def quick_plot(self, x_min, x_max, x_scale="linear", y_scale="linear",
res=200, filename=None, function_name="f", units=None): # pragma: no cover
"""
Plot the formula.
Parameters
----------
x_min : unitful scalar quantity
The mininum value of the "x" variable to plot.
x_max : unitful scalar quantity
The maximum value of the "x" variable to plot.
x_scale : string
The scaling for the x axis. Can be "linear" or "log".
y_scale : string
The scaling for the y axis. Can be "linear" or "log".
res : integer
The number of points to use in the plot. Default is 200.
filename : str
If set, save the plot to this filename.
function_name : str
The name of the function for the y-axis of the plot.
units : str
The units to convert the y-axis values to.
Examples
--------
>>> import astropy.units as u
>>> r_min = 0.1*u.kpc
>>> r_max = 1000.*u.kpc
>>> density_profile.quick_plot(r_min, r_max, x_scale="log")
"""
from IPython.display import HTML, display
matplotlib.rc("font", size=16, family="serif")
fig = matplotlib.figure.Figure(figsize=(8,8))
ax = fig.add_subplot(111)
arr = check_type(x_min)
x = arr(np.linspace(x_min.value, x_max.value, num=res), get_units(x_min))
y = arr(self(x))
if units is not None:
y = in_units(y, units)
x_units = latexify_units(x)
y_units = latexify_units(y)
ax.plot(np.array(x), np.array(y))
ax.set_xlabel(r"$\mathrm{%s}$ (" % self.x + x_units + ")")
ax.set_ylabel(r"$\mathrm{%s(%s)}$ (" % (function_name, self.x) + y_units + ")")
ax.set_xscale(x_scale)
ax.set_yscale(y_scale)
fig.tight_layout()
if filename is not None:
fig.savefig(filename)
canvas = FigureCanvasAgg(fig)
f = BytesIO()
canvas.print_figure(f)
f.seek(0)
img = base64.b64encode(f.read()).decode()
ret = r'<img style="max-width:100%%;max-height:100%%;" ' \
r'src="data:image/png;base64,{0}"><br>'.format(img)
display(HTML(ret))
def quick_plot(self, x_min, x_max, x_scale="linear", y_scale="linear",
res=200, filename=None, function_name="f", units=None): # pragma: no cover
"""
Plot the formula.
Parameters
----------
x_min : unitful scalar quantity
The mininum value of the "x" variable to plot.
x_max : unitful scalar quantity
The maximum value of the "x" variable to plot.
x_scale : string
The scaling for the x axis. Can be "linear" or "log".
y_scale : string
The scaling for the y axis. Can be "linear" or "log".
res : integer
The number of points to use in the plot. Default is 200.
filename : str
If set, save the plot to this filename.
function_name : str
The name of the function for the y-axis of the plot.
units : str
The units to convert the y-axis values to.
Examples
--------
>>> import astropy.units as u
>>> r_min = 0.1*u.kpc
>>> r_max = 1000.*u.kpc
>>> density_profile.quick_plot(r_min, r_max, x_scale="log")
"""
from IPython.display import HTML, display
matplotlib.rc("font", size=16, family="serif")
fig = matplotlib.figure.Figure(figsize=(8,8))
ax = fig.add_subplot(111)
arr = check_type(x_min)
x = arr(np.linspace(x_min.value, x_max.value, num=res), get_units(x_min))
y = arr(self(x))
if units is not None:
y = in_units(y, units)
x_units = latexify_units(x)
y_units = latexify_units(y)
ax.plot(np.array(x), np.array(y))
ax.set_xlabel(r"$\mathrm{%s}$ (" % self.x + x_units + ")")
ax.set_ylabel(r"$\mathrm{%s(%s)}$ (" % (function_name, self.x) + y_units + ")")
ax.set_xscale(x_scale)
ax.set_yscale(y_scale)
fig.tight_layout()
if filename is not None:
fig.savefig(filename)
canvas = FigureCanvasAgg(fig)
f = BytesIO()
canvas.print_figure(f)
f.seek(0)
img = base64.b64encode(f.read()).decode()
ret = r'<img style="max-width:100%%;max-height:100%%;" ' \
r'src="data:image/png;base64,{0}"><br>'.format(img)
display(HTML(ret))
def quick_plot(self, x_min, x_max, y_min, y_max, x_scale="linear",
y_scale="linear", z_scale="linear", res_x=200,
res_y=200, filename=None, cmap=None, function_name="f",
units=None): # pragma: no cover
"""
Plot the formula.
Parameters
----------
x_min : unitful scalar quantity
The mininum value of the "x" variable to plot.
x_max : unitful scalar quantity
The maximum value of the "x" variable to plot.
y_min : unitful scalar quantity
The mininum value of the "y" variable to plot.
y_max : unitful scalar quantity
The maximum value of the "y" variable to plot.
x_scale : string
The scaling for the x axis. Can be "linear" or "log".
y_scale : string
The scaling for the y axis. Can be "linear" or "log".
z_scale : string
The scaling for the z axis. Can be "linear" or "log".
res_x : integer
The number of points to use in the plot along the x-axis.
Default is 200.
res_y : integer
The number of points to use in the plot along the y-axis.
Default is 200.
filename : str
If set, save the plot to this filename.
cmap : str
The colormap to use when making the plot.
function_name : str
The name of the function for the colormap.
units : str
The units to convert the colormap values to.
Examples
--------
>>> import yt.units as u
>>> g_x = gaussian(x="v_x", A="A_x", mu="mu_x", sigma="sigma_x")
>>> g_y = gaussian(x="v_y", A="A_y", mu="mu_y", sigma="sigma_y")
>>> g = g_x*g_y
>>> g.set_param_values(A_x=1.0, A_y=1.0, mu_x=0*u.km/u.s, mu_y=0*u.km/u.s,
... sigma_x=200*u.km/u.s, sigma_y=100*u.km/u.s)
>>> g.quick_plot(-300*u.km/u.s, 300*u.km/u.s, -300*u.km/u.s, 300*u.km/u.s,)
"""
from IPython.display import display, HTML
from mpl_toolkits.axes_grid1 import make_axes_locatable
matplotlib.rc("font", size=16, family="serif")
fig = matplotlib.figure.Figure(figsize=(8,8))
ax = fig.add_subplot(111)
arr = check_type(x_min)
x = np.linspace(x_min.value, x_max.value, num=res_x)
y = np.linspace(y_min.value, y_max.value, num=res_y)
xx, yy = np.meshgrid(x, y)
xx = arr(xx, get_units(x_min))
yy = arr(yy, get_units(y_min))
vars = {str(self.x):xx,str(self.y):yy}
z = arr(self(**vars))
if units is not None:
z = in_units(z, units)
x_units = latexify_units(xx)
y_units = latexify_units(yy)
z_units = latexify_units(z)
extent = (x[0], x[-1], y[0], y[-1])
im = ax.imshow(z, extent=extent, cmap=cmap)
ax.set_xlabel(r"$\mathrm{%s}$ (" % self.x + x_units + ")")
ax.set_ylabel(r"$\mathrm{%s}$ (" % self.y + y_units + ")")
ax.set_xscale(x_scale)
ax.set_yscale(y_scale)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cbar = fig.colorbar(im, cax=cax)
cbar.set_label(r"$\mathrm{%s(%s,%s)}$ (" % (function_name, self.x, self.y) + z_units + ")")
cbar.ax.set_yscale(z_scale)
fig.tight_layout()
if filename is not None:
fig.savefig(filename)
canvas = FigureCanvasAgg(fig)
f = BytesIO()
canvas.print_figure(f)
f.seek(0)
img = base64.b64encode(f.read()).decode()
ret = r'<img style="max-width:100%%;max-height:100%%;" ' \
r'src="data:image/png;base64,{0}"><br>'.format(img)
display(HTML(ret))
def quick_plot(self, x_min, x_max, y_min, y_max, x_scale="linear",
y_scale="linear", z_scale="linear", res_x=200,
res_y=200, filename=None, cmap=None, function_name="f",
units=None): # pragma: no cover
"""
Plot the formula.
Parameters
----------
x_min : unitful scalar quantity
The mininum value of the "x" variable to plot.
x_max : unitful scalar quantity
The maximum value of the "x" variable to plot.
y_min : unitful scalar quantity
The mininum value of the "y" variable to plot.
y_max : unitful scalar quantity
The maximum value of the "y" variable to plot.
x_scale : string
The scaling for the x axis. Can be "linear" or "log".
y_scale : string
The scaling for the y axis. Can be "linear" or "log".
z_scale : string
The scaling for the z axis. Can be "linear" or "log".
res_x : integer
The number of points to use in the plot along the x-axis.
Default is 200.
res_y : integer
The number of points to use in the plot along the y-axis.
Default is 200.
filename : str
If set, save the plot to this filename.
cmap : str
The colormap to use when making the plot.
function_name : str
The name of the function for the colormap.
units : str
The units to convert the colormap values to.
Examples
--------
>>> import yt.units as u
>>> g_x = gaussian(x="v_x", A="A_x", mu="mu_x", sigma="sigma_x")
>>> g_y = gaussian(x="v_y", A="A_y", mu="mu_y", sigma="sigma_y")
>>> g = g_x*g_y
>>> g.set_param_values(A_x=1.0, A_y=1.0, mu_x=0*u.km/u.s, mu_y=0*u.km/u.s,
... sigma_x=200*u.km/u.s, sigma_y=100*u.km/u.s)
>>> g.quick_plot(-300*u.km/u.s, 300*u.km/u.s, -300*u.km/u.s, 300*u.km/u.s,)
"""
from IPython.display import display, HTML
from mpl_toolkits.axes_grid1 import make_axes_locatable
matplotlib.rc("font", size=16, family="serif")
fig = matplotlib.figure.Figure(figsize=(8,8))
ax = fig.add_subplot(111)
arr = check_type(x_min)
x = np.linspace(x_min.value, x_max.value, num=res_x)
y = np.linspace(y_min.value, y_max.value, num=res_y)
xx, yy = np.meshgrid(x, y)
xx = arr(xx, get_units(x_min))
yy = arr(yy, get_units(y_min))
vars = {str(self.x):xx,str(self.y):yy}
z = arr(self(**vars))
if units is not None:
z = in_units(z, units)
x_units = latexify_units(xx)
y_units = latexify_units(yy)
z_units = latexify_units(z)
extent = (x[0], x[-1], y[0], y[-1])
im = ax.imshow(z, extent=extent, cmap=cmap)
ax.set_xlabel(r"$\mathrm{%s}$ (" % self.x + x_units + ")")
ax.set_ylabel(r"$\mathrm{%s}$ (" % self.y + y_units + ")")
ax.set_xscale(x_scale)
ax.set_yscale(y_scale)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cbar = fig.colorbar(im, cax=cax)
cbar.set_label(r"$\mathrm{%s(%s,%s)}$ (" % (function_name, self.x, self.y) + z_units + ")")
cbar.ax.set_yscale(z_scale)
fig.tight_layout()
if filename is not None:
fig.savefig(filename)
canvas = FigureCanvasAgg(fig)
f = BytesIO()
canvas.print_figure(f)
f.seek(0)
img = base64.b64encode(f.read()).decode()
ret = r'<img style="max-width:100%%;max-height:100%%;" ' \
r'src="data:image/png;base64,{0}"><br>'.format(img)
display(HTML(ret))
