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()

def plot_and_save(name):
    x = Symbol('x')
    y = Symbol('y')
    z = Symbol('z')

    ###
    # Examples from the 'introduction' notebook
    ###

    p = plot(x)
    p = plot(x*sin(x),x*cos(x))
    p.extend(p)
    p[0].line_color = lambda a : a
    p[1].line_color='b'
    p.title = 'Big title'
    p.xlabel = 'the x axis'
    p[1].label = 'straight line'
    p.legend = True
    p.aspect_ratio = (1,1)
    p.xlim = (-15,20)
    p.save(tmp_file('%s_basic_options_and_colors.png' % name))

    p.extend(plot(x+1))
    p.append(plot(x+3,x**2)[1])
    p.save(tmp_file('%s_plot_extend_append.png' % name))

    p[2] = plot(x**2, (x, -2, 3))
    p.save(tmp_file('%s_plot_setitem.png' % name))

    p = plot(sin(x),(x,-2*pi,4*pi))
    p.save(tmp_file('%s_line_explicit.png' % name))

    p = plot(sin(x))
    p.save(tmp_file('%s_line_default_range.png' % name))

    p = plot((x**2, (x, -5, 5)), (x**3, (x, -3, 3)))
    p.save(tmp_file('%s_line_multiple_range.png' % name))


    #parametric 2d plots.
    #Single plot with default range.
    plot_parametric(sin(x), cos(x)).save(tmp_file())

    #Single plot with range.
    p = plot_parametric(sin(x), cos(x), (x, -5, 5))
    p.save(tmp_file('%s_parametric_range.png' % name))

    #Multiple plots with same range.
    p = plot_parametric((sin(x), cos(x)), (x, sin(x)))
    p.save(tmp_file('%s_parametric_multiple.png' % name))

    #Multiple plots with different ranges.
    p = plot_parametric((sin(x), cos(x), (x, -3, 3)), (x, sin(x), (x, -5, 5)))
    p.save(tmp_file('%s_parametric_multiple_ranges.png' % name))

    #depth of recursion specified.
    p = plot_parametric(x, sin(x), depth=13)
    p.save(tmp_file('%s_recursion_depth' % name))

    #No adaptive sampling.
    p = plot_parametric(cos(x), sin(x), adaptive=False, nb_of_points=500)
    p.save(tmp_file('%s_adaptive' % name))

    #3d parametric plots
    p = plot3d_parametric_line(sin(x),cos(x),x)
    p.save(tmp_file('%s_3d_line.png' % name))

    p = plot3d_parametric_line((sin(x), cos(x), x, (x, -5, 5)), (cos(x), sin(x), x, (x, -3, 3)))
    p.save(tmp_file('%s_3d_line_multiple' % name))

    p = plot3d_parametric_line(sin(x), cos(x), x, nb_of_points=30)
    p.save(tmp_file('%s_3d_line_points' % name))

    # 3d surface single plot.
    p = plot3d(x * y)
    p.save(tmp_file('%s_surface.png' % name))

    # Multiple 3D plots with same range.
    p = plot3d(-x * y, x * y, (x, -5, 5))
    p.save(tmp_file('%s_surface_multiple' % name))

    # Multiple 3D plots with different ranges.
    p = plot3d((x * y, (x, -3, 3), (y, -3, 3)), (-x * y, (x, -3, 3), (y, -3, 3)))
    p.save(tmp_file('%s_surface_multiple_ranges' % name))

    # Single Parametric 3D plot
    p = plot3d_parametric_surface(sin(x + y), cos(x - y), x - y)
    p.save(tmp_file('%s_parametric_surface' % name))

    # Multiple Parametric 3D plots.
    p = plot3d_parametric_surface((x*sin(z),x*cos(z),z, (x, -5, 5), (z, -5, 5)),
                (sin(x + y), cos(x - y), x - y, (x, -5, 5), (y, -5, 5)))
    p.save(tmp_file('%s_parametric_surface.png' % name))

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

    p = plot(sin(x))
    p[0].line_color = lambda a : a
#.........这里部分代码省略.........

from sympy.plotting import plot
from sympy import Symbol
x = Symbol('x')
plot(2*x+3)

def ploteq(*args, **kwargs):
    """軸のアスペクト比を均等にプロットするための関数である。broken。以下の方法で均等なプロットを見ることはできるが、Plot._backend を生成するためには plot(..., show=False) とはできないようである。なので均等ではないプロットと均等なプロットとが両方表示されてしまう。しかし何にせよ、均等なプロットを見ることはできている。それらが両方表示されるのは好ましいと考えることもできる。"""
    p = plot(*args, **kwargs)
    p._backend.ax.set_aspect("equal")
    return p._backend.fig

from sympy import *
from sympy.plotting import plot
import sys

exprstr = raw_input('Enter an expression in terms of x and y: ')

x = Symbol('x')
try:
	expr1 = sympify(exprstr)
except SympifyError:
	print ('Invalid input(s)')
	sys.exit("Exiting")

p = plot(expr1, legend = true, show = false)
p.show()

 def plotExpressioFromTo(self, fromm, to):
     plot(self.expression, (self.x,fromm,to) )