2022-03-04 17:19:56 +05:00
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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2022-04-21 19:19:33 +05:00
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"import warnings\n",
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"warnings.simplefilter(action = 'ignore')\n",
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"\n",
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2022-03-04 17:19:56 +05:00
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"from anisotropy.database import Database, tables\n",
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"import pathlib\n",
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2022-03-05 01:08:18 +05:00
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"import peewee as pw\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt"
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2022-03-04 17:19:56 +05:00
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]
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},
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{
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"cell_type": "code",
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2022-03-05 01:08:18 +05:00
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"execution_count": 2,
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2022-03-04 17:19:56 +05:00
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"metadata": {},
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"outputs": [],
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"source": [
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"db = Database(pathlib.Path(\"anisotropy.db\").resolve())\n",
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2022-03-05 01:08:18 +05:00
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"execution = 5"
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2022-03-04 17:19:56 +05:00
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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2022-03-05 01:08:18 +05:00
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"def load_data(execution: int, field: str):\n",
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" if not db.getExecution(execution):\n",
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" print(\"Execution not found\")\n",
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2022-03-04 17:19:56 +05:00
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"\n",
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2022-03-05 01:08:18 +05:00
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" for model in db.tables:\n",
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" try:\n",
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" column = getattr(model, field)\n",
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" \n",
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" except AttributeError:\n",
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" pass\n",
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2022-03-04 17:19:56 +05:00
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"\n",
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2022-03-05 01:08:18 +05:00
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" else:\n",
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" break\n",
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"\n",
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" query = model.select(tables.Shape.alpha, column, tables.Shape.direction, tables.Shape.label)\n",
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" idn = db.tables.index(model)\n",
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"\n",
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" for table in reversed(db.tables[ :idn]):\n",
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" query = query.join(table, pw.JOIN.LEFT_OUTER)\n",
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" \n",
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" query = query.switch(tables.Shape)\n",
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" query = query.where(\n",
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" tables.Shape.exec_id == execution,\n",
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" # tables.Shape.label == structure,\n",
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" )\n",
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" query = query.order_by(tables.Shape.label, tables.Shape.direction, tables.Shape.alpha)\n",
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"\n",
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" with db:\n",
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" if query.exists():\n",
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" table = []\n",
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" for row in query.dicts():\n",
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" for k in row.keys():\n",
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" if type(row[k]) == list:\n",
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" row[k] = str(row[k])\n",
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"\n",
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" table.append(row)\n",
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" \n",
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" else:\n",
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" table = None\n",
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"\n",
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" if table is None:\n",
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" print(\"Results not found\")\n",
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2022-03-04 17:19:56 +05:00
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"\n",
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" else:\n",
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2022-03-05 01:08:18 +05:00
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" return pd.DataFrame(table)\n",
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2022-03-04 17:19:56 +05:00
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"\n",
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2022-04-21 19:19:33 +05:00
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"def permeability(label, direction):\n",
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" def concrete(execution, field):\n",
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" df = load_data(execution, field)\n",
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" return df[df.label == label][df.direction == direction][field].to_numpy()\n",
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"\n",
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" alpha = concrete(7, \"alpha\")\n",
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" viscosity = concrete(5, \"viscosity\")\n",
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" viscosityKinematic = concrete(5, \"viscosityKinematic\")\n",
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" length = concrete(7, \"length\")\n",
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" flowRate = concrete(5, \"flowRate\")\n",
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" areaCellOutlet = concrete(7, \"areaCellOutlet\")\n",
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" pressureInlet = concrete(5, \"pressureInlet\")\n",
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" pressureOutlet = concrete(5, \"pressureOutlet\")\n",
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" density = concrete(5, \"density\")\n",
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"\n",
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" return viscosity * length * flowRate / (areaCellOutlet * (pressureInlet - pressureOutlet))\n",
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2022-03-04 17:19:56 +05:00
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"\n",
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2022-03-05 01:08:18 +05:00
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"def nanmean(arr):\n",
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" temp = arr.copy()\n",
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"\n",
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2022-04-21 19:19:33 +05:00
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" if np.isnan(temp[0]):\n",
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" temp[0] = temp[1]\n",
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"\n",
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2022-03-05 01:08:18 +05:00
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" for n, item in enumerate(temp):\n",
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" if np.all(np.isnan(item)):\n",
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" \n",
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" vals = temp[n - 1 : n + 2]\n",
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"\n",
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" if np.sum(~np.isnan(vals)) <= 1:\n",
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" vals = temp[n - 2 : n + 3]\n",
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"\n",
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" temp[n] = vals[~np.isnan(vals)].mean()\n",
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"\n",
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" return temp\n",
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"\n",
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2022-04-21 19:19:33 +05:00
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"def filter_group(arr, nan = True, qhigh = True, quantile = 0.97):\n",
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2022-03-05 01:08:18 +05:00
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" temp = arr.copy()\n",
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" check = True\n",
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2022-04-21 19:19:33 +05:00
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" quan = np.quantile(temp[~np.isnan(temp)], quantile)\n",
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2022-03-05 01:08:18 +05:00
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" limit = 1000\n",
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"\n",
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" while check:\n",
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" if nan and np.any(np.isnan(temp)):\n",
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" temp = nanmean(temp)\n",
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" check = True\n",
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" \n",
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" elif qhigh and np.any(quan < temp):\n",
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" temp[quan < temp] = np.nan\n",
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" check = True\n",
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"\n",
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" else:\n",
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" check = False \n",
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" \n",
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" if limit <= 0:\n",
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" break\n",
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"\n",
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" else:\n",
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" limit -= 1\n",
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"\n",
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" return temp"
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2022-03-04 17:19:56 +05:00
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]
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},
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2022-04-21 19:19:33 +05:00
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.rcParams.update({\n",
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" \"font.size\": 18,\n",
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" \"lines.markersize\": 9.0,\n",
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" \"lines.linewidth\": 3\n",
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"})\n",
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"savefig = True\n",
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"anisotropy = pd.DataFrame({\"simple\": None, \"bodyCentered\": None, \"faceCentered\": None}, index = [\"A21\", \"A31\"])"
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]
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},
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2022-03-04 17:19:56 +05:00
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{
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2022-03-05 01:08:18 +05:00
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"cell_type": "markdown",
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2022-03-04 17:19:56 +05:00
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"metadata": {},
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"source": [
|
2022-03-05 01:08:18 +05:00
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"Porosity"
|
2022-03-04 17:19:56 +05:00
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]
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},
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{
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"cell_type": "code",
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2022-04-21 19:19:33 +05:00
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"execution_count": 57,
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2022-03-04 17:19:56 +05:00
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"metadata": {},
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"outputs": [
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{
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2022-03-05 01:08:18 +05:00
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"data": {
|
2022-04-21 19:19:33 +05:00
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"image/png": "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2022-03-05 01:08:18 +05:00
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"text/plain": [
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2022-04-21 19:19:33 +05:00
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"<Figure size 864x432 with 1 Axes>"
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2022-03-05 01:08:18 +05:00
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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2022-03-04 17:19:56 +05:00
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}
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],
|
|
|
|
|
"source": [
|
2022-03-05 01:08:18 +05:00
|
|
|
"df = load_data(execution, \"porosity\")\n",
|
|
|
|
|
"alpha = df[\"alpha\"].unique()\n",
|
|
|
|
|
"simple = df[df.label == \"simple\"][df.direction == \"[0.0, 0.0, 1.0]\"][\"porosity\"].to_numpy()\n",
|
|
|
|
|
"bodyCentered = df[df.label == \"bodyCentered\"][df.direction == \"[0.0, 0.0, 1.0]\"][\"porosity\"].to_numpy()\n",
|
|
|
|
|
"faceCentered = df[df.label == \"faceCentered\"][df.direction == \"[0.0, 0.0, 1.0]\"][\"porosity\"].to_numpy()\n",
|
|
|
|
|
"\n",
|
2022-04-21 19:19:33 +05:00
|
|
|
"fig, ax = plt.subplots(nrows = 1, ncols = 1, figsize = (12, 6))\n",
|
|
|
|
|
"\n",
|
2022-03-05 01:08:18 +05:00
|
|
|
"ax.plot(alpha, np.pad(simple, (0, alpha.size - simple.size), 'constant', constant_values = np.nan), \":\", label = \"КП\")\n",
|
|
|
|
|
"ax.plot(alpha, np.pad(bodyCentered, (0, alpha.size - bodyCentered.size), 'constant', constant_values = np.nan), \"--\", label = \"КОП\")\n",
|
|
|
|
|
"ax.plot(alpha, np.pad(faceCentered, (0, alpha.size - faceCentered.size), 'constant', constant_values = np.nan), \"-.\", label = \"КГЦ\")\n",
|
|
|
|
|
"plt.legend()\n",
|
|
|
|
|
"plt.grid(True)\n",
|
|
|
|
|
"plt.xlabel(r\"$\\alpha$\")\n",
|
|
|
|
|
"plt.ylabel(r\"$m$\")\n",
|
|
|
|
|
"plt.show()\n",
|
2022-03-04 17:19:56 +05:00
|
|
|
"\n",
|
2022-03-05 01:08:18 +05:00
|
|
|
"if savefig:\n",
|
2022-04-21 19:19:33 +05:00
|
|
|
" fig.tight_layout()\n",
|
|
|
|
|
" fig.savefig(\"porosity-rounded.tiff\")"
|
2022-03-04 17:19:56 +05:00
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
2022-03-05 01:08:18 +05:00
|
|
|
"cell_type": "markdown",
|
2022-03-04 17:19:56 +05:00
|
|
|
"metadata": {},
|
|
|
|
|
"source": [
|
2022-03-05 01:08:18 +05:00
|
|
|
"Simple structure"
|
2022-03-04 17:19:56 +05:00
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2022-04-21 19:19:33 +05:00
|
|
|
"execution_count": 69,
|
2022-03-04 17:19:56 +05:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
2022-04-21 19:19:33 +05:00
|
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAs8AAAGDCAYAAADUAP09AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAA5hUlEQVR4nO3de7xcVX3//9cnkBDgEIgNROQWBBXqBSjHCl5PLChiReFb7LcULBehVKNF+8WvClUUFGtb5ScqGqmgqf5SkCZVUCoXDyCgkEAQBBGBQAMakZDAgSTksr5/zJxwcphzztpzZs/tvJ6PxzwmZ8/ae6/ZaybznjVrrx0pJSRJkiSNbVKrKyBJkiR1CsOzJEmSlMnwLEmSJGUyPEuSJEmZDM+SJElSJsOzJEmSlGnLVlegiBkzZqRZs2bVfOzpp59m2223bW6FVCrbtPvYpt3HNu0+tmn3sU3rs3jx4j+klHYcvryjwvOsWbNYtGhRzcf6+/vp6+trboVUKtu0+9im3cc27T62afexTesTEQ/VWu6wDUmSJCmT4VmSJEnKZHiWJEmSMhmeJUmSpEyGZ0mSJCmT4VmSJEnKZHiWJEmSMhmeJUmSpEwddZEUSZK6Qe85V/GHgWdHLTOjZwqLzjy0STWSlMueZ0mSmmys4JxbRlLzGZ4lSZKkTIZnSZIkKZPhWZIkScqUdcJgRLwUOBZ4C7AXMBW4H7gUOC+l9PQY608GzgdeDewBbAc8CtwCfC6ldHu9T0CSJDWPJztqosudbeNE4P3A94HvAOuA2cA5wLsj4qCU0upR1p8C9AI3AvOAp4DdgROAn0fEYSmla+t7CpIktVbZgbKdAqsnO2qiyw3P3wPOTSmtGrLsaxFxH3AGcBLw5ZFWrvZM9w5fHhFfAx4G/g9geJYkdaSyA6WBtfna6QuL2ktWeE4pLRrhof+gEp5fUef+fw+sAabXub4kSRqmSPCrWfbKK2qWnUj8wqKRjPciKbtW75fnFI6ILagE5S2B3aj0OPcAPxxnPSRJVfaYtb8ZPVOy2qheRYKfIbExfN9NHHWH52oQ/kdgPfDdzNX2Be4c8vcq4NzqTZLUAIah9meA6j6+7yaO8fQ8nwccDHw8pXRv5joPAodSOYFwbyozeGwPbEUlhD9PRJwCnAIwc+ZM+vv7a254YGBgxMfUmWzT7mObtpdGtIVtWkzZx6ro9ouUL3Pbnaze4/LBa5/myTGy9LQp8KU3b1u4fM2yw4biDN22iqkrPEfE2cAcYG5KKbvXuHri4NVDtvNN4DbgMuCwEdaZC8wF6O3tTX19fTW33d/fz0iPqTPZpt3HNm2SYR+SI2lEW9imVWUf86LbL1K+zG13spKPy5MZ5Z98tr7yRbetYgqH54g4CzgTuAg4dTw7TykNRMR/Av83IvZKKd0/nu1JctydOls7vX7bqS4ThcdcnaBQeK4G508C3wLem1JKDajD1tX7F1C58IqkcWincXeexa+i2un12051aSdlnuzoMVcnyA7PEfEJKsF5HnBiSmnjCOV2pjKO+eGU0jPVZTsCjw9fJyJeCBwNDAC/rOsZSBqXMnt62umDsJ16tNqpLmqMsmfPKHv7Rfi61ESXe3nu9wOfonJBk6uBYyJiaJHlKaWrqv8+F/gbKlcg7K8u+2vgtIhYQOWkwWeBl1bLTafSi/3MuJ6J1EHGPQfrCGXr0ckBt0j5dnqe7VSXsk2ULwpl17/Tj09Zynx9tdMXFrWX3J7nV1fvd6cyZGO464CraiwfdAOVKwz+ObAzldk2llMJ4v9fSummzHpIXcE5WGsr+lwn0rFpF0WH4rRTG02UIA/Fgl/ZIbFTf93qhteBypF7hcHjgePrLZtSWkxlWjpJHWwihY92UvS4Oya1tk6ue1FF3oPDyzZ6BpV2Ou72VKsRxnuFQUkTSDt9CJatnb4oFD3uRerUTs9TKps91WoEw7NUg4FCE+WLwkR5nlK7KdpT3U5DcSY6w7NK08kB1EAhSSpT0c++dhqKM9EZnlWadgqgnRzki7LHQZ2snV6/7VSXicJjrk5geNaE0E5Bvmzt9AVgIn0QTqQvaGVqp/HatlXzeczVCQzP6ljtFFbaqS5FlRlw2+mnw7KDfJEvaBPpS0WZJtKXYkntw/CsttHJ88cWrUs7nfjRToG+zBNo2ul5tlNdyuYXBZXJ15dawfCsQjp1svt2M54TP7pZmSfQqDGKhpV2aiODVms089ct1aeTfz1tBcOzCplIAVfPZ/hojXY67u00FKcoP/hbo52Oezu9l9qJn+3FGJ4lZWunD8GytdOHbJnHvZ2ep1S2ifR/mMpjeJZqMFBoonzITpTnKUmNYnie4Moc59TJAdRAIUmSajE8d5miYbjMcU7tFEA7OchLkqT2YXiuoZPPOnXQf23t2FbqLn5Baz6PuaRWMDzX0G4BtJPDfJna6YOzneqi1pho77924DGX1AqG5xZop6EV7aST549tp7pIkqTyGJ4bYCKF4YlyKWdJkiYKfz0txvDcAJ0chouyh1WSpO7iZ3sxk1pdAUmSJKlTGJ4lSZKkTIbnCS5nDJPjnCRJkioc89xlOnnGCkmSpHZneK6hk886NQxLkiSVJys8R8RLgWOBtwB7AVOB+4FLgfNSSk+Psf504D3A24F9gRnAw8B1wNkppf+p9wmUod0CaCeHeUmSpG6S2/N8IvB+4PvAd4B1wGzgHODdEXFQSmn1KOu/BvhX4Brgy8AfgFcAf1td/7Uppbvrewqdx6EVkiRJnSk3PH8PODeltGrIsq9FxH3AGcBJVELxSH4FvCyldP/QhRFxBXAV8GngL7Jr3WYMw5IkSRNDVnhOKS0a4aH/oBKeXzHG+ktHWH51RKwYa/12ZxiWJEmaGMY7Vd2u1fvl9awcEdsD29W7viRJktRMkVKqb8WILYAbgFcDr0gp3VvHNj4PnA6clFL65ghlTgFOAZg5c+aB8+fPr7mtgYEBenp6ilZBbcw27T62afexTbuPbdp9bNP6zJ49e3FKqXf48vGE5/OBOcDHU0rn1rH+XwCXAP8NHJ4yKtLb25sWLao9gqS/v5++vr6i1VAbs027j23afWzT7mObdh/btD4RUTM81zVsIyLOphKc59YZnA+nMmvHYuAvc4KzJEmS1GqFw3NEnAWcCVwEnFrH+ocB/wn8EnhLSunJotuQJEmSWqFQeK4G508C3wLeW7THuBqcF1KZuu6QlNITRdaXJEmSWik7PEfEJ6gE53nAiSmljSOU2zki9omIbYYtfwuwALgX+LOU0or6qy1JkiQ1X+7lud8PfIrKJbWvBo6JiKFFlqeUrqr++1zgb6hcgbC/un4v8F9AUBnu8bZh65NS+vd6n4QkSZLUDLlXGHx19X53KkM2hruOypUCR/IKYGr1318coYzhWZIkSW0ta9hGSun4lFKMcuurUbZ/yLKLx1g/au1XkiRJaifjvcKgJEmSNGEYniVJkqRMhmdJkiQpk+FZkiRJymR4liRJkjIZniVJkqRMufM8S5IkSfSecxV/GHh21DIzeqaw6MxDm1Sj5rLnWZIkSdnGCs65ZTqV4VmSJEnKZHiWJEmSMhmeJUmSpEyGZ0mSJCmT4VmSJEnKZHiWJEmSMhmeJUmSpEyGZ0mSJGWb0TOlIWU6lVcYlCRJUrZuvXJgLnueJUmSpEyGZ0mSJCmT4VmSJEnKZHiWJEmSMhmeJUmSpExZ4TkiXhoRn46In0XEYxHxVEQsiYgzImLbzG28OyIuiog7ImJdRKSImDWu2kuSJElNlNvzfCLwIeB+4NPA6cC9wDnATRGxdcY23gf8b2B1dTuSJElSR8md5/l7wLkppVVDln0tIu4DzgBOAr48xjbeAzyaUlofEV8GXla4tpIkSeoYvedcxR8Gnh21zIyeKR01d3RWz3NKadGw4DzoP6r3r8jYxsMppfVFKidJkqTONVZwzi3TTsZ7wuCu1fvl462IJEmS1O7qDs8RsQXwj8B64LsNq5EkSZLUpnLHPNdyHnAw8PGU0r2Nqc7zRcQpwCkAM2fOpL+/v2a5gYGBER9TZ7JNu49t2n1s0+5jm3afTmj
|
2022-03-04 17:19:56 +05:00
|
|
|
"text/plain": [
|
2022-04-21 19:19:33 +05:00
|
|
|
"<Figure size 864x432 with 1 Axes>"
|
2022-03-04 17:19:56 +05:00
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {
|
|
|
|
|
"needs_background": "light"
|
|
|
|
|
},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
2022-03-05 01:08:18 +05:00
|
|
|
"df = load_data(execution, \"flowRate\")\n",
|
|
|
|
|
"simple = df[df.label == \"simple\"].groupby(df.direction)\n",
|
|
|
|
|
"alpha = simple.get_group(\"[0.0, 0.0, 1.0]\")[\"alpha\"].to_numpy()\n",
|
|
|
|
|
"\n",
|
2022-04-21 19:19:33 +05:00
|
|
|
"k1 = (permeability(\"simple\", \"[0.0, 0.0, 1.0]\"))\n",
|
|
|
|
|
"k2 = (permeability(\"simple\", \"[1.0, 0.0, 0.0]\"))\n",
|
|
|
|
|
"k3 = (permeability(\"simple\", \"[1.0, 1.0, 1.0]\"))\n",
|
2022-03-05 01:08:18 +05:00
|
|
|
"\n",
|
2022-04-21 19:19:33 +05:00
|
|
|
"#poly = np.polynomial.Polynomial.fit(alpha, anisotropy_21, 1)\n",
|
2022-03-05 01:08:18 +05:00
|
|
|
"\n",
|
2022-04-21 19:19:33 +05:00
|
|
|
"fig, ax = plt.subplots(nrows = 1, ncols = 1, figsize = (12, 6))\n",
|
|
|
|
|
"ax.plot(alpha, k2 / k1, \"s\", label = \"$k_2$ / $k_1$\")\n",
|
|
|
|
|
"ax.plot(alpha, k3 / k1, \"^\", label = \"$k_3$ / $k_1$\")\n",
|
2022-03-05 01:08:18 +05:00
|
|
|
"#ax.plot(alpha, poly(alpha), \"-\")\n",
|
2022-03-04 17:19:56 +05:00
|
|
|
"plt.legend()\n",
|
|
|
|
|
"plt.grid(True)\n",
|
2022-03-05 01:08:18 +05:00
|
|
|
"plt.xlabel(r\"$\\alpha$\")\n",
|
2022-04-21 19:19:33 +05:00
|
|
|
"#plt.ylabel(\"Анизотропия проницаемости\")\n",
|
|
|
|
|
"#plt.title(\"Простая кубическая\")\n",
|
2022-03-04 17:19:56 +05:00
|
|
|
"plt.show()\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"if savefig:\n",
|
2022-04-21 19:19:33 +05:00
|
|
|
" fig.tight_layout()\n",
|
|
|
|
|
" fig.savefig(\"anisotropy-simple.tiff\")"
|
2022-03-04 17:19:56 +05:00
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2022-04-21 19:19:33 +05:00
|
|
|
"execution_count": 70,
|
2022-03-05 01:08:18 +05:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
2022-04-21 19:19:33 +05:00
|
|
|
"source": [
|
|
|
|
|
"anisotropy[\"simple\"] = [(k2 / k1).mean(), (k3 / k1).mean()]"
|
|
|
|
|
]
|
2022-03-05 01:08:18 +05:00
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"source": [
|
|
|
|
|
"Body-centered structure"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2022-04-21 19:19:33 +05:00
|
|
|
"execution_count": 71,
|
2022-03-04 17:19:56 +05:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
2022-04-21 19:19:33 +05:00
|
|
|
"image/png": "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
|
2022-03-04 17:19:56 +05:00
|
|
|
"text/plain": [
|
2022-04-21 19:19:33 +05:00
|
|
|
"<Figure size 864x432 with 1 Axes>"
|
2022-03-04 17:19:56 +05:00
|
|
|
]
|
|
|
|
|
},
|
|
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"metadata": {
|
|
|
|
|
"needs_background": "light"
|
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|
|
|
},
|
|
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|
|
"output_type": "display_data"
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}
|
|
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|
|
],
|
|
|
|
|
"source": [
|
2022-03-05 01:08:18 +05:00
|
|
|
"df = load_data(execution, \"flowRate\")\n",
|
|
|
|
|
"bodyCentered = df[df.label == \"bodyCentered\"].groupby(df.direction)\n",
|
|
|
|
|
"alpha = bodyCentered.get_group(\"[0.0, 0.0, 1.0]\")[\"alpha\"].to_numpy()\n",
|
|
|
|
|
"\n",
|
2022-04-21 19:19:33 +05:00
|
|
|
"k1 = filter_group(permeability(\"bodyCentered\", \"[0.0, 0.0, 1.0]\"))\n",
|
|
|
|
|
"k2 = filter_group(permeability(\"bodyCentered\", \"[1.0, 0.0, 0.0]\"))\n",
|
|
|
|
|
"k3 = filter_group(permeability(\"bodyCentered\", \"[1.0, 1.0, 1.0]\"))\n",
|
2022-03-05 01:08:18 +05:00
|
|
|
"\n",
|
|
|
|
|
"#poly = np.polynomial.Polynomial.fit(alpha, anisotropy_21, 10)\n",
|
|
|
|
|
"\n",
|
2022-04-21 19:19:33 +05:00
|
|
|
"fig, ax = plt.subplots(nrows = 1, ncols = 1, figsize = (12, 6))\n",
|
|
|
|
|
"ax.plot(alpha, k2 / k1, \"s\", label = r\"$k_2$ / $k_1$\")\n",
|
|
|
|
|
"ax.plot(alpha, k3 / k1, \"^\", label = r\"$k_3$ / $k_1$\")\n",
|
|
|
|
|
"ax.axvline(0.13, linestyle = \"-.\", color = \"green\")\n",
|
2022-03-05 01:08:18 +05:00
|
|
|
"#ax.plot(alpha, poly(alpha), \"-\")\n",
|
2022-03-04 17:19:56 +05:00
|
|
|
"plt.legend()\n",
|
|
|
|
|
"plt.grid(True)\n",
|
2022-03-05 01:08:18 +05:00
|
|
|
"plt.xlabel(r\"$\\alpha$\")\n",
|
2022-04-21 19:19:33 +05:00
|
|
|
"#plt.ylabel(\"Анизотропия проницаемости\")\n",
|
|
|
|
|
"#plt.title(\"Кубическая объемноцентрированная\")\n",
|
2022-03-04 17:19:56 +05:00
|
|
|
"\n",
|
|
|
|
|
"if savefig:\n",
|
2022-04-21 19:19:33 +05:00
|
|
|
" fig.tight_layout()\n",
|
|
|
|
|
" fig.savefig(\"anisotropy-bodycentered.tiff\")"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 72,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": [
|
|
|
|
|
"anisotropy[\"bodyCentered\"] = [(k2 / k1).mean(), (k3 / k1).mean()]"
|
2022-03-04 17:19:56 +05:00
|
|
|
]
|
|
|
|
|
},
|
2022-03-05 01:08:18 +05:00
|
|
|
{
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"source": [
|
|
|
|
|
"Face-centered structure"
|
|
|
|
|
]
|
|
|
|
|
},
|
2022-03-04 17:19:56 +05:00
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2022-04-21 19:19:33 +05:00
|
|
|
"execution_count": 73,
|
2022-03-04 17:19:56 +05:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
2022-04-21 19:19:33 +05:00
|
|
|
"image/png": "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
|
2022-03-04 17:19:56 +05:00
|
|
|
"text/plain": [
|
2022-04-21 19:19:33 +05:00
|
|
|
"<Figure size 864x432 with 1 Axes>"
|
2022-03-04 17:19:56 +05:00
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {
|
|
|
|
|
"needs_background": "light"
|
|
|
|
|
},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
2022-03-05 01:08:18 +05:00
|
|
|
"df = load_data(execution, \"flowRate\")\n",
|
|
|
|
|
"faceCentered = df[df.label == \"faceCentered\"].groupby(df.direction)\n",
|
|
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|
|
"alpha = faceCentered.get_group(\"[0.0, 0.0, 1.0]\")[\"alpha\"].to_numpy()\n",
|
|
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|
|
"\n",
|
2022-04-21 19:19:33 +05:00
|
|
|
"k1 = filter_group(permeability(\"faceCentered\", \"[0.0, 0.0, 1.0]\"))\n",
|
|
|
|
|
"k2 = filter_group(permeability(\"faceCentered\", \"[1.0, 0.0, 0.0]\"))\n",
|
|
|
|
|
"k3 = filter_group(permeability(\"faceCentered\", \"[1.0, 1.0, 1.0]\"))\n",
|
2022-03-05 01:08:18 +05:00
|
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|
"\n",
|
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|
|
"#poly = np.polynomial.Polynomial.fit(alpha, anisotropy_21, 10)\n",
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|
"\n",
|
2022-04-21 19:19:33 +05:00
|
|
|
"fig, ax = plt.subplots(nrows = 1, ncols = 1, figsize = (12, 6))\n",
|
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|
|
"ax.plot(alpha, filter_group(k2 / k1, quantile = 0.97), \"s\", label = r\"$k_2$ / $k_1$\")\n",
|
|
|
|
|
"ax.plot(alpha, filter_group(k3 / k1, quantile = 0.8), \"^\", label = r\"$k_3$ / $k_1$\")\n",
|
2022-03-05 01:08:18 +05:00
|
|
|
"#ax.plot(alpha, poly(alpha), \"-\")\n",
|
2022-03-04 17:19:56 +05:00
|
|
|
"plt.legend()\n",
|
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|
|
"plt.grid(True)\n",
|
2022-03-05 01:08:18 +05:00
|
|
|
"plt.xlabel(r\"$\\alpha$\")\n",
|
2022-04-21 19:19:33 +05:00
|
|
|
"#plt.ylabel(\"Анизотропия проницаемости\")\n",
|
|
|
|
|
"#plt.title(\"Кубическая гранецентрированная\")\n",
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|
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|
|
"plt.show()\n",
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|
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|
|
"\n",
|
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|
|
"if savefig:\n",
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|
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|
|
" fig.tight_layout()\n",
|
|
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|
|
" fig.savefig(\"anisotropy-facecentered.tiff\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 74,
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"metadata": {},
|
|
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|
|
"outputs": [],
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|
|
|
|
"source": [
|
|
|
|
|
"anisotropy[\"faceCentered\"] = [(k2 / k1).mean(), (k3 / k1).mean()]"
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]
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|
},
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{
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"cell_type": "markdown",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"source": [
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|
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|
|
"Anisotropy mean values"
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|
]
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},
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{
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"cell_type": "code",
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"execution_count": 78,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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|
"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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|
" .dataframe tbody tr th {\n",
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|
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" vertical-align: top;\n",
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" }\n",
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"\n",
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|
" .dataframe thead th {\n",
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" text-align: right;\n",
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|
" }\n",
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|
|
"</style>\n",
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|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
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|
|
" <thead>\n",
|
|
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|
|
" <tr style=\"text-align: right;\">\n",
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|
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|
|
" <th></th>\n",
|
|
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|
|
" <th>simple</th>\n",
|
|
|
|
|
" <th>bodyCentered</th>\n",
|
|
|
|
|
" <th>faceCentered</th>\n",
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|
|
" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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|
|
" <tr>\n",
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|
|
|
|
" <th>A21</th>\n",
|
|
|
|
|
" <td>2.2591</td>\n",
|
|
|
|
|
" <td>0.7571</td>\n",
|
|
|
|
|
" <td>1.1312</td>\n",
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
" <th>A31</th>\n",
|
|
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|
|
" <td>1.5660</td>\n",
|
|
|
|
|
" <td>1.0036</td>\n",
|
|
|
|
|
" <td>0.9148</td>\n",
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
" </tbody>\n",
|
|
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|
|
"</table>\n",
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|
|
"</div>"
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|
|
],
|
|
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|
|
"text/plain": [
|
|
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|
|
" simple bodyCentered faceCentered\n",
|
|
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|
|
"A21 2.2591 0.7571 1.1312\n",
|
|
|
|
|
"A31 1.5660 1.0036 0.9148"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"execution_count": 78,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
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|
|
"anisotropy.round(4)"
|
|
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|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"source": [
|
|
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|
|
"Analytical porosity"
|
|
|
|
|
]
|
|
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|
|
},
|
|
|
|
|
{
|
|
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|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 5,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": [
|
|
|
|
|
"def porosity_a(a, label):\n",
|
|
|
|
|
" if label == \"simple\":\n",
|
|
|
|
|
" val = (1 - np.pi * (2 - 3 * a ** 2 * (3 - a)) / (12 * (1 - a) ** 3))\n",
|
|
|
|
|
"\n",
|
|
|
|
|
" elif label == \"bodyCentered\":\n",
|
|
|
|
|
" a13 = a[a <= 0.134]\n",
|
|
|
|
|
" a13g = a[a > 0.134]\n",
|
|
|
|
|
" a = a13\n",
|
|
|
|
|
" val = (1 - np.pi * np.sqrt(3) * (1 - 2 * a ** 2 * (3 - a)) / (8 * (1 - a) ** 3)\n",
|
|
|
|
|
" )\n",
|
|
|
|
|
" a = a13g\n",
|
|
|
|
|
" val2 = (1 - np.pi * np.sqrt(3) * (1 - 2 * a ** 2 * (3 - a)) / (8 * (1 - a) ** 3) +\n",
|
|
|
|
|
" np.pi * (np.sqrt(3) - 2 * (1 - a)) ** 2 * \n",
|
|
|
|
|
" (np.sqrt(3) - (1 - a)) / (8 * (1 - a)**3)\n",
|
|
|
|
|
" )\n",
|
|
|
|
|
" val = np.append(val, val2)\n",
|
|
|
|
|
"\n",
|
|
|
|
|
" elif label == \"faceCentered\":\n",
|
|
|
|
|
" val = (1 - np.pi * (1 - 3 * a ** 2 * (3 - a)) / (3 * np.sqrt(2) * (1 - a) ** 3))\n",
|
|
|
|
|
"\n",
|
|
|
|
|
" return val\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"def pad(x, y):\n",
|
|
|
|
|
" return np.pad(y, (0, x.size - y.size), 'constant', constant_values = np.nan)"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 6,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": [
|
|
|
|
|
"alpha = np.linspace(0, 0.3, 50)\n",
|
|
|
|
|
"porosity_simple = porosity_a(alpha[alpha <= 0.2929], \"simple\")\n",
|
|
|
|
|
"porosity_bodyCentered = porosity_a(alpha[alpha <= 0.1835], \"bodyCentered\")\n",
|
|
|
|
|
"porosity_faceCentered = porosity_a(alpha[alpha <= 0.134], \"faceCentered\")"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 7,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
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"image/png": "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
|
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|
|
"text/plain": [
|
|
|
|
|
"<Figure size 576x432 with 1 Axes>"
|
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|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {
|
|
|
|
|
"needs_background": "light"
|
|
|
|
|
},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"fig, ax = plt.subplots(nrows = 1, ncols = 1, figsize = (8, 6))\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"ax.plot(alpha, pad(alpha, porosity_simple), \":\", label = \"КП\")\n",
|
|
|
|
|
"ax.plot(alpha, pad(alpha, porosity_bodyCentered), \"--\", label = \"КОЦ\")\n",
|
|
|
|
|
"ax.plot(alpha, pad(alpha, porosity_faceCentered), \"-.\", label = \"КГЦ\")\n",
|
|
|
|
|
"plt.legend()\n",
|
|
|
|
|
"plt.grid(True)\n",
|
|
|
|
|
"plt.xlabel(r\"$\\alpha$\")\n",
|
|
|
|
|
"plt.ylabel(r\"$m$\")\n",
|
|
|
|
|
"plt.show()\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"if savefig:\n",
|
|
|
|
|
" fig.tight_layout()\n",
|
|
|
|
|
" fig.savefig(\"porosity-analytical.tiff\")"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
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|
"cell_type": "code",
|
|
|
|
|
"execution_count": 131,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": [
|
|
|
|
|
"df = load_data(execution, \"porosity\")\n",
|
|
|
|
|
"alpha = df[\"alpha\"].unique()\n",
|
|
|
|
|
"simple = df[df.label == \"simple\"][df.direction == \"[0.0, 0.0, 1.0]\"][\"porosity\"].to_numpy()\n",
|
|
|
|
|
"bodyCentered = df[df.label == \"bodyCentered\"][df.direction == \"[0.0, 0.0, 1.0]\"][\"porosity\"].to_numpy()\n",
|
|
|
|
|
"faceCentered = df[df.label == \"faceCentered\"][df.direction == \"[0.0, 0.0, 1.0]\"][\"porosity\"].to_numpy()\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"part_simple = pad(alpha, simple)[alpha <= 0.29] / porosity_a(alpha[alpha <= 0.29], \"simple\")\n",
|
|
|
|
|
"part_bodyCentered = pad(alpha, bodyCentered)[alpha <= 0.18] / porosity_a(alpha[alpha <= 0.18], \"bodyCentered\")\n",
|
|
|
|
|
"part_faceCentered = pad(alpha, faceCentered)[alpha <= 0.13] / porosity_a(alpha[alpha <= 0.13], \"faceCentered\")\n"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 145,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAABoAAAAQCAYAAAAI0W+oAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAABJ0AAASdAHeZh94AAAB00lEQVR4nL3UPWzNYRTH8U8bCUm9JRoaA+KlIiIiMTQIg8TgZTBZLAYGE2kXluNEBEkHkQ5MEkwWJiQkommMHQwGgyqLl6ANDUtTw30u1/W/tyTit5w85/md55vz/5/n6ZiZmfE/NKdxkZkXsRW96MZXjOMOhiLiQ5P/JVa2OPttRPRUgnASo3iAd+hCH87gWGb2RcTrpppJXKoAfWnZERZGxLfmisw8h9M4heNN2xMRcaYC9Is6GxdVkKJbJa6b7cBWau6olQ6U+LRib25mHsYKTBXPcERMzwrKzAHMxyK14dhRDrhQYe/BjabcWGYeiYjH9USnag0gcKJA7mNPRLxv8l3D7gLrwiZcxSrcy8zNdWNHu3uUmcuwrXSyAPsjYrRlwc+6QfTjTkQcbNcRiIi3EXEbe7AE12eDFF0pcWc90RbUABzHM2zMzO4/KKl/4q6/AhUtL3G6raumvhJf1BM/pi4ze9WejcnGiszsxFksxZOI+FTyG/AqIqaa/KswVJY3fwNhL85n5gjG8AHLsAur8QZHG/yH0J+Zw2rv4WeswT7Mw10MVoEeYq3aOG/BYrUL+FztnlyOiI8N/kdYX7zb1f7HBEaK/0ZE/BjptuP9L/UdP8eNfEo28QwAAAAASUVORK5CYII=",
|
|
|
|
|
"text/latex": [
|
|
|
|
|
"$\\displaystyle 35$"
|
|
|
|
|
],
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"35"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"execution_count": 145,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"porosity_a(alpha[alpha <= 0.18], \"bodyCentered\").size"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 151,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": "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
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 576x432 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {
|
|
|
|
|
"needs_background": "light"
|
|
|
|
|
},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"plt.rcParams.update({\n",
|
|
|
|
|
" \"lines.markersize\": 6.0\n",
|
|
|
|
|
"})\n",
|
|
|
|
|
"fig, ax = plt.subplots(nrows = 1, ncols = 1, figsize = (8, 6))\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"ax.plot(alpha, pad(alpha, part_simple), \"o\", label = \"КП\")\n",
|
|
|
|
|
"ax.plot(alpha, pad(alpha, part_bodyCentered), \"s\", label = \"КОП\")\n",
|
|
|
|
|
"ax.plot(alpha, pad(alpha, part_faceCentered), \"^\", label = \"КГЦ\")\n",
|
|
|
|
|
"plt.legend()\n",
|
|
|
|
|
"plt.grid(True)\n",
|
|
|
|
|
"plt.xlabel(r\"$\\alpha$\")\n",
|
|
|
|
|
"#plt.ylabel(r\"\")\n",
|
2022-03-05 01:08:18 +05:00
|
|
|
"plt.show()\n",
|
2022-03-04 17:19:56 +05:00
|
|
|
"\n",
|
|
|
|
|
"if savefig:\n",
|
2022-04-21 19:19:33 +05:00
|
|
|
" fig.tight_layout()\n",
|
|
|
|
|
" fig.savefig(\"porosity-proportion.tiff\")"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 152,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": "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",
|
|
|
|
|
"text/latex": [
|
|
|
|
|
"$\\displaystyle 0.914869058316947$"
|
|
|
|
|
],
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"0.9148690583169465"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"execution_count": 152,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"part_simple.mean()"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 154,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": "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",
|
|
|
|
|
"text/latex": [
|
|
|
|
|
"$\\displaystyle 0.866128471396414$"
|
|
|
|
|
],
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"0.8661284713964136"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"execution_count": 154,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"part_bodyCentered[~np.isnan(part_bodyCentered)].mean()"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 155,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": "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",
|
|
|
|
|
"text/latex": [
|
|
|
|
|
"$\\displaystyle 0.90543798706737$"
|
|
|
|
|
],
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"0.90543798706737"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"execution_count": 155,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"part_faceCentered[~np.isnan(part_faceCentered)].mean()"
|
2022-03-04 17:19:56 +05:00
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
|
|
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