From 90a541e547c1d6c6742173f1e9cf9901e734a0f7 Mon Sep 17 00:00:00 2001
From: "isidora.araya" <isidora.araya@ug.uchile.cl>
Date: Wed, 15 Jun 2022 20:11:06 +0200
Subject: [PATCH] add instructions on how to save and load qsymm models
---
docs/source/tutorial/basics.rst | 21 +++
qsymm/json_serialization/convert_func.py | 74 ---------
qsymm/json_serialization/minimal_model.ipynb | 151 -------------------
qsymm/json_serialization/model.json | 1 -
4 files changed, 21 insertions(+), 226 deletions(-)
delete mode 100644 qsymm/json_serialization/convert_func.py
delete mode 100644 qsymm/json_serialization/minimal_model.ipynb
delete mode 100644 qsymm/json_serialization/model.json
diff --git a/docs/source/tutorial/basics.rst b/docs/source/tutorial/basics.rst
index 10f0fa6..1ee6f79 100644
--- a/docs/source/tutorial/basics.rst
+++ b/docs/source/tutorial/basics.rst
@@ -209,3 +209,24 @@ It is exactly the Hamiltonian family we started with.
For more detailed examples see :ref:`tutorial_kdotp_generator`, :ref:`tutorial_bloch_generator`
and :ref:`tutorial_kekule`.
+
+
+Saving and loading Qsymm models
+-------------------------------------------------
+We can save and load Qsymm models.
+
+To save we do:
+
+.. jupyter-execute::
+
+ H2D_str = str(H2D.tosympy(nsimplify=True))
+
+ file = open("H2D.txt", "w")
+ file.write(H2D_str)
+ file.close()
+
+To load we do:
+
+.. jupyter-execute::
+ f = open('H2D.txt','r').read()
+ loaded_H2D = qsymm.Model(sympy.parsing.sympy_parser.parse_expr(f), momenta=['k_x', 'k_z'])
diff --git a/qsymm/json_serialization/convert_func.py b/qsymm/json_serialization/convert_func.py
deleted file mode 100644
index 326eb23..0000000
--- a/qsymm/json_serialization/convert_func.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import numpy as np
-import sympy
-import re
-import json
-from astropy.utils.misc import JsonCustomEncoder
-import qsymm
-from icecream import ic
-
-def bloch_model_to_json(model, name):
- name = name + '.json'
- model_dict = {str(key) : model[key].tolist() for key in model.keys()}
- with open(name, 'w') as f:
- json.dump(model_dict, f, cls = JsonCustomEncoder)
-
-def bloch_model_from_json(name):
- with open("json_models/" + name + ".json", "r") as f:
- model = json.load(f, cls=JsonCustomDecoderExp)
- return qsymm.Model(model, momenta=["k_x", "k_y"])
-
-
-class JsonCustomDecoderExp(json.JSONDecoder):
- """
- A decoder function tailored specifically to our purposes: take a dictionary of strings, output a dictionary
- of symbolic keys (Mul types etc.) and arrays.
- """
-
- def __init__(self, *args, **kwargs):
- json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs)
-
- def object_hook(self, obj):
- new_dict = dict()
- for key in obj.keys():
- # back-convert the key from string to symbols
- symbolic = []
- # find exponents, works for arbitrary number of exponents
- exp_full = re.findall('\*?e\*\*\(-?I\*k_\w\)\*?', key) # gives list with exponential terms in key
- if exp_full:
- coef = key
- for exp in exp_full:
- coef = coef.replace(exp, '') # constant coefficient
-
- exp_power_pl = re.findall('\*?e\*\*\(I\*(k_\w)\)\*?', key) # gives list of positive exponents
- exp_power_mn = re.findall('\*?e\*\*\(-I\*(k_\w)\)\*?', key) # gives list of negative exponents
-
- if len(exp_power_pl)>=1:
- sym_exp_pls = []
- for exp in exp_power_pl:
- sym_exp_pls.append(sympy.Pow(sympy.Symbol('e'), sympy.I*sympy.Symbol(exp)))
- if len(exp_power_mn)==0: # no negative powers
- symbolic_key = sympy.Mul(*sym_exp_pls, sympy.Symbol(coef))
-
- if len(exp_power_mn)>=1:
- sym_exp_mns = []
- for exp in exp_power_mn:
- sym_exp_mns.append(sympy.Pow(sympy.Symbol('e'), -sympy.I*sympy.Symbol(exp)))
- if len(exp_power_pl)==0: # no positive powers
- symbolic_key = sympy.Mul(*sym_exp_mns, sympy.Symbol(coef))
- else: # both positive and negative powers
- symbolic_key = sympy.Mul(*sym_exp_mns, *sym_exp_pls, sympy.Symbol(coef))
- else:
- symbolic_key = sympy.Symbol(key)
-
- assert str(key)==str(symbolic_key) # make sure converter works
- array = []
- for line in obj[key]:
- lineski = []
- for entry in line:
- lineski.append(entry[0] + 1j * entry[1])
- array.append(lineski)
- new_dict[symbolic_key] = np.array(array)
- return new_dict
-
-
-
diff --git a/qsymm/json_serialization/minimal_model.ipynb b/qsymm/json_serialization/minimal_model.ipynb
deleted file mode 100644
index 36dfa66..0000000
--- a/qsymm/json_serialization/minimal_model.ipynb
+++ /dev/null
@@ -1,151 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "ec96970a-359f-4e6f-b2d9-529749253dab",
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import tinyarray\n",
- "import qsymm\n",
- "import sympy\n",
- "from sympy import Matrix\n",
- "import string\n",
- "from convert_func import bloch_model_from_json, bloch_model_to_json"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "d7832d0c-1b99-47aa-9a40-00091eda980e",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/latex": [
- "$\\displaystyle \\left[\\begin{matrix}0.5 e^{i k_{x}} - 0.5 i e^{i k_{y}} + 0.5 i e^{- i k_{y}} + 0.5 e^{- i k_{x}} & 0\\\\0 & - 0.5 e^{i k_{x}} - 0.5 i e^{i k_{y}} + 0.5 i e^{- i k_{y}} - 0.5 e^{- i k_{x}}\\end{matrix}\\right]$"
- ],
- "text/plain": [
- "Matrix([\n",
- "[0.5*e**(I*k_x) - 0.5*I*e**(I*k_y) + 0.5*I*e**(-I*k_y) + 0.5*e**(-I*k_x), 0],\n",
- "[ 0, -0.5*e**(I*k_x) - 0.5*I*e**(I*k_y) + 0.5*I*e**(-I*k_y) - 0.5*e**(-I*k_x)]])"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "sigma_x = tinyarray.array([[0, 1], [1, 0]])\n",
- "sigma_y = tinyarray.array([[0, -1j], [1j, 0]])\n",
- "sigma_z = tinyarray.array([[1, 0], [0, -1]])\n",
- "\n",
- "#create qsymm model\n",
- "ham = \" cos(k_x) * sigma_z + sin(k_y) * eye(2) \"\n",
- "H = qsymm.Model(ham, momenta=[\"k_x\", \"k_y\"])\n",
- "H.tosympy()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "309fb778-7db3-4a02-a163-ab6479aa7711",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "8 1\n"
- ]
- },
- {
- "data": {
- "text/latex": [
- "$\\displaystyle \\left[\\begin{matrix}d + e^{i k_{x}} i + \\frac{e^{i k_{x}}}{2} + e^{i k_{y}} m + i e^{i k_{y}} q - \\frac{i e^{i k_{y}}}{2} + e^{- i k_{y}} m - i e^{- i k_{y}} q + \\frac{i e^{- i k_{y}}}{2} + e^{- i k_{x}} i + \\frac{e^{- i k_{x}}}{2} & e^{i k_{x}} j + i e^{i k_{x}} l + e^{i k_{y}} n + i e^{i k_{y}} r + f + i h + e^{- i k_{y}} o - i e^{- i k_{y}} s + e^{- i k_{x}} j + i e^{- i k_{x}} l\\\\e^{i k_{x}} j - i e^{i k_{x}} l + e^{i k_{y}} o + i e^{i k_{y}} s + f - i h + e^{- i k_{y}} n - i e^{- i k_{y}} r + e^{- i k_{x}} j - i e^{- i k_{x}} l & e^{i k_{x}} k - \\frac{e^{i k_{x}}}{2} + e^{i k_{y}} p + i e^{i k_{y}} t - \\frac{i e^{i k_{y}}}{2} + g + e^{- i k_{y}} p - i e^{- i k_{y}} t + \\frac{i e^{- i k_{y}}}{2} + e^{- i k_{x}} k - \\frac{e^{- i k_{x}}}{2}\\end{matrix}\\right]$"
- ],
- "text/plain": [
- "Matrix([\n",
- "[d + e**(I*k_x)*i + e**(I*k_x)/2 + e**(I*k_y)*m + I*e**(I*k_y)*q - I*e**(I*k_y)/2 + e**(-I*k_y)*m - I*e**(-I*k_y)*q + I*e**(-I*k_y)/2 + e**(-I*k_x)*i + e**(-I*k_x)/2, e**(I*k_x)*j + I*e**(I*k_x)*l + e**(I*k_y)*n + I*e**(I*k_y)*r + f + I*h + e**(-I*k_y)*o - I*e**(-I*k_y)*s + e**(-I*k_x)*j + I*e**(-I*k_x)*l],\n",
- "[ e**(I*k_x)*j - I*e**(I*k_x)*l + e**(I*k_y)*o + I*e**(I*k_y)*s + f - I*h + e**(-I*k_y)*n - I*e**(-I*k_y)*r + e**(-I*k_x)*j - I*e**(-I*k_x)*l, e**(I*k_x)*k - e**(I*k_x)/2 + e**(I*k_y)*p + I*e**(I*k_y)*t - I*e**(I*k_y)/2 + g + e**(-I*k_y)*p - I*e**(-I*k_y)*t + I*e**(-I*k_y)/2 + e**(-I*k_x)*k - e**(-I*k_x)/2]])"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#find symmetries\n",
- "candidates = qsymm.groups.square()\n",
- "discrete_symm, continuous_symm = qsymm.symmetries(H, candidates)\n",
- "print(len(discrete_symm), len(continuous_symm))\n",
- "\n",
- "abcd_sympy = list(sympy.symbols('a:z'))\n",
- "abcd_list = list(string.ascii_lowercase)\n",
- "del abcd_sympy[4]\n",
- "del abcd_list[4]\n",
- "\n",
- "norbs = [('A', 2)] # A atom per unit cell, 4 orbitals each\n",
- "hopping_vectors = [('A', 'A', [0, 1]), ('A', 'A', [1, 0])] \n",
- "\n",
- "#find all temrs compatible with 1 specific symmetry\n",
- "family= qsymm.bloch_family(hopping_vectors, [discrete_symm[1]], norbs)\n",
- "\n",
- "#add all the terms compatible with the symmetry\n",
- "H = (H + sum([symbol * term for symbol, term in zip(abcd_sympy[3:], family)]))\n",
- "H.tosympy(nsimplify=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "387f8212-1861-4875-8fc5-798cb5d83220",
- "metadata": {},
- "outputs": [],
- "source": [
- "bloch_model_to_json(H, 'model')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "de61fbf0-f6a6-43db-b32c-300e408fcbd3",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f5cb9c98-cc14-448d-8a03-41df556ce3d2",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.10"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/qsymm/json_serialization/model.json b/qsymm/json_serialization/model.json
deleted file mode 100644
index 8f80422..0000000
--- a/qsymm/json_serialization/model.json
+++ /dev/null
@@ -1 +0,0 @@
-{"e**(-I*k_x)": [[[0.5, 0.0], [0.0, 0.0]], [[0.0, 0.0], [-0.5, 0.0]]], "e**(-I*k_y)": [[[0.0, 0.5], [0.0, 0.0]], [[0.0, 0.0], [0.0, 0.5]]], "e**(I*k_x)": [[[0.5, 0.0], [0.0, 0.0]], [[0.0, 0.0], [-0.5, 0.0]]], "e**(I*k_y)": [[[0.0, -0.5], [0.0, 0.0]], [[0.0, 0.0], [0.0, -0.5]]], "e**(-I*k_x)*l": [[[-0.0, 0.0], [0.0, 1.0]], [[0.0, -1.0], [-0.0, 0.0]]], "e**(-I*k_x)*j": [[[0.0, 0.0], [1.0, 0.0]], [[1.0, 0.0], [0.0, 0.0]]], "e**(I*k_y)*s": [[[0.0, 0.0], [0.0, 0.0]], [[0.0, 1.0], [0.0, 0.0]]], "e**(-I*k_y)*o": [[[0.0, 0.0], [1.0, 0.0]], [[0.0, 0.0], [0.0, 0.0]]], "e**(-I*k_y)*t": [[[0.0, 0.0], [0.0, 0.0]], [[0.0, 0.0], [0.0, -1.0]]], "e**(-I*k_y)*m": [[[1.0, 0.0], [0.0, 0.0]], [[0.0, 0.0], [0.0, 0.0]]], "e**(I*k_y)*o": [[[0.0, 0.0], [0.0, 0.0]], [[1.0, 0.0], [0.0, 0.0]]], "e**(I*k_y)*t": [[[0.0, 0.0], [0.0, 0.0]], [[0.0, 0.0], [0.0, 1.0]]], "e**(I*k_y)*m": [[[1.0, 0.0], [0.0, 0.0]], [[0.0, 0.0], [0.0, 0.0]]], "e**(-I*k_y)*r": [[[0.0, 0.0], [0.0, 0.0]], [[0.0, -1.0], [0.0, 0.0]]], "e**(-I*k_y)*n": [[[0.0, 0.0], [0.0, 0.0]], [[1.0, 0.0], [0.0, 0.0]]], "e**(-I*k_x)*i": [[[1.0, 0.0], [0.0, 0.0]], [[0.0, 0.0], [0.0, 0.0]]], "e**(I*k_x)*j": [[[0.0, 0.0], [1.0, 0.0]], [[1.0, 0.0], [0.0, 0.0]]], "e**(I*k_x)*l": [[[-0.0, 0.0], [0.0, 1.0]], [[0.0, -1.0], [-0.0, 0.0]]], "e**(-I*k_x)*k": [[[0.0, 0.0], [0.0, 0.0]], [[0.0, 0.0], [1.0, 0.0]]], "e**(-I*k_y)*p": [[[0.0, 0.0], [0.0, 0.0]], [[0.0, 0.0], [1.0, 0.0]]], "d": [[[1.0, 0.0], [0.0, 0.0]], [[0.0, 0.0], [0.0, 0.0]]], "f": [[[0.0, 0.0], [1.0, 0.0]], [[1.0, 0.0], [0.0, 0.0]]], "e**(I*k_y)*n": [[[0.0, 0.0], [1.0, 0.0]], [[0.0, 0.0], [0.0, 0.0]]], "h": [[[-0.0, 0.0], [0.0, 1.0]], [[0.0, -1.0], [-0.0, 0.0]]], "e**(I*k_y)*p": [[[0.0, 0.0], [0.0, 0.0]], [[0.0, 0.0], [1.0, 0.0]]], "e**(-I*k_y)*q": [[[0.0, -1.0], [0.0, 0.0]], [[0.0, 0.0], [0.0, 0.0]]], "e**(I*k_y)*r": [[[0.0, 0.0], [0.0, 1.0]], [[0.0, 0.0], [0.0, 0.0]]], "g": [[[0.0, 0.0], [0.0, 0.0]], [[0.0, 0.0], [1.0, 0.0]]], "e**(I*k_y)*q": [[[0.0, 1.0], [0.0, 0.0]], [[0.0, 0.0], [0.0, 0.0]]], "e**(I*k_x)*i": [[[1.0, 0.0], [0.0, 0.0]], [[0.0, 0.0], [0.0, 0.0]]], "e**(I*k_x)*k": [[[0.0, 0.0], [0.0, 0.0]], [[0.0, 0.0], [1.0, 0.0]]], "e**(-I*k_y)*s": [[[0.0, 0.0], [0.0, -1.0]], [[0.0, 0.0], [0.0, 0.0]]]}
\ No newline at end of file
--
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