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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "7c1065dd",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pyerrors as pe"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "20f67709",
"metadata": {},
"outputs": [],
"source": [
"plt.style.use('./base_style.mplstyle')\n",
"plt.rc('text', usetex=True)"
]
},
{
"cell_type": "markdown",
"id": "e5764fd0",
"metadata": {},
"source": [
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"We can load data from a preprocessed file which contain a list of `pyerror` `Obs`:"
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]
},
{
"cell_type": "code",
"execution_count": 3,
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"id": "fbfa65f5",
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"metadata": {},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data has been written using pyerrors 2.0.0.\n",
"Format version 0.1\n",
"Written by fjosw on 2022-01-06 11:11:19 +0100 on host XPS139305, Linux-5.11.0-44-generic-x86_64-with-glibc2.29\n",
"\n",
"Description: Test data for the correlator example\n"
]
}
],
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"source": [
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"correlator_data = pe.input.json.load_json(\"./data/correlator_test\")"
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]
},
{
"cell_type": "markdown",
"id": "ae93c7c2",
"metadata": {},
"source": [
"With this list a `Corr` object can be initialised"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "33a8fdec",
"metadata": {},
"outputs": [],
"source": [
"my_correlator = pe.correlators.Corr(correlator_data)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "5f954607",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x0/a\tCorr(x0/a)\n",
"------------------\n",
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"8\t 548(13)\n",
"9\t 433(11)\n",
"10\t 343.1(8.6)\n",
"11\t 273.2(6.6)\n",
"12\t 217.5(5.6)\n",
"13\t 172.9(4.9)\n",
"14\t 137.6(4.6)\n",
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"\n"
]
}
],
"source": [
"my_correlator.print([8, 14])"
]
},
{
"cell_type": "markdown",
"id": "b00d670b",
"metadata": {},
"source": [
"The `show` method can display the correlator"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "b71529d0",
"metadata": {},
"outputs": [
{
"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAlYAAAGLCAYAAAAF7dxzAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAdx0lEQVR4nO3dUYid14Ef8P+RvVjsVjMTuQRr64RqxFLqNZSV7YVA89KV00BxnySnlLzGrulDaR+kCrYYkwcjPXTbQglSXk3pRqJs6y01eFIoW1jYOGILxixtPS6JE7nbRLoz3t1IydqnD/e78tXVnZl7R+fOvTPz+8FF+r5z7tX5dCR9f51zvnNLrTUAADy8I/NuAADAQSFYAQA0IlgBADQiWAEANCJYAQA0IlgBADQiWAEANPLovBuwl0opJcmvJvl43m0BAPaVY0l+XHfYAPRQBav0Q9WH824EALAvPZnkR9tVOGzB6uMk+eEPf5ilpaV5twUA2Ac2NzfzhS98IZlgxuuwBaskydLSkmAFADRn8ToAQCOCFQBAI4IVAEAjghUAQCOCFQBAI4IVAEAjghUAQCOCFQBAI4IVAEAjghUAQCOCFQBAI4IVAEAjghUAQCOCFQBAI4IVAEAjghUAQCOCFQBAI4IVAEAjghUAQCOCFQBAI4/OuwHz8MmnNX/4/k/zpx/fyeePHc1vnjyeR46UeTcLANjnDmWw+srv/Lf8v7uP3Ds+sXw0r77wVL769Ik5tgoA2O8O5VTg/928e9/xRxt38sobN/LWuzfn1CIA4CA4lMFqVO1ev/177+Zm72fzbg4AsE8JVkN+8mc/zzd//715NwMA2KcEqxE/+8Un824CALBPCVYjXnz2C/NuAgCwTx3KpwLHKUmeWD6ar/z6E/NuCgCwTx3KEavRHasGx6++8JT9rACAXTuUwerzS4/dd/zE8tF86+un7WMFADyUUmuddxv2TCllKcnGrdu9/MlP/9LO6wDAjjY3N7O8vJwky7XWze3qHso1Vo8cKfnSqcfn3QwA4IA5lFOBAACzsKsRq1LKpSTvd4e3aq3Xh8rOJ+l1hyu11ssj751pOQDAvEwVrEopK0m+m+S3aq29UsrpJN9P92BdF3pSa73aHZ8ppVyptb68F+UAAPM01eL1UsqVJO8PjxKVUs7UWte6n99OcrLW2hsqr7XWshflE7R/KcnGxsZGlpaWJr5uAODwmmbx+rRrrF5Kcr2UslpKOZMkQ6FqNf2pud7om7qRpZmWj2tsKeWxUsrS4JXk2OSXCgAwnYmDVRdskuR0kpUk66WUK0OhZnXsG/vroVb2oHyci0k2hl4fblEPAOChTTNiNQg2vVrrjVrrepILSa7t8L5bSY7Pqfz1JMtDrye3+RwAgIeym6cC3xn8pFvAvrLVVFxnu1A00/Ja690kdwfHpdgEFACYnWlGrNa3ON9LfzRrq/KVrmzW5QAAczVxsOqm/tbz4FqnlSTvdOW9obVYw+9dm3X5pNcBADAr0z4VeCHJ1wYHpZSzSdZqrTe6U68nOTNSfnXo/bMuBwCYm6m/hLmU8lI+ewrv8VrrhZHy8/lsau65vS7foe32sQIApjLNPlZTB6v9TLACAKY1yw1CAQDYgmAFANCIYAUA0IhgBQDQiGAFANCIYAUA0IhgBQDQiGAFANCIYAUA0IhgBQDQiGAFANCIYAUA0IhgBQDQiGAFANCIYAUA0IhgBQDQiGAFANCIYAUA0IhgBQDQiGAFANCIYAUA0IhgBQDQiGAFANCIYAUA0IhgBQDQiGAFANCIYAUA0IhgBQDQiGAFANCIYAUA0IhgBQDQyKPTVC6lnEnycpK3k6wneT7J92qt14fqnE/S6w5Xaq2XRz5jpuUAAPMy7YjVSpIzSa50r/fHhKrUWq/WWq8muVFKubJX5QAA81RqrZNXLuVskrVaa2+L8ttJTg6Xl1JqrbXsRfkE7V9KsrGxsZGlpaWJrhkAONw2NzezvLycJMu11s3t6jZbY1VKWU1/aq43puzMrMu3aNNjpZSlwSvJsSkvCwBgYlOtseq8WEq5leR4klO11gvd+dUt6vfSn0Kcdfk4F5O8ukUZAEBT045Y3Uh/KvB6t8bp/VLKtR3eMwhh8yh/Pcny0OvJbT4HAOChTDViVWtdHzn1nSRXSikr27xtu1A00/Ja690kdwfHpUy0FAsAYFemGrHqFq/fM7TeaTX97RfGWenKZl0OADBXEwerblTqWreIfPhckqx3o1m94fKBWuvarMsnvQ4AgFmZOFh1o1OXR6YDX0pyfWjk6vX097lKcm+E6+pQ/VmXAwDMzbT7WK2kH6YGHh96KnBQ53w+m5p7bq/Ld2i/fawAgKlMs4/VVMFqvxOsAIBpzWWDUACAw06wAgBoRLACAGhEsAIAaESwAgBoRLACAGhEsAIAaESwAgBoRLACAGhEsAIAaESwAgBoRLACAGhEsAIAaESwAgBoRLACAGhEsAIAaESwAgBoRLACAGhEsAIAaESwAgBoRLACAGhEsAIAaESwAgBoRLACAGhEsAIAaESwAgBoRLACAGhEsAIAaESwAgBoRLACAGhEsAIAaOTRh3lzKeXtWuvzI+fOJ+l1hyu11st7WQ4AMC+7HrEqpZxNcmbk3PkkqbVerbVeTXKjlHJlr8oBAOap1Fqnf1MpK0leTHKl1lqGzt9OcrLW2hs6Vwd1Zl0+QbuXkmxsbGxkaWlp2ssGAA6hzc3NLC8vJ8lyrXVzu7q7HbF6Mcl3hk+UUlbTn5rrjVYupZyZdfm4RpZSHiulLA1eSY5NcnEAALsxdbDqQszamKLVLd7SS7KyB+XjXEyyMfT6cIt6AAAPbTcjViu11vUp6t9KcnxO5a8nWR56PbnN5wAAPJSpngospbzULRqfxnahaKbltda7Se4OjkuZaCkWAMCuTDxiVUo5neSdbapsNYq10pXNuhwAYK6mGbE6nuT00ELxU8m9LRDWa63XSym9Usrq6FRhrXWtqzvTcgCAeZp4xKrWulZrvTx4JbnSnb9ca73eVXs9Q3tbdXtdDU8dzrocAGBudruP1dkkX0tyNsnlJG8PjSqdz2dTc8/VWi+MvHem5Tu02z5WAMBUptnHalfBar8SrACAae3FBqEAAIwQrAAAGhGsAAAaEawAABoRrAAAGhGsAAAaEawAABoRrAAAGhGsAAAaEawAABoRrAAAGhGsAAAaEawAABoRrAAAGhGsAAAaEawAABoRrAAAGhGsAAAaEawAABoRrAAAGhGsAAAaEawAABoRrAAAGhGsAAAaEawAABoRrAAAGhGsAAAaEawAABoRrAAAGhGsAAAaEawAABp5dJrKpZSVJC92h6eSrCb5Rq21N1TnfJLB8Uqt9fLIZ8y0HABgXqYdsbqUZK3WerXWeiHJrSTXBoVd6ElXfjXJjVLKlb0qBwCYp1JrnbxyKW8neXswStQFnYu11s91x7eTnBwZwaq11rIX5RO0fynJxsbGRpaWlia+bgDg8Nrc3Mzy8nKSLNdaN7erO9VUYK31+ZFTzyVZS5JSymr6U3O90feVUs4kWZ9lea11bZprAQBobapgNayUcjbJSpJz3anVLar2unqzLh/XxseSPDZ06tgWnwEA8NCmfiqwlLJSSnkp/aBzbdwI0ohbSY7Pqfxiko2h14fbfA4AwEOZOljVWnvd4vHBOqvb3dOCW9kuFM26/PUky0OvJ3f4LACAXZs4WHUjVZdGQtRa+tNwgzVQ46x0ZbMuf0Ct9W6tdXPwSvLxFp8BAPDQphmxWk1yPvePEK10P/ZqretJet0i9vvUWtdmXT7FdQAAzMTEwarWeiPJ5S7gDHwtyY2hYPN6+qNXSe4tcL86VH/W5QAAczPtPlYrSV4aOnUqyYUxO68Pwtdz3Uai2avyHdpvHysAYCrT7GM1VbDa7wQrAGBa0wQrX8IMANCIYAUA0IhgBQDQiGAFANCIYAUA0IhgBQDQiGAFANCIYAUA0IhgBQDQiGAFANCIYAUA0IhgBQDQiGAFANCIYAUA0IhgBQDQyKPzbsAi+eTTmj/64Fb+9OM7+fyxo/nNk8fzyJEy72Y
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"text/plain": [
"<Figure size 640x395.55 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"my_correlator.show()"
]
},
{
"cell_type": "markdown",
"id": "c659557e",
"metadata": {},
"source": [
"## Manipulating correlators"
]
},
{
"cell_type": "markdown",
"id": "416cf39a",
"metadata": {},
"source": [
"`Corr` objects can be shifted"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "e8d65dd5",
"metadata": {},
"outputs": [],
"source": [
"shifted_correlator = my_correlator.roll(20)\n",
"shifted_correlator.tag = r'Correlator shifted by $x_0/a=20$'"
]
},
{
"cell_type": "markdown",
"id": "634dd613",
"metadata": {},
"source": [
"Or symmetrised"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "127a661d",
"metadata": {},
"outputs": [],
"source": [
"symmetrised_correlator = my_correlator.symmetric()\n",
"symmetrised_correlator.tag = 'Symmetrised correlator'"
]
},
{
"cell_type": "markdown",
"id": "3d733872",
"metadata": {},
"source": [
"And we can compare different `Corr` objects by passing `comp` to the `show` method"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "8e264aed",
"metadata": {},
"outputs": [
{
"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAk0AAAGNCAYAAAAM+kVxAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAA9hAAAPYQGoP6dpAABjNklEQVR4nO3dfXxU5Z03/s8VlIcKZHgoEgGRoSIGrHQAH7bSbjVBaBVtG54sW3+tNam33XZX3WTj6o+71TZ3oth721qa0Idt1yqStPWB3wpkxC24i4UkUoWoaAYVMZhCmBBcHmpy/f645kzOTM6ZOWcyM+ecmc/79cprMmfOzFwzJ5nznev6Xt9LSClBRERERIkVON0AIiIiIi9g0ERERERkAYMmIiIiIgsYNBERERFZwKCJiIiIyAIGTUREREQWMGgiIiIissBVQZMQolEIEYj81DrdHiIiIiLNOU43II4fwPMAWgAsd7gtRERERFHCTRXBhRBlUsqmFO8rAFwAoDe9rSIiIqIcNwbA+zJJUJT2niYhhA/ACgDLpZSlBrdXAghHrvqklHW6mxeq2AfjAUBK2WDjqS8A8F4KTSYiIiKaCuBwoh3S2tMkhAgAWADAB2CllHJ+3O2VAKAFSkKIEqjgqsLgsToAzJdShi0+91gAPYcOHcLYsWOH8jKIiIgoT5w4cQLTpk0DgEIp5YlE+6a1p0lK2QagTQhRZrJLNYAZuv2DQohmABWR+yyUUlZFbg5D5Ti12WnD2LFjGTQRERFR2mUtEVwI4Ycajgsb3FYCIISBYTtE9rUVMBERERFlSjZnz/lNtoehAqSgEKJM63ECMCgfSk8IMQLACN2mMWlpJREREZEBN5Qc6MZA4rc2c87KDLpqAGsz1SgiIiIiPTcUtxyf4v1qABTqfqamrUVEREREcbLZ0xQy2e5LcJspKeUZAGeEEHcCuBPuCACJiIgoR2Ut0JBShgCEIwnh8bcFh/C4j0opiwFcMZT2ERERESWSqaDJbMitBkCJdiWS9G2ngCURERGRI9I6PBfpRSoDsBKAtujuHi3BW0pZJ4So1NVxWmhU2NLmc8YOz/X3AQd3Aic/AEafD0z/G6Bg2FCegjKsr19i98FudPWexqQxI3HFjPEYViCcbhYREVEMV609NxRaRfCemksx9rSuCrrvQmDx94HiZY61jcxt2deJ7z7bjs6e09FtRYUjsfbGYiyZW+Rgy4iIKB+cOHEChYWFgIWK4LmXPD3xUuC2IFB9WF1OmgNs+irQ/ozTLaM4W/Z14o7H2mICJgA40nMadzzWhi37Oh1qGRER0WCe72mKG567pOf4cYz1+QZ26O8HNt4CdLUD336ZQ3Uu0dcvcU3t9kEBk0YAmFw4Ei9WXZuxobquE6fR1Xsm6X6TxozApLEjM9IGIiJylp2eJjcUtxwSKeWjAB7VhudQENd5VlAALLoL+EUp8PJjwPxbnWgmxWlsedc0YAIACaCz5zQaW97FqiumZ6QNv/3Tu/jX599Mut93rrsY/1g6KyNtICIi7/B80GTJpEvV5a6fMGhyiQ07D1reL1NB01euvBClxedHr7/VdRL/8ORe/N+V8/CJSaOj2yeNGWF0dyKKCIfD8Ol7+IlylOdzmoQQdwoh2gHsNt2p6zV1efW3stMoSur2RTPSul8qJo0diblTCqM/WqD0iUmjY7Y7MTRXVVWFqqoq1NXVoaGhAU1NTdHt2dbW1obS0lLMnDkz68+dKq3N8+fPT+n+wWAQFRUVqKioiL73Q1VRUYFx48YhGExcli4YDGL+/PkoLU24/KarVFVVIRwOO90MoozzfNA0qLhlf3/sDv39wM5HAN904FNrst9AMrR8wYUoKhwJs2wlATWLbvmCC7PSnr5+iVfeCwMAXnkvjL5+Z3L92traoifM2tpaVFZWory8HIFAAMuXL0/bCdyOQCCQcrDW0OBMGTatzclO5FVVVVi+fPmg7dr7v3z58mgPylBfS319Pfx+s3XLB5SUlKC6uhrd3d1Der5sc0NPU11dHerq6qIBr9HtDQ0NaGhoQF1dnQMtJK/zfNA0SNNtwKHdwJledbnxFuDAFmDxg0wCd5FhBQJrbywGgEGBk3Z97Y3FWanXtGVfJ66p3Y57/7APAHDvH/bhmtrtjszeW758OWpra1FSUhKz3e/3G54EsmX8+NSWiGxubk5zS6yz0ubS0lKsXLkyZltbWxv8fj98Ph9KSkqixyKbr8UNAYhVTU1NrugVq6qqQmVlJSorK1FfXw8AMe3SgqTy8vLoFxEn/6fIm3Ivp+noayrpW+ObDqz4Des0udCSuUVYvyYwqE7T5CzWadLKHsT3K2llD9avCWStXpTWmxMfMGlKSkos9VS4RUNDA0Ih28tKZpXZex0ftHjhtTjlySefRGNjo6NtCIfDaGtri8mtqqiowPz58xEKheD3+1FTU4ODBwdyKUtKSlBaWhoNsIis8HzQNKgi+Df/Czi+jxXBPWLJ3CKUFk92pCJ4X7/Ed59tHxQwAWr2ngDw3WfbUVo8OSvtaWpqMj2Ja+KHyerq6qKBVCgUQmVlJQCVF1NVVRXtodJ6SUpLSw2319bWxjyedqIpKyuDmXA4jIaGBvj9fjQ3N6OiogKBQCD6/M3NzQiFQtFv+FrbUmm31j4jWhvC4TBCoRB8Ph/Ky8ujt7e1tSEUCiEUCuHYsWPRx2pra0NVVRVCoRA6Ojqi2+rr66Pt1nqcrLwWo/esrq4OPp8v5Z46bTi2u7sb4XA4+rxNTU2oqalBKBRCY2MjSkpKEAqFUFpaCr/fn3QoUHvc5uZmVFVVIRgMoqOjAxUVFbYC83A4bPjawuEwampqsHDhQgBqaLKxsTGjPWgtLS0IhULRv0HtdWh/F2bJ6sFgMOn/HVGUlDInfgCMBSB7enokkRX//dZROb1qc9Kf/37raFbaA0DW1tZa3r+srEw2NzdHr3d0dMiSkpLo9cbGRhkIBGRzc7NsbW2VlZWVCbeXlZXJxsbG6P1LSkpka2urlFLK1tZW6ff7Y56/srJSdnR0RK/7/X55/Pjx6PXm5mYZCATS1m4jjY2Nsr6+PuaxtOutra3S5/PFPJff74++JrPXZbQt0Wsxe88qKytj2nb8+HEJIKY9ZpqbmyWAmPezvr5elpeXx+wT304rfz/19fXRxy0vL5dlZWXRtutfixX19fUx76eU6nUGAoHoc7S2tkp1qsmuxsbG6HuovZ/xfD6f7ddMuaenp0dCfVceK5PEGp7vaSJKVVeveZ2oVPbLpra2NgSDwZhhEb/fj+7u7ug3Z5/Ph7a2tui3aO0buNH2UCiEpqammMdbvnw56uvrTYcvQqEQgsFgtFfH7/cjGAwm7J0aSrvNNDY2YsWKFfD5fPD7/ViwYEH0tnA4HNOLoPUIJXtMKxK9Z7W1tairq9O+0AFQ77ud5w0EAjE9I+Xl5RBCRHviSkpK0N3djba2tkG9K4mMHz8++rihUCia15NKzlZzc3NMrx6gekNXrlwZfY7u7u6kr9tqbtH8+fMHPZ+Zmpoa1NfXJ+zdGj9+vOcS7slZDJoob00aY62UgNX9hsrv90eHicxoQ0AtLS2GJ0htqEwLFMxOovHbg8EgfD5fzHT4jo6OhHk8WrCgDX90d3cnPQENtd3xysrKUF9fj3HjxiEQCGDlypUxQ2fxj+Pz+dJ2kkz0nmm3pZvf748mqgMqkHryyScRCAQsDzPpg9qWlhbDfCT9sKAWoMXT/hbjNTQ0xPwd6wNgM+nOK9ICt2QBFgMmsotBE+WtK2aMR1HhSBzpOW2Y16Qt5XLFjNTyUewqKytLWlJA69mxWhPH7MQdvz0cDg86OSY70bW1taGmpgalpaVYsWJF0kBHyyuxwk7A0dzcHO3B0k6++sApE7TXYvaeNTU1pZzHlEj8SX7lypW47rrrUFtbi1AoZCs3RwuI49/rUCiE5ubmmBloRo9bX18/qIeora0NQGywquVNZUtTUxNmzpwZEzCZ/W1qx5DIKs+XHLBU3JLIgJvKHgADyc5mxQ/1Sbda4m+8UCgUTb61QxuiM3pOs7Zcd911qK6uRnl5OXw
2021-10-11 18:31:02 +01:00
"text/plain": [
"<Figure size 640x395.55 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"shifted_correlator.show(comp=symmetrised_correlator, logscale=True)"
]
},
{
"cell_type": "markdown",
"id": "232e88af",
"metadata": {},
"source": [
"## Effective mass"
]
},
{
"cell_type": "markdown",
"id": "83dc751c",
"metadata": {},
"source": [
"The effective mass of the correlator can be obtained by calling the `m_eff` method"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "c686f7e0",
"metadata": {},
"outputs": [],
"source": [
"m_eff = symmetrised_correlator.m_eff()\n",
"m_eff.tag = 'Effective mass'"
]
},
{
"cell_type": "markdown",
"id": "4a9d13b2",
"metadata": {},
"source": [
"We can also use the priodicity of the lattice in order to obtain the cosh effective mass"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "5acde8cf",
"metadata": {},
"outputs": [],
"source": [
"periodic_m_eff = symmetrised_correlator.m_eff('periodic')\n",
"periodic_m_eff.tag = 'Cosh effective mass'"
]
},
{
"cell_type": "markdown",
"id": "c658b000",
"metadata": {},
"source": [
"We can compare the two and see how the standard effective mass deviates form the plateau at the center of the lattice"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "1d6ea22a",
"metadata": {},
"outputs": [
{
"data": {
2022-01-06 11:16:00 +01:00
"image/png": "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
2021-10-11 18:31:02 +01:00
"text/plain": [
"<Figure size 640x395.55 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"periodic_m_eff.show([4,47], comp=m_eff, ylabel=r'$am_\\mathrm{eff}$')"
]
},
{
"cell_type": "markdown",
"id": "e3762e68",
"metadata": {},
"source": [
"Arithmetic operations and mathematical functions are also overloaded for the `Corr` class. We can compute the difference between the two variants of the effective mass as follows."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "e56d164c",
"metadata": {},
"outputs": [
{
"data": {
2022-01-06 11:16:00 +01:00
"image/png": "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
2021-10-11 18:31:02 +01:00
"text/plain": [
"<Figure size 640x395.55 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"difference_m_eff = np.abs(periodic_m_eff - m_eff)\n",
"difference_m_eff.show([0, 47], logscale=True)"
]
},
{
"cell_type": "markdown",
"id": "472ab97b",
"metadata": {},
"source": [
"## Derivatives"
]
},
{
"cell_type": "markdown",
"id": "d99414fe",
"metadata": {},
"source": [
"We can obtain derivatives of correlators in the following way"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "03007f8a",
"metadata": {},
"outputs": [],
"source": [
"first_derivative = symmetrised_correlator.deriv()\n",
"first_derivative.tag = 'First derivative'"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "c0311739",
"metadata": {},
"outputs": [],
"source": [
"second_derivative = symmetrised_correlator.second_deriv()\n",
"second_derivative.tag = 'Second derivative'"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "165550d9",
"metadata": {},
"outputs": [
{
"data": {
2022-01-06 11:16:00 +01:00
"image/png": "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"text/plain": [
"<Figure size 640x395.55 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"symmetrised_correlator.show([5, 20], comp=[first_derivative, second_derivative], y_range=[-500, 1300])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ff177781",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"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",
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"version": "3.8.10"
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}
},
"nbformat": 4,
"nbformat_minor": 5
}
