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luke naylor latex documents
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Stabilities - Hodge Club
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05dfb000
Commit
05dfb000
authored
1 year ago
by
Luke Naylor
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Give examples of possible Gamma curves for Lambda stab
parent
76103594
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main
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Pipeline
#25270
passed
1 year ago
Stage: test
Changes
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4 changed files
figures/Figures-notebook.ipynb
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figures/Figures-notebook.ipynb
main.tex
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main.tex
sagetexscripts/fig7.sage
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sagetexscripts/fig7.sage
sagetexscripts/fig8.sage
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sagetexscripts/fig8.sage
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figures/Figures-notebook.ipynb
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View file @
05dfb000
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@@ -447,6 +447,82 @@
"p.tick_label_color(\"white\")\n",
"p.show()"
]
},
{
"cell_type": "code",
"execution_count": 67,
"id": "8f0ee2d7",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"Graphics object consisting of 6 graphics primitives"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plot_range = 4\n",
"v = Chern_Char(2,0,-1,1)\n",
"lam = stability.Lambda(s=1/3)\n",
"Theta = lam.rank(v)\n",
"Gamma = lam.degree(v)\n",
"mu = stability.Mumford().slope(v)\n",
"p = (\n",
"implicit_plot(Theta, (lam.beta, -plot_range,plot_range), (lam.alpha, 0, plot_range))\n",
"+ implicit_plot(Gamma, (lam.beta, -plot_range,plot_range), (lam.alpha, 0, plot_range), rgbcolor=\"green\")\n",
"+ line([(mu,0),(mu,plot_range)], rgbcolor = \"red\")\n",
"+ text(r\"$\\beta=\\mu$\", (mu, -0.5), fontsize=\"x-large\", rgbcolor=\"red\")\n",
"+ text(r\"$\\nu = 0$\", (mu-1, -0.5), fontsize=\"x-large\", rgbcolor=\"blue\")\n",
"+ text(r\"$\\lambda = 0$\", (mu+2, -0.5), fontsize=\"x-large\", rgbcolor=\"green\")\n",
")\n",
"p.tick_label_color(\"white\")\n",
"p.ymin(0)\n",
"p"
]
},
{
"cell_type": "code",
"execution_count": 73,
"id": "4b66636a",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"Graphics object consisting of 6 graphics primitives"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plot_range = 4\n",
"v = Chern_Char(2,0,-1,1/2)\n",
"lam = stability.Lambda(s=1/3)\n",
"Theta = lam.rank(v)\n",
"Gamma = lam.degree(v)\n",
"mu = stability.Mumford().slope(v)\n",
"p = (\n",
"implicit_plot(Theta, (lam.beta, -plot_range,plot_range), (lam.alpha, 0, plot_range))\n",
"+ implicit_plot(Gamma, (lam.beta, -plot_range,plot_range), (lam.alpha, 0, plot_range), rgbcolor=\"green\")\n",
"+ line([(mu,0),(mu,plot_range)], rgbcolor = \"red\")\n",
"+ text(r\"$\\beta=\\mu$\", (mu, -0.5), fontsize=\"x-large\", rgbcolor=\"red\")\n",
"+ text(r\"$\\nu = 0$\", (mu+1, -0.5), fontsize=\"x-large\", rgbcolor=\"blue\")\n",
"+ text(r\"$\\lambda = 0$\", (mu-1.5, -0.5), fontsize=\"x-large\", rgbcolor=\"green\")\n",
")\n",
"p.tick_label_color(\"white\")\n",
"p.ymin(0)\n",
"p"
]
}
],
"metadata": {
...
...
%% Cell type:code id:51c1d049 tags:
```
sage
from pseudowalls import *
```
%% Cell type:code id:cbd6285e tags:
```
sage
O = Chern_Char(1,0)
O
```
%% Output
Chern Character:\begin{array}{l} \mathrm{ch}_{0} = 1 \\ \mathrm{ch}_{1} = 0 \ell^{1} \end{array}
<pseudowalls.chern_character.Chern_Char object at 0x7ff55f6a1c30>
%% Cell type:code id:32366efd tags:
```
sage
O1 = exponential_chern(1,1)
O1
```
%% Output
Chern Character:\begin{array}{l} \mathrm{ch}_{0} = 1 \\ \mathrm{ch}_{1} = 1 \ell^{1} \end{array}
<pseudowalls.chern_character.Chern_Char object at 0x7ff55f6a1840>
%% Cell type:code id:54385028 tags:
```
sage
O1inv = exponential_chern(-1,1)
O1inv
```
%% Output
Chern Character:\begin{array}{l} \mathrm{ch}_{0} = 1 \\ \mathrm{ch}_{1} = -1 \ell^{1} \end{array}
<pseudowalls.chern_character.Chern_Char object at 0x7ff55f6a2680>
%% Cell type:code id:f0768fcd tags:
```
sage
O2 = O1 * O1
O2
```
%% Output
Chern Character:\begin{array}{l} \mathrm{ch}_{0} = 1 \\ \mathrm{ch}_{1} = 2 \ell^{1} \end{array}
<pseudowalls.chern_character.Chern_Char object at 0x7ff55f6a2a10>
%% Cell type:code id:d5c5f903 tags:
```
sage
O_x = Chern_Char(0,1)
O_x
```
%% Output
Chern Character:\begin{array}{l} \mathrm{ch}_{0} = 0 \\ \mathrm{ch}_{1} = 1 \ell^{1} \end{array}
<pseudowalls.chern_character.Chern_Char object at 0x7ff55f6a2d10>
%% Cell type:code id:f5e93bd3 tags:
```
sage
def plot_central_charge(chern, name, argument = None, radius = None):
Z = stability.Mumford().central_charge(chern)
x = Z.real()
y = Z.imag()
if not argument:
argument = arctan(y/x) if x != 0 else pi / 2
if argument <= 0:
argument += pi
if not radius:
radius = (pi-argument/2)/pi
return point(
Z,
marker = "o",
size = 600,
rgbcolor = "white",
#markeredgecolor = "purple",
zorder = 100
) + point(
Z,
marker = name,
size = 500,
rgbcolor = "red",
zorder = 101
) + line(
(0, Z),
rgbcolor = "red",
linestyle = "dashed",
zorder = 99
) + disk(
(0,0),
float(radius),
(0, float(argument)),
alpha=.2,
fill=False,
thickness=1,
rgbcolor="purple"
)
p = sum(
plot_central_charge(chern, name)
for chern, name in [
(O1, r"$\mathcal{O}(1)$"),
(O, r"$\mathcal{O}_X$"),
(O1inv, r"$\mathcal{O}(-1)$"),
(O2, r"$\mathcal{O}(2)$"),
(O_x, r"$\mathcal{O}_p$")
]
)
xmax = (2.5)
xmin = (-2.5)
ymin = (-0.25)
ymax = (1.5)
aspect_ratio = (1)
p += polygon(
[
(xmax + 1,0),
(xmin - 1,0),
(xmin - 1,ymax + 1),
(xmax + 1,ymax + 1)
],
rgbcolor = "yellow",
alpha = 0.2,
zorder = 102
)
p.xmax(xmax)
p.xmin(xmin)
p.ymin(ymin)
p.ymax(ymax)
p.set_aspect_ratio(aspect_ratio)
p.axes_labels([r"$\mathcal{R}$",r"$\mathcal{I}$"])
p.show()
```
%% Output
%% Cell type:code id:50d8265f tags:
```
sage
plot_central_charge(O, r"$\mathcal{O}_X$", radius = 0.9)
```
%% Output
Graphics object consisting of 4 graphics primitives
%% Cell type:code id:1cfa37b3 tags:
```
sage
plot_central_charge(-O, r"$\mathcal{O}_X[1]$", argument=3*pi/2, radius = 0.9)
```
%% Output
Graphics object consisting of 4 graphics primitives
%% Cell type:code id:8149044b tags:
```
sage
plot_central_charge(O, r"$\mathcal{O}_X[2]$", argument=2*pi, radius = 0.9) + \
disk(
(0,0),
0.9,
(0, float(pi/2)),
alpha=.4,
fill=False,
thickness=1,
rgbcolor="purple"
)
```
%% Output
Graphics object consisting of 5 graphics primitives
%% Cell type:code id:4af73988 tags:
```
sage
ymin = 0
ymax = 2
xmin = -3
xmax = 1
mu = -1
p = line(
[(mu,0), (mu,ymax)],
linestyle = "dashed",
) + text(
r"""$\beta = \mu(E)$
$\mathfrak{Im}(\mathcal{Z}_{\alpha,\beta}(E)) = 0$""",
(mu, -0.25),
fontsize = "large",
rgbcolor = "black"
) + text(
r"""$\longleftarrow$
$\mathrm{Coh}(X) \leftarrow \mathcal{B}^\beta$""",
(-2.5,1),
fontsize = "large"
) + text(
r"""$E \in \mathcal{B}^\beta$
$\mathfrak{Im}(\mathcal{Z}_{\alpha,\beta}(E)) \geq 0$""",
(-1.5,1),
fontsize = "large"
) + text(
r"""$E[1] \in \mathcal{B}^\beta$
$\mathfrak{Im}(\mathcal{Z}_{\alpha,\beta}(E)) < 0$""",
(-0.5,1),
fontsize = "large",
rgbcolor = "red"
) + polygon(
[
(mu,0),
(xmin,0),
(xmin,ymax),
(mu,ymax)
],
rgbcolor = "blue",
alpha = 0.1,
zorder = 102
) + polygon(
[
(xmax,0),
(mu,0),
(mu,ymax),
(xmax,ymax)
],
rgbcolor = "orange",
alpha = 0.1,
zorder = 102
)
p.axes_labels([r"$\beta$",r"$\alpha$"])
p.tick_label_color("white")
p.show()
```
%% Output
%% Cell type:code id:8a52b3a8 tags:
```
sage
v = Chern_Char(1,0,-1)
u = Chern_Char(1,1,-3)
ts = stability.Tilt()
mu = stability.Mumford().slope
p = implicit_plot(
ts.wall_eqn(u,v)/ts.alpha == 0,
(ts.beta, -5, 5),
(ts.alpha, 0, 5),
rgbcolor = "purple",
) + implicit_plot(
ts.degree(v) == 0,
(ts.beta, -5, 5),
(ts.alpha, 0, 5),
rgbcolor = "red",
linestyle = "dotted",
) + implicit_plot(
ts.degree(u) == 0,
(ts.beta, -5, 5),
(ts.alpha, 0, 5),
linestyle = "dotted",
) + line(
[(mu(v),0),
(mu(v),5)],
rgbcolor = "red",
legend_label = "Potential destabilizer",
) + line(
[(mu(u),0),
(mu(u),5)],
legend_label = "Fixed $v$ who's repr. obj. destabilized",
)
p.tick_label_color("white")
p.show()
```
%% Output
%% Cell type:code id:8f0ee2d7 tags:
```
sage
plot_range = 4
v = Chern_Char(2,0,-1,1)
lam = stability.Lambda(s=1/3)
Theta = lam.rank(v)
Gamma = lam.degree(v)
mu = stability.Mumford().slope(v)
p = (
implicit_plot(Theta, (lam.beta, -plot_range,plot_range), (lam.alpha, 0, plot_range))
+ implicit_plot(Gamma, (lam.beta, -plot_range,plot_range), (lam.alpha, 0, plot_range), rgbcolor="green")
+ line([(mu,0),(mu,plot_range)], rgbcolor = "red")
+ text(r"$\beta=\mu$", (mu, -0.5), fontsize="x-large", rgbcolor="red")
+ text(r"$\nu = 0$", (mu-1, -0.5), fontsize="x-large", rgbcolor="blue")
+ text(r"$\lambda = 0$", (mu+2, -0.5), fontsize="x-large", rgbcolor="green")
)
p.tick_label_color("white")
p.ymin(0)
p
```
%% Output
Graphics object consisting of 6 graphics primitives
%% Cell type:code id:4b66636a tags:
```
sage
plot_range = 4
v = Chern_Char(2,0,-1,1/2)
lam = stability.Lambda(s=1/3)
Theta = lam.rank(v)
Gamma = lam.degree(v)
mu = stability.Mumford().slope(v)
p = (
implicit_plot(Theta, (lam.beta, -plot_range,plot_range), (lam.alpha, 0, plot_range))
+ implicit_plot(Gamma, (lam.beta, -plot_range,plot_range), (lam.alpha, 0, plot_range), rgbcolor="green")
+ line([(mu,0),(mu,plot_range)], rgbcolor = "red")
+ text(r"$\beta=\mu$", (mu, -0.5), fontsize="x-large", rgbcolor="red")
+ text(r"$\nu = 0$", (mu+1, -0.5), fontsize="x-large", rgbcolor="blue")
+ text(r"$\lambda = 0$", (mu-1.5, -0.5), fontsize="x-large", rgbcolor="green")
)
p.tick_label_color("white")
p.ymin(0)
p
```
%% Output
Graphics object consisting of 6 graphics primitives
...
...
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main.tex
+
8
−
0
View file @
05dfb000
...
...
@@ -440,4 +440,12 @@
\end{itemize}
\end{frame}
\begin{frame}
\resizebox
{
\hsize
}{
!
}{
\sageplot
{
fig7.plot()
}
}
\end{frame}
\begin{frame}
\resizebox
{
\linewidth
}{
!
}{
\sageplot
{
fig8.plot()
}
}
\end{frame}
\end{document}
This diff is collapsed.
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sagetexscripts/fig7.sage
0 → 100644
+
21
−
0
View file @
05dfb000
from pseudowalls import *
def plot():
plot_range = 4
v = Chern_Char(2,0,-1,1/2)
lam = stability.Lambda(s=1/3)
Theta = lam.rank(v)
Gamma = lam.degree(v)
mu = stability.Mumford().slope(v)
p = (
implicit_plot(Theta, (lam.beta, -plot_range,plot_range), (lam.alpha, 0, plot_range))
+ implicit_plot(Gamma, (lam.beta, -plot_range,plot_range), (lam.alpha, 0, plot_range), rgbcolor="green")
+ line([(mu,0),(mu,plot_range)], rgbcolor = "red")
+ text(r"$\beta=\mu$", (mu, -0.5), fontsize="x-large", rgbcolor="red")
+ text(r"$\nu = 0$", (mu+1, -0.5), fontsize="x-large", rgbcolor="blue")
+ text(r"$\lambda = 0$", (mu-1.5, -0.5), fontsize="x-large", rgbcolor="green")
)
p.tick_label_color("white")
p.ymin(0)
p
return p
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Click to expand it.
sagetexscripts/fig8.sage
0 → 100644
+
21
−
0
View file @
05dfb000
from pseudowalls import *
def plot():
plot_range = 4
v = Chern_Char(2,0,-1,1)
lam = stability.Lambda(s=1/3)
Theta = lam.rank(v)
Gamma = lam.degree(v)
mu = stability.Mumford().slope(v)
p = (
implicit_plot(Theta, (lam.beta, -plot_range,plot_range), (lam.alpha, 0, plot_range))
+ implicit_plot(Gamma, (lam.beta, -plot_range,plot_range), (lam.alpha, 0, plot_range), rgbcolor="green")
+ line([(mu,0),(mu,plot_range)], rgbcolor = "red")
+ text(r"$\beta=\mu$", (mu, -0.5), fontsize="x-large", rgbcolor="red")
+ text(r"$\nu = 0$", (mu-1, -0.5), fontsize="x-large", rgbcolor="blue")
+ text(r"$\lambda = 0$", (mu+2, -0.5), fontsize="x-large", rgbcolor="green")
)
p.tick_label_color("white")
p.ymin(0)
p
return p
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