())

def main():
    x = symbols("x")

    m = Rational(1,3) #1/3
    k = 2 #Rational(1,2)
    rho = (1 - (k**2) * (x**2) )**(-m)
    color = ["b","r","g","k","c","m","y"]

    T = rodrigues_formula(rho,m,k)
    p = []


    for i in range(7):
        t = simplify(T[i])
        print ("T(",i,") = ", t)
        if i > 0 :
            p.append( plot(T[i],(x,-0.52,0.52),ylim=(-1.1,1.1),show=False,line_color=color[i]) )
            p[0].extend(p[i])
        else:
            p.append( plot((T[i],(x,-0.52,0.52)),ylim=(-1.1,1.1),show=False,line_color=color[i]) )
    p[0].show()

def plot_and_save_4(name):
    tmp_file = TmpFileManager.tmp_file

    x = Symbol('x')
    y = Symbol('y')
    z = Symbol('z')

    ###
    # Examples from the 'advanced' notebook
    ###

    # XXX: This raises the warning "The evaluation of the expression is
    # problematic. We are trying a failback method that may still work. Please
    # report this as a bug." It has to use the fallback because using evalf()
    # is the only way to evaluate the integral. We should perhaps just remove
    # that warning.

    with warns(UserWarning, match="The evaluation of the expression is problematic"):
        i = Integral(log((sin(x)**2 + 1)*sqrt(x**2 + 1)), (x, 0, y))
        p = plot(i, (y, 1, 5))
        p.save(tmp_file('%s_advanced_integral' % name))
        p._backend.close()

def plot_and_save_3(name):
    tmp_file = TmpFileManager.tmp_file

    x = Symbol('x')
    y = Symbol('y')
    z = Symbol('z')

    ###
    # Examples from the 'colors' notebook
    ###

    p = plot(sin(x))
    p[0].line_color = lambda a: a
    p.save(tmp_file('%s_colors_line_arity1' % name))

    p[0].line_color = lambda a, b: b
    p.save(tmp_file('%s_colors_line_arity2' % name))
    p._backend.close()

    p = plot(x*sin(x), x*cos(x), (x, 0, 10))
    p[0].line_color = lambda a: a
    p.save(tmp_file('%s_colors_param_line_arity1' % name))

    p[0].line_color = lambda a, b: a
    p.save(tmp_file('%s_colors_param_line_arity2a' % name))

    p[0].line_color = lambda a, b: b
    p.save(tmp_file('%s_colors_param_line_arity2b' % name))
    p._backend.close()

    p = plot3d_parametric_line(sin(x) + 0.1*sin(x)*cos(7*x),
             cos(x) + 0.1*cos(x)*cos(7*x),
        0.1*sin(7*x),
        (x, 0, 2*pi))
    p[0].line_color = lambdify_(x, sin(4*x))
    p.save(tmp_file('%s_colors_3d_line_arity1' % name))
    p[0].line_color = lambda a, b: b
    p.save(tmp_file('%s_colors_3d_line_arity2' % name))
    p[0].line_color = lambda a, b, c: c
    p.save(tmp_file('%s_colors_3d_line_arity3' % name))
    p._backend.close()

    p = plot3d(sin(x)*y, (x, 0, 6*pi), (y, -5, 5))
    p[0].surface_color = lambda a: a
    p.save(tmp_file('%s_colors_surface_arity1' % name))
    p[0].surface_color = lambda a, b: b
    p.save(tmp_file('%s_colors_surface_arity2' % name))
    p[0].surface_color = lambda a, b, c: c
    p.save(tmp_file('%s_colors_surface_arity3a' % name))
    p[0].surface_color = lambdify_((x, y, z), sqrt((x - 3*pi)**2 + y**2))
    p.save(tmp_file('%s_colors_surface_arity3b' % name))
    p._backend.close()

    p = plot3d_parametric_surface(x * cos(4 * y), x * sin(4 * y), y,
             (x, -1, 1), (y, -1, 1))
    p[0].surface_color = lambda a: a
    p.save(tmp_file('%s_colors_param_surf_arity1' % name))
    p[0].surface_color = lambda a, b: a*b
    p.save(tmp_file('%s_colors_param_surf_arity2' % name))
    p[0].surface_color = lambdify_((x, y, z), sqrt(x**2 + y**2 + z**2))
    p.save(tmp_file('%s_colors_param_surf_arity3' % name))
    p._backend.close()