From 7372de1285c9884c475f8f012dd4c723e7403f54 Mon Sep 17 00:00:00 2001
From: Pauli Virtanen <pauli.t.virtanen@jyu.fi>
Date: Tue, 28 Feb 2023 17:45:15 +0200
Subject: [PATCH] examples: cpr_sns: curves for different lattice spacing
---
examples/bulk_j_an.py | 8 ++++----
examples/cpr_sns.py | 32 ++++++++++++++++++++++++++++++--
2 files changed, 34 insertions(+), 6 deletions(-)
diff --git a/examples/bulk_j_an.py b/examples/bulk_j_an.py
index 37882cf..c5b4ed5 100644
--- a/examples/bulk_j_an.py
+++ b/examples/bulk_j_an.py
@@ -50,11 +50,11 @@ def j_anom(T, h, n=5, alpha_soc=0.01, perturbative=False):
# Solver without SOC
if perturbative:
- sol0 = get_solver(soc_alpha=0, eta=0, h=h)
+ sol0 = get_solver(soc_alpha=0, eta=0, h=h, n=n)
# Solver for evaluating current perturbatively in SOC
eta = 1.0
- sol = get_solver(soc_alpha=alpha_soc, eta=eta, h=h, D=1.0)
+ sol = get_solver(soc_alpha=alpha_soc, eta=eta, h=h, D=1.0, n=n)
E_typical = 10 + 10 * T
w, a = get_matsubara_sum(T, E_typical)
@@ -104,9 +104,9 @@ def main():
T = 0.02
alpha_soc = 0.05
Da2 = alpha_soc**2
- h = np.linspace(0, 1.75, 29)
+ h = np.linspace(0, 1.75, 79)
- res = j_anom(T, h, alpha_soc=alpha_soc, perturbative=False, mem=mem)
+ res = j_anom(T, h, n=20, alpha_soc=alpha_soc, perturbative=False, mem=mem)
j = np.asarray([x[0] for x in res])
j_an = np.asarray([x[1] for x in res])
diff --git a/examples/cpr_sns.py b/examples/cpr_sns.py
index f245acf..6bc1a6a 100644
--- a/examples/cpr_sns.py
+++ b/examples/cpr_sns.py
@@ -79,21 +79,49 @@ def j(T, h, phi, n=15, eta=0.1, alpha_soc=0.1, L=10):
def main():
T = 0.1
alpha_soc = 0.9
- h = np.r_[0.0, 0.25, 0.5, 1.0, 1.5]
+ h = np.r_[0.0, 1.0]
phi = np.linspace(-pi, pi, 37)
- res = j(T, h[:, None], phi[None, :], alpha_soc=alpha_soc, L=3, mem=mem)
+ res = j(T, h[:, None], phi[None, :], alpha_soc=alpha_soc, L=3, n=20, mem=mem)
+ res0 = j(T, h[:, None], phi[None, :], alpha_soc=alpha_soc, L=3, mem=mem)
+ res1 = j(T, h[:, None], phi[None, :], alpha_soc=alpha_soc, L=3, n=10, mem=mem)
Jx_mean = np.asarray(
[Res(*x).Jx[1:-1].sum(axis=1).mean(axis=0) for x in res.flat]
).reshape(res.shape)
+ Jx0_mean = np.asarray(
+ [Res(*x).Jx[1:-1].sum(axis=1).mean(axis=0) for x in res0.flat]
+ ).reshape(res.shape)
+
+ Jx1_mean = np.asarray(
+ [Res(*x).Jx[1:-1].sum(axis=1).mean(axis=0) for x in res1.flat]
+ ).reshape(res.shape)
+
plt.plot(phi / pi, Jx_mean.T)
+ plt.plot(phi / pi, Jx0_mean.T, 'k:')
+ plt.plot(phi / pi, Jx1_mean.T, 'k:')
plt.xlabel(r"$\varphi / \pi$")
plt.ylabel(r"$I$")
plt.legend(h, title="$h$")
plt.savefig("cpr_sns.pdf")
+ Jx_sym = (Jx_mean + Jx_mean[:,::-1])/2
+ Jx0_sym = (Jx0_mean + Jx0_mean[:,::-1])/2
+ Jx1_sym = (Jx1_mean + Jx1_mean[:,::-1])/2
+ plt.clf()
+ plt.plot(phi / pi, Jx_sym.T)
+ plt.plot(phi / pi, Jx0_sym.T, 'k:')
+ plt.plot(phi / pi, Jx1_sym.T, 'k:')
+ plt.xlabel(r"$\varphi / \pi$")
+ plt.ylabel(r"$[I(\varphi)+I(-\varphi)]/2$")
+ plt.legend(h, title="$h$")
+ plt.savefig("cpr_sns_sym.pdf")
+
+ print(Jx_mean.mean(axis=1))
+ print(Jx0_mean.mean(axis=1))
+ print(Jx1_mean.mean(axis=1))
+
if __name__ == "__main__":
main()
--
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