{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "anaconda-cloud": {
      "attach-environment": true,
      "summary": "Magnetno polje v osi tuljave",
      "thumbnail": 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"
    },
    "environment": {
      "channels": [
        "defaults"
      ],
      "dependencies": [
        "_license=1.1",
        "_nb_ext_conf=0.3.0",
        "alabaster=0.7.9",
        "anaconda-clean=1.0.0",
        "anaconda-client=1.5.1",
        "anaconda-navigator=1.3.1",
        "anaconda=4.2.0",
        "argcomplete=1.0.0",
        "astroid=1.4.7",
        "astropy=1.2.1",
        "babel=2.3.4",
        "backports=1.0",
        "beautifulsoup4=4.5.1",
        "bitarray=0.8.1",
        "blaze=0.10.1",
        "bokeh=0.12.2",
        "boto=2.42.0",
        "bottleneck=1.1.0",
        "bzip2=1.0.6",
        "cffi=1.7.0",
        "chest=0.2.3",
        "click=6.6",
        "cloudpickle=0.2.1",
        "clyent=1.2.2",
        "colorama=0.3.7",
        "comtypes=1.1.2",
        "conda-build=2.0.2",
        "conda-env=2.6.0",
        "conda=4.2.13",
        "configobj=5.0.6",
        "console_shortcut=0.1.1",
        "contextlib2=0.5.3",
        "cryptography=1.5",
        "curl=7.49.0",
        "cycler=0.10.0",
        "cython=0.24.1",
        "cytoolz=0.8.0",
        "dask=0.11.0",
        "datashape=0.5.2",
        "decorator=4.0.10",
        "dill=0.2.5",
        "docutils=0.12",
        "dynd-python=0.7.2",
        "entrypoints=0.2.2",
        "et_xmlfile=1.0.1",
        "fastcache=1.0.2",
        "filelock=2.0.6",
        "flask-cors=2.1.2",
        "flask=0.11.1",
        "freetype=2.5.5",
        "get_terminal_size=1.0.0",
        "gevent=1.1.2",
        "glueviz=0.9.1",
        "greenlet=0.4.10",
        "h5py=2.6.0",
        "hdf5=1.8.15.1",
        "heapdict=1.0.0",
        "icu=57.1",
        "idna=2.1",
        "imagesize=0.7.1",
        "ipykernel=4.5.0",
        "ipython=5.1.0",
        "ipython_genutils=0.1.0",
        "ipywidgets=5.2.2",
        "itsdangerous=0.24",
        "jdcal=1.2",
        "jedi=0.9.0",
        "jinja2=2.8",
        "jpeg=8d",
        "jsonschema=2.5.1",
        "jupyter=1.0.0",
        "jupyter_client=4.4.0",
        "jupyter_console=5.0.0",
        "jupyter_core=4.2.0",
        "lazy-object-proxy=1.2.1",
        "libdynd=0.7.2",
        "libpng=1.6.22",
        "libtiff=4.0.6",
        "llvmlite=0.13.0",
        "locket=0.2.0",
        "lxml=3.6.4",
        "markupsafe=0.23",
        "matplotlib=1.5.3",
        "menuinst=1.4.1",
        "mistune=0.7.3",
        "mkl-service=1.1.2",
        "mkl=11.3.3",
        "mpmath=0.19",
        "multipledispatch=0.4.8",
        "nb_anacondacloud=1.2.0",
        "nb_conda=2.0.0",
        "nb_conda_kernels=2.0.0",
        "nbconvert=4.2.0",
        "nbformat=4.1.0",
        "nbpresent=3.0.2",
        "networkx=1.11",
        "nltk=3.2.1",
        "nose=1.3.7",
        "notebook=4.2.3",
        "numba=0.28.1",
        "numexpr=2.6.1",
        "numpy=1.11.1",
        "odo=0.5.0",
        "openpyxl=2.3.2",
        "openssl=1.0.2j",
        "pandas=0.18.1",
        "partd=0.3.6",
        "path.py=8.2.1",
        "pathlib2=2.1.0",
        "patsy=0.4.1",
        "pep8=1.7.0",
        "pickleshare=0.7.4",
        "pillow=3.3.1",
        "pip=8.1.2",
        "pkginfo=1.3.2",
        "ply=3.9",
        "prompt_toolkit=1.0.3",
        "psutil=4.3.1",
        "py=1.4.31",
        "pyasn1=0.1.9",
        "pycosat=0.6.1",
        "pycparser=2.14",
        "pycrypto=2.6.1",
        "pycurl=7.43.0",
        "pyflakes=1.3.0",
        "pygments=2.1.3",
        "pylint=1.5.4",
        "pyopenssl=16.0.0",
        "pyparsing=2.1.4",
        "pyqt=5.6.0",
        "pytables=3.2.2",
        "pytest=2.9.2",
        "python-dateutil=2.5.3",
        "python=3.5.2",
        "pytz=2016.6.1",
        "pywin32=220",
        "pyyaml=3.12",
        "pyzmq=15.4.0",
        "qt=5.6.0",
        "qtawesome=0.3.3",
        "qtconsole=4.2.1",
        "qtpy=1.1.2",
        "requests=2.11.1",
        "rope=0.9.4",
        "ruamel_yaml=0.11.14",
        "scikit-image=0.12.3",
        "scikit-learn=0.17.1",
        "scipy=0.18.1",
        "setuptools=27.2.0",
        "simplegeneric=0.8.1",
        "singledispatch=3.4.0.3",
        "sip=4.18",
        "six=1.10.0",
        "snowballstemmer=1.2.1",
        "sockjs-tornado=1.0.3",
        "sphinx=1.4.6",
        "spyder=3.0.0",
        "sqlalchemy=1.0.13",
        "statsmodels=0.6.1",
        "sympy=1.0",
        "tk=8.5.18",
        "toolz=0.8.0",
        "tornado=4.4.1",
        "traitlets=4.3.0",
        "unicodecsv=0.14.1",
        "vs2015_runtime=14.0.25123",
        "wcwidth=0.1.7",
        "werkzeug=0.11.11",
        "wheel=0.29.0",
        "widgetsnbextension=1.2.6",
        "win_unicode_console=0.5",
        "wrapt=1.10.6",
        "xlrd=1.0.0",
        "xlsxwriter=0.9.3",
        "xlwings=0.10.0",
        "xlwt=1.1.2",
        "zlib=1.2.8",
        {
          "pip": [
            "backports.shutil-get-terminal-size",
            "dynd",
            "rope-py3k",
            "ruamel-yaml-",
            "tables"
          ]
        }
      ],
      "name": "notebook-magnetno-polje-v-osi-tuljave-ipynb"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.7.3"
    },
    "colab": {
      "name": "Copy of Copy of Magnetno-polje-v-osi-tuljave_induktivnost.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/osnove/Dodatno/blob/master/Magnetno_polje_v_osi_tuljave_induktivnost.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "CdlZUKx1U5ma",
        "colab_type": "text"
      },
      "source": [
        "# Izračun polja v osi tuljave\n",
        "\n",
        "R je polmer tuljave\n",
        "NI je N*I\n",
        "l je dolžina tuljave (z2-z1)\n",
        "\n",
        "Formula za izračun polja v osi tuljave je:\n",
        "\n",
        "$$ {B_z} =\\frac{{{\\mu _0}NI}}{{2l}}\\left( {\\frac{{{z_2} - z}}{{\\sqrt {{{\\left( {z - {z_2}} \\right)}^2} + {R^2}} }} - \\frac{{({z_1} - z)}}{{\\sqrt {{{\\left( {z - {z_1}} \\right)}^2} + {R^2}} }}} \\right)$$\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JC_wFK1XU5mc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from IPython.display import Image\n",
        "from IPython.core.display import HTML "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MSp5PUXJU5mf",
        "colab_type": "code",
        "outputId": "434bc176-016b-4c02-bda7-164c495d6fba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        }
      },
      "source": [
        "Image(url= \"https://raw.githubusercontent.com/osnove/Slike/master/oe2_solenoid.png\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<img src=\"https://raw.githubusercontent.com/osnove/Slike/master/oe2_solenoid.png\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tG3dPtmr-zOw",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0lmnMLTjU5mi",
        "colab_type": "code",
        "outputId": "701fc6f5-a252-49a2-d378-5fc7ef998522",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 642
        }
      },
      "source": [
        "import numpy as np\n",
        "import math\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib\n",
        "\n",
        "NI=6000   # tok\n",
        "R=3e-2     # polmer\n",
        "mi0=4*np.pi*1e-7  # permeabilnost\n",
        "z1=-5e-2\n",
        "z2=5e-2\n",
        "l=z2-z1    # dolžina\n",
        "\n",
        "z=np.arange(-15,15,0.11)*1e-2\n",
        "\n",
        "B=mi0*NI/(2*l)*((z2-z)/np.sqrt((z-z2)**2+R**2)-(z1-z)/np.sqrt((z-z1)**2+R**2))\n",
        "plt.figure(figsize=(15,10))\n",
        "plt.title('Polje v osi tuljave',fontsize=18)\n",
        "plt.xlabel('Razdalja / cm',fontsize=18)\n",
        "plt.ylabel('Gostota magnetnega pretoka / mT',fontsize=18)\n",
        "plt.tick_params(labelsize=18)\n",
        "\n",
        "plt.plot(z*1e2,B*1e3)\n",
        "plt.grid()\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9SRE3PGqroiQ1JgtFSdJ+vbZmC/e9vIIPnHgInUpLso6jNqxX51IuOmYgf3xm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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ADs7wmsU5ml",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "\t"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ahQHtEZvBBxM",
        "colab_type": "text"
      },
      "source": [
        "## Izračun induktivnosti \n",
        "po http://info.ee.surrey.ac.uk/Workshop/advice/coils/air_coils.html\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AlsGFnN3U5mn",
        "colab_type": "code",
        "outputId": "ace254af-d054-4d6d-84cf-e80a0e5b9a89",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        }
      },
      "source": [
        "Image(url= \"https://raw.githubusercontent.com/osnove/Slike/master/mult_layer_air_coil.png\")\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<img src=\"https://raw.githubusercontent.com/osnove/Slike/master/mult_layer_air_coil.png\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lEHZIRz8Bxpw",
        "colab_type": "code",
        "outputId": "5ce10c7c-618d-4fa2-d2e3-1c19e77eff00",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        }
      },
      "source": [
        "Image(url= \"https://raw.githubusercontent.com/osnove/Slike/master/Brooks_ratio.png\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<img src=\"https://raw.githubusercontent.com/osnove/Slike/master/Brooks_ratio.png\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VIqp_1caIVmi",
        "colab_type": "text"
      },
      "source": [
        "## Izračun za Brooksovo tuljavo z maximalnim L za c=b"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1aqoxe8lU5mp",
        "colab_type": "code",
        "outputId": "a6962c97-c8da-426f-fa13-dfbf3b3b3b6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        }
      },
      "source": [
        "d= 0.05  # premer tuljave\n",
        "c=0.07  # debelina (višina) ovojev\n",
        "b=c   # dolžina tuljave\n",
        "d_zice=1.8e-3\n",
        "I=10 # Tok v žici\n",
        "\n",
        "\n",
        "#nl=50 # Number of layers (debelina žice * nl = c)\n",
        "nl=round(c/d_zice)\n",
        "nw=nl # število ovojev vzdolž tuljave\n",
        "N=nl*nw\n",
        "print('Število plasti (layerjev) = ', nl)\n",
        "print('Število ovojev) = ', N)\n",
        "### korekcijski faktor zaradi okrogle žice\n",
        "\n",
        "#kf=(np.pi*(d_zice/2)**2)/d_zice**2=(1-np.pi/4)\n",
        "#kf=np.pi/4\n",
        "kf=1\n",
        "NI=N*I # A-ovoji\n",
        "Il=kf*NI/nl  # tok v enem layerju\n",
        "\n",
        "#N=1200    # število ovojev\n",
        "\n",
        "\n",
        "# Dolžina žice\n",
        "l_zice=0\n",
        "\n",
        "a=(d+c)/2 # srednji polmer v m\n",
        "S1 = (c/(2*a))**2 \n",
        "\n",
        "\n",
        "L = 4E-7*np.pi*a*N**2*((0.5+S1/12)*np.log(8/S1) - 0.84834+0.2041*S1)\n",
        "print('Induktivnost = ',L*1e3, ' mH')\n",
        "\n",
        "Ball=0\n",
        "z1=-b/2\n",
        "z2=b/2\n",
        "\n",
        "def B2(r,z):\n",
        "\t# This function returns B for specific radius and position z \n",
        "  B2=mi0*Il/(2*l)*((z2-z)/np.sqrt((z-z2)**2+r**2)-(z1-z)/np.sqrt((z-z1)**2+r**2))\n",
        "  return B2\n",
        " \n",
        "for i in range(1,nl):\n",
        "  r=(d/2)+i/nl*c\n",
        "  l_zice=nw*(2*np.pi*r)+l_zice\n",
        "  Bl=B2(r,0)\n",
        "  Ball=Ball+Bl \n",
        "  #print(Bl)\n",
        "\n",
        "print(\"Polje v sredini je \",Ball*1e3, \"mT\")\n",
        "print(\"dolžina žice = \",l_zice, \"m\")\n",
        "\n",
        "# Upornost žice\n",
        "ro_Cu=1.72e-8  # Ohm*m\n",
        "R_Cu=ro_Cu*l_zice/(np.pi*(d_zice/2)**2)\n",
        "print(\"Upornost žice = \",R_Cu, \"Ohm\")\n",
        "\n",
        "# Teža\n",
        "gostota_Cu=8.79e3 #kg/m3\n",
        "#masa=gostota_Cu*l_zice*(np.pi*(d_zice/2)**2)\n",
        "masa=gostota_Cu*np.pi*((d/2+c)**2-(d/2)**2)*c\n",
        "print(\"Masa žice = \",masa, \"kg\")\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Število plasti (layerjev) =  39\n",
            "Število ovojev) =  1521\n",
            "Induktivnost =  155.130776162855  mH\n",
            "Polje v sredini je  98.43445235838281 mT\n",
            "dolžina žice =  558.7008375144089 m\n",
            "Upornost žice =  3.7763555555555564 Ohm\n",
            "Masa žice =  16.23738446193192 kg\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YZQ-4wvpIthv",
        "colab_type": "text"
      },
      "source": [
        "## Izračun polja s sumacijo po višini"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-dchQoWcDnsa",
        "colab_type": "code",
        "outputId": "86be9fac-f19c-4399-bed2-f867a07e6b0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "d= 0.05  # premer tuljave\n",
        "c=0.04  # debelina (višina) ovojev\n",
        "b=0.07   # dolžina tuljave\n",
        "d_zice=1.88e-3\n",
        "I=10 # Tok v žici\n",
        "\n",
        "def B2(r,z):\n",
        "\t# This function returns B for specific radius and position z \n",
        "  B2=mi0*Il/(2*l)*((z2-z)/np.sqrt((z-z2)**2+r**2)-(z1-z)/np.sqrt((z-z1)**2+r**2))\n",
        "  return B2\n",
        " \n",
        "Bb=[]\n",
        "LL=[]\n",
        "for b1 in range(1,11):\n",
        "  b=b1*1e-2\n",
        "  print(b)\n",
        "  z1=-b/2\n",
        "  z2=b/2\n",
        "  #nl=50 # Number of layers (debelina žice * nl = c)\n",
        "  nl=round(c/d_zice)\n",
        "  nw=round(b/d_zice) # število ovojev vzdolž tuljave\n",
        "  N=nl*nw  # število vseh ovojev\n",
        "  print('Število plasti (layerjev) = ', nl)\n",
        "  print('Število ovojev) = ', N)\n",
        "  ### korekcijski faktor zaradi okrogle žice\n",
        "  #kf=(np.pi*(d_zice/2)**2)/d_zice**2=(1-np.pi/4)\n",
        "  #kf=np.pi/4\n",
        "  kf=1\n",
        "  NI=N*I # A-ovoji\n",
        "  Il=kf*NI/nl  # tok v enem layerju\n",
        "  Il=I*(c*b)/d_zice**2/nl  # korigiran tok zaradi zaokroževanja \n",
        "  # Dolžina žice\n",
        "  l_zice=0\n",
        "\n",
        "  Ball=0\n",
        "  for i in range(0,nl):\n",
        "    r=(d/2)+i/(nl-1)*c\n",
        "    l_zice=nw*(2*np.pi*r)+l_zice\n",
        "    Bl=B2(r,0)\n",
        "    Ball=Ball+Bl \n",
        "    #print(Bl)\n",
        "\n",
        "  Bb=np.concatenate((Bb,[Ball*1e3]))\n",
        "  print(Bb)\n",
        "\n",
        "  # Izračun induktivnosti po Brooksu\n",
        "  a=(d+c)/2 # srednji polmer v m\n",
        "  S1 = (c/(2*a))**2 \n",
        "  L = 4E-7*np.pi*a*N**2*((0.5+S1/12)*np.log(8/S1) - 0.84834+0.2041*S1)\n",
        "  print('Induktivnost = ',L*1e3, ' mH')\n",
        "  \n",
        "  # Whellerjeva formula za poljubno dolžino, spremeni inče 1 in = 2.54 cm\n",
        "  L=0.8*(a)**2*N**2*4E-7/(6*a+9*b+10*c)/2.54e-2  # v uH\n",
        "  print('Induktivnost po Wheeler= ',L*1e3, ' mH')\n",
        "  LL=np.concatenate((LL,[L*1e3]))\n",
        "\n",
        "\n",
        "  print(\"Polje v sredini je \",Ball*1e3, \"mT\")\n",
        "  print(\"dolžina žice = \",l_zice, \"m\")\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "  # Upornost žice\n",
        "  ro_Cu=1.72e-8  # Ohm*m\n",
        "  R_Cu=ro_Cu*l_zice/(np.pi*(d_zice/2)**2)\n",
        "  print(\"Upornost žice = \",R_Cu, \"Ohm\")\n",
        "\n",
        "  # Teža\n",
        "  gostota_Cu=8.79e3 #kg/m3\n",
        "  masa_okrogle=gostota_Cu*l_zice*(np.pi*(d_zice/2)**2)\n",
        "  masa_polne=gostota_Cu*np.pi*((d/2+c)**2-(d/2)**2)*b\n",
        "  print(\"Masa okrogle žice = \",masa_okrogle, \"kg\")\n",
        "  print(\"Masa polne žice = \",masa_polne, \"kg\")\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.01\n",
            "Število plasti (layerjev) =  21\n",
            "Število ovojev) =  105\n",
            "[1.69666105]\n",
            "Induktivnost =  0.6880095927556684  mH\n",
            "Induktivnost po Wheeler=  0.3700891007045171  mH\n",
            "Polje v sredini je  1.6966610537408773 mT\n",
            "dolžina žice =  29.68805057642355 m\n",
            "Upornost žice =  0.18395201448619286 Ohm\n",
            "Masa okrogle žice =  0.7243961545010088 kg\n",
            "Masa polne žice =  0.9941255793019543 kg\n",
            "0.02\n",
            "Število plasti (layerjev) =  21\n",
            "Število ovojev) =  231\n",
            "[1.69666105 6.61163328]\n",
            "Induktivnost =  3.3299664289374347  mH\n",
            "Induktivnost po Wheeler=  1.601571468272348  mH\n",
            "Polje v sredini je  6.611633275862268 mT\n",
            "dolžina žice =  65.31371126813181 m\n",
            "Upornost žice =  0.40469443186962434 Ohm\n",
            "Masa okrogle žice =  1.5936715399022194 kg\n",
            "Masa polne žice =  1.9882511586039087 kg\n",
            "0.03\n",
            "Število plasti (layerjev) =  21\n",
            "Število ovojev) =  336\n",
            "[ 1.69666105  6.61163328 14.29742879]\n",
            "Induktivnost =  7.045218229818044  mH\n",
            "Induktivnost po Wheeler=  3.0640227843859935  mH\n",
            "Polje v sredini je  14.297428786373418 mT\n",
            "dolžina žice =  95.00176184455536 m\n",
            "Upornost žice =  0.5886464463558172 Ohm\n",
            "Masa okrogle žice =  2.3180676944032284 kg\n",
            "Masa polne žice =  2.982376737905863 kg\n",
            "0.04\n",
            "Število plasti (layerjev) =  21\n",
            "Število ovojev) =  441\n",
            "[ 1.69666105  6.61163328 14.29742879 24.19608694]\n",
            "Induktivnost =  12.13648921620999  mH\n",
            "Induktivnost po Wheeler=  4.817050989985474  mH\n",
            "Polje v sredini je  24.196086938639358 mT\n",
            "dolžina žice =  124.68981242097888 m\n",
            "Upornost žice =  0.7725984608420099 Ohm\n",
            "Masa okrogle žice =  3.0424638489042364 kg\n",
            "Masa polne žice =  3.9765023172078173 kg\n",
            "0.05\n",
            "Število plasti (layerjev) =  21\n",
            "Število ovojev) =  567\n",
            "[ 1.69666105  6.61163328 14.29742879 24.19608694 35.77191688]\n",
            "Induktivnost =  20.06235972475529  mH\n",
            "Induktivnost po Wheeler=  7.323005905511811  mH\n",
            "Polje v sredini je  35.77191688297134 mT\n",
            "dolžina žice =  160.3154731126872 m\n",
            "Upornost žice =  0.9933408782254416 Ohm\n",
            "Masa okrogle žice =  3.9117392343054482 kg\n",
            "Masa polne žice =  4.970627896509772 kg\n",
            "0.06\n",
            "Število plasti (layerjev) =  21\n",
            "Število ovojev) =  672\n",
            "[ 1.69666105  6.61163328 14.29742879 24.19608694 35.77191688 48.57454541]\n",
            "Induktivnost =  28.180872919272176  mH\n",
            "Induktivnost po Wheeler=  9.521260883711848  mH\n",
            "Polje v sredini je  48.57454541449561 mT\n",
            "dolžina žice =  190.00352368911072 m\n",
            "Upornost žice =  1.1772928927116344 Ohm\n",
            "Masa okrogle žice =  4.636135388806457 kg\n",
            "Masa polne žice =  5.964753475811726 kg\n",
            "0.07\n",
            "Število plasti (layerjev) =  21\n",
            "Število ovojev) =  777\n",
            "[ 1.69666105  6.61163328 14.29742879 24.19608694 35.77191688 48.57454541\n",
            " 62.25144615]\n",
            "Induktivnost =  37.675405299300394  mH\n",
            "Induktivnost po Wheeler=  11.84786165960024  mH\n",
            "Polje v sredini je  62.251446149503046 mT\n",
            "dolžina žice =  219.69157426553426 m\n",
            "Upornost žice =  1.3612449071978272 Ohm\n",
            "Masa okrogle žice =  5.360531543307465 kg\n",
            "Masa polne žice =  6.958879055113681 kg\n",
            "0.08\n",
            "Število plasti (layerjev) =  21\n",
            "Število ovojev) =  903\n",
            "[ 1.69666105  6.61163328 14.29742879 24.19608694 35.77191688 48.57454541\n",
            " 62.25144615 76.53745053]\n",
            "Induktivnost =  50.88518948020922  mH\n",
            "Induktivnost po Wheeler=  14.9658707301875  mH\n",
            "Polje v sredini je  76.53745052774228 mT\n",
            "dolžina žice =  255.3172349572425 m\n",
            "Upornost žice =  1.5819873245812586 Ohm\n",
            "Masa okrogle žice =  6.229806928708675 kg\n",
            "Masa polne žice =  7.953004634415635 kg\n",
            "0.09\n",
            "Število plasti (layerjev) =  21\n",
            "Število ovojev) =  1008\n",
            "[ 1.69666105  6.61163328 14.29742879 24.19608694 35.77191688 48.57454541\n",
            " 62.25144615 76.53745053 91.23774923]\n",
            "Induktivnost =  63.4069640683624  mH\n",
            "Induktivnost po Wheeler=  17.514616726963183  mH\n",
            "Polje v sredini je  91.23774922650195 mT\n",
            "dolžina žice =  285.005285533666 m\n",
            "Upornost žice =  1.7659393390674507 Ohm\n",
            "Masa okrogle žice =  6.954203083209682 kg\n",
            "Masa polne žice =  8.947130213717589 kg\n",
            "0.1\n",
            "Število plasti (layerjev) =  21\n",
            "Število ovojev) =  1113\n",
            "[  1.69666105   6.61163328  14.29742879  24.19608694  35.77191688\n",
            "  48.57454541  62.25144615  76.53745053  91.23774923 106.21141983]\n",
            "Induktivnost =  77.30475784202687  mH\n",
            "Induktivnost po Wheeler=  20.129452630523094  mH\n",
            "Polje v sredini je  106.21141982676396 mT\n",
            "dolžina žice =  314.6933361100896 m\n",
            "Upornost žice =  1.9498913535536442 Ohm\n",
            "Masa okrogle žice =  7.678599237710693 kg\n",
            "Masa polne žice =  9.941255793019543 kg\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1cJQ7Ydh6NHe",
        "colab_type": "code",
        "outputId": "e8419558-dbba-4bab-8ef6-8ec4b1760282",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "b=np.linspace(1,10,10)*1e-2\n",
        "fig, ax1 = plt.subplots()\n",
        "\n",
        "color = 'tab:red'\n",
        "ax1.set_xlabel('Dolžina tuljave')\n",
        "ax1.set_ylabel('Polje / mT', color=color)\n",
        "ax1.plot(b, Bb, color=color)\n",
        "ax1.tick_params(axis='y', labelcolor=color)\n",
        "\n",
        "ax2 = ax1.twinx()  # x os za drugi plot naj bo enaka x osi prvega plota\n",
        "\n",
        "color = 'tab:blue'\n",
        "ax2.set_ylabel('Induktivnost  / mH', color=color)  \n",
        "ax2.plot(b, LL, color=color)\n",
        "ax2.tick_params(axis='y', labelcolor=color)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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o4G4CmVQRyLAfVoYaiVILUVQ1vp2ZDJq2kqTkfVx6clNe7nk8hSMMMr/+mprn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\nMCQBBsCQPpx3nYSxrw4gAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qvzbo-HD6tWo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}