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        "id": "view-in-github",
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        "<a href=\"https://colab.research.google.com/github/osnove/Dodatno/blob/master/oe1_Q_i.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "\n",
        "# Uporaba Jupyer online zvezka za izračune in izrise grafov\n",
        "(primer izrisa Toka in Naboja kot funkcije časa)\n",
        "\n",
        "<p align=\"justify\">\n",
        "   Dejan Križaj, 2019 \n",
        "</p>\n",
        "\n",
        "\n",
        "<br>\n",
        "\n",
        "**Namen:** Z izvajanjem in študijem primerov v tem zvezku se boste naučili uporabljati Jupyter zvezek za izračune funkcij in različne načine njihove grafične predstavitve.  \n",
        "\n",
        "\n",
        "<br>\n",
        "<div class=\"alert alert-block alert-info\">\n",
        "<b>Namig:</b> Obstajata dve verziji tega dokumenta. Ena je v obliki html datoteke (končnica html), ki je ni mogoče izvajati, druga pa ima končnico ipny (Jupyter Notebook), ki jo lahko izvajamo z Jupyter aplikacijo. To aplikacijo imate lahko naloženo na vašem računalniku in se izvaja v brskalniku, lahko jo ogledujete s spletno aplikacijo nbViewer, s spletnimi aplikacijami Binder ali Google Colab pa jo lahko tudi zaganjate in spreminjate. Več o tem si preberite v \n",
        "<a href=\"http://lbm.fe.uni-lj.si/index.php?option=com_content&view=article&id=59&Itemid=135&lang=si\">tem članku</a>.\n",
        "<br>    \n",
        "Za izvajanje tega zvezka ne potrebujete posebnega znanja programiranja v Pythonu, lahko pa poljubno spreminjate kodo in se sproti učite tudi uporabe programskega jezika. Več podobnih primerov je na <a href=\"https://github.com/osnove/Dodatno/\"> Githubu </a>  \n",
        "</div>\n",
        "\n",
        "\n",
        "<br>\n",
        "<b> Način izvajanja:</b> S klikom na tekst se ga lahko editira, s Ctrl Enter se zažene eno celico, z Runtime/Run all pa se osveži celoten zvezek (notebook). Ker se pogosto izvajanje naslednje celice navezuje na rešitev prejšnje, jih je potrebno zagnati eno za drugo ali pa vse naenkrat.\n",
        "\n",
        "<center>\n",
        "\n",
        "(Po potrebi poglej https://colab.research.google.com/notebooks/welcome.ipynb za osnovne informacije o uporabi Colab-a.)\n",
        "\n",
        "\n",
        "<br>\n",
        "\n",
        "<b> Sproti rešuj male \"programerske\" izzive in se nauči dela z Jupytrom. </b>\n",
        "</center>\n",
        "\n",
        "***\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y7D6sf99i0n-",
        "colab_type": "text"
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      "source": [
        "### Predpriprava za začetnike - Supernakratko o nizih (vektorjih) \n",
        "Želimo narediti niz vrednosti in izračunati funkcijo za te vrednosti. Na primer, niz (ali vektor) z vrednostmi od 0 do 5 naredimo preprosto takole:  \n",
        "x=[0,1,2,3,4]. Ta izraz lahko preiskusiš tako, da narediš novo celico (klikneš +Code) in zaženeš z Ctrl-Enter. Izgleda, kot da se ni zgodilo nič, v resnici se je ukaz izvedel in v x so spravljene vrednosti kot zapisano. To lahko preveriš tako, da pod to vrstico napišeš x ali display(x) ali print(x) in izpisala se bo vrednost x-a. Nize (vektorje) naredimo pogosto z ukazi kot npr. y=range(4). Toda pozor: python začne šteti z 0 in ne 1. Range(4) torej naredi niz štirih vrednosti, 0,1,2,3. Vrednost posameznega elementa v nizu dobimo z y[indeks], kjer ima lahko indeks vrednosti od 0 do 3. Lahko uporabimo tudi ukaz arange iz modula Numpy, npr. z=np.arange(0,11,2), ki naredi niz od 0 do 10 s korakom 2. Podoben je ukaz s=np.linspace(0,10,6), ki naredi niz od 0 do 10 razdeljen na 6 \n",
        "števil z enakim razmikom. Na nizih lahko delamo enake operacije, kot bi jih delali s številom. Npr. ukaz p=2*s+3 pomnoži vse vrednosti niza s z 2 in jim prišteje 3.\n",
        "\n",
        "In še beseda o matematičnih funkcijah. Običajno jih vsebuje modul Numpy. Npr. np.pi vrne vrednosti $\\pi$, np.sin(x) vrne sinus x-a, np.exp(x) vrednost(i) eksponentne funkcije, itd. Če je x niz, bo niz tudi rezultat operacije. "
      ]
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      "metadata": {
        "id": "LSb-PtsafRdk",
        "colab_type": "code",
        "outputId": "1c5e38e2-65a5-4387-be1a-c1975acb3d80",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        }
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      "source": [
        "# Če poznaš osnove nizov, boš to preskočil\n",
        "# To je komentar, izpisi ukaza print so v naslednji celici\n",
        "\n",
        "# Različni načini kreiranja nizov \n",
        "x=[0,1,2,3,4]\n",
        "print(x) # izpiše [0, 1, 2, 3, 4]\n",
        "y=range(4)\n",
        "print(y)\n",
        "print(y[3]) # izpiše četrto vrednost v nizu, saj začne python šteti od 0 in ne od 1\n",
        "\n",
        "import numpy as np # vnos modula numpy, ukaze se naslavlja z np.ukaz\n",
        "y=np.arange(5) # podobno kot range, samo s knjižnico numpy\n",
        "display(y)  # isto kot print\n",
        "z=np.arange(0,11,2) # niz od 0 do 11 po koraku 2\n",
        "print(z)\n",
        "s=np.linspace(0,10,6) # niz od 0 do 10 s 6 vrednostmi, rezultat torej [ 0  2  4  6  8 10]\n",
        "print(s)\n",
        "p=2*s+3 # vse vrednosti v nizu pomnožimo z 2 in prištejemo 3\n",
        "print(p)\n",
        "a=np.sin(x) # sinus pač, x je v radianih\n",
        "print(a)\n",
        "b=np.exp(x) # eksponentna funkcija ...\n",
        "print(b)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0, 1, 2, 3, 4]\n",
            "range(0, 4)\n",
            "3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4])"
            ]
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          "output_type": "stream",
          "text": [
            "[ 0  2  4  6  8 10]\n",
            "[ 0.  2.  4.  6.  8. 10.]\n",
            "[ 3.  7. 11. 15. 19. 23.]\n",
            "[ 0.          0.84147098  0.90929743  0.14112001 -0.7568025 ]\n",
            "[ 1.          2.71828183  7.3890561  20.08553692 54.59815003]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H8xTjlX2sRo2",
        "colab_type": "text"
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      "source": [
        "<b> Zdaj pa ti: </b>\n",
        "* Izdelaj niz vrednosti x od 0 do $2\\pi$ z 20 točkami in izračunaj $y=2\\sin(x)$. Rešitev je na koncu zvezka.\n",
        "* Naredi niz (vektor) t, ki bo imel vrednosti od 0 do 30, s korakom po 0.1. Nato naredi še niz Q, ki bo ustrezal funkciji $Q(t) = 5\\left( {1 - {e^{ - \\frac{t}{{10}}}}} \\right)$. Za rešitev poglej spodnjo celico z izračunom in izrisom."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sLH0pdeoX2Dj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wuf61xHPOIJi",
        "colab_type": "text"
      },
      "source": [
        "## Izračun in izris toka kot funkcije časa v grafu\n",
        "\n",
        "Jupyter je zasnovan na programskem jeziku Python. Za matematične operacije pogosto uporabljamo knjižnico Numpy, za izrise pa knjižnico Matplotlib. Pred začetkom dela je potrebno v kodo vnesti knjižnice (module), ki jih želimo uporabljati."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "akR2AFsreu_g",
        "colab_type": "text"
      },
      "source": [
        "## Naboj v odvisnosti od časa in tok kot odvod naboja po času\n",
        "\n",
        "Za primer bomo vzeli elektrenje kondenzatorja s tokom. Izkaže se, da je to elektrenje eksponentna funkcija, odvisna od časovne konstante $\\tau=RC$. \n",
        "\n",
        "Izrisali bomo naboj kot funkcijo časa\n",
        "\n",
        "$Q(t) = 5\\left( {1 - {e^{ - \\frac{t}{{10{\\rm{\\: s}}}}}}} \\right){\\rm{mC}}$\n",
        "\n",
        "ter tok kot odvod naboja po času\n",
        "\n",
        "$i(t) =\\frac{d Q}{dt}=0.5{e^{-t/10 \\: \\rm s}} \\: \\rm mA $\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VTMmnQDfe6RJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# vnos potrebnih knjižnic, matplotlib za grafe in numpy za matematične operacije\n",
        "#%matplotlib inline # potreben ukaz, da se slike izrišejo v notebooku\n",
        "# ko smo enkrat knjižnico (modul) zagnali z ukazom import, to velja za vse celice v zvezku\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np  # np bo okrajšava za ukaze iz knjižnice numpy "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V6djPmic1BLs",
        "colab_type": "text"
      },
      "source": [
        "### Najpreprostejši način izračuna in izrisa"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "smUaQQFq0-Gy",
        "colab_type": "code",
        "outputId": "3652ecb5-d48d-4c1c-8aa7-c60c399d01d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "# kreiranje časovnega niza od 0 do 30 s korakom 0.1\n",
        "t = np.arange(0.0, 30.0, 0.1) # np. pomeni, da gre za uporabo funkcije iz knjižnice numpy\n",
        "# Izračun naboja\n",
        "Q = 5*(1-np.exp(-t/10))\n",
        "\n",
        "# Priprava slike\n",
        "plt.figure()  # plt. pomeni, da gre za uporabo knjižnice matplotlib\n",
        "plt.plot(t, Q)  # izris, kje kjer je t na X osi in Q na Y osi\n",
        "\n",
        "# oblikovanje slike\n",
        "plt.xlabel('Čas (s)')\n",
        "plt.ylabel('Naboj (mC)') \n",
        "plt.title('Naboj v odvisnosti od časa')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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R0SqNO84+nAydqSoiMdJgH7yZ5QFXA98DHgBGufvWligsWf3u+YUs2lTOfRcU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\npYAXEUlSCngRkSSlgBcRSVL/H5cvGLjgG3smAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ka-dUK-l1Ws5",
        "colab_type": "text"
      },
      "source": [
        "### Nekoliko drugačen način - subplot\n",
        "\n",
        "Subplot omogoča več načinov izrisa in naslavljanja grafov."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kqn1-1GN1Z9G",
        "colab_type": "code",
        "outputId": "2bdf2c60-ab06-4ac6-f3c8-8e89442f34c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "# kreiranje časovnega niza od 0 do 30 s korakom 0.1\n",
        "t = np.arange(0.0, 30.0, 0.1)\n",
        "# Izračun naboja\n",
        "Q = 5*(1-np.exp(-t/10))\n",
        "\n",
        "# Priprava slike\n",
        "fig, ax = plt.subplots()\n",
        "ax.plot(t, Q)\n",
        "\n",
        "ax.set(xlabel='čas (s)', ylabel='Naboj (mC)', # Označitev osi\n",
        "       title='Naboj v odvisnosti od časa')\n",
        "ax.grid()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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oczcW8atXVnL2sFy+PW5gtMsRkQTRmICvDP7daWYXmNlJQOdGbHcGMAE4y8wW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\niEiCUsCLiCQoBbyISIL6fyP3ngFJdHUYAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v6k3F3RZYFix",
        "colab_type": "text"
      },
      "source": [
        "<b> Zdaj pa ti: </b>\n",
        "* Spremeni kodo tako, da boš namesto naboja kot funcijo časa izrisal krivuljo toka kot funkcijo časa. \n",
        "*  Spremeni kodo tako, da boš izrisal  krivuljo za čase od 0 do 50 s.\n",
        "* Spremeni kodo tako, da dodaš enako krivuljo, ki pa bo dodala le pike v redkih točkah. Namig: kreiraj še en niz z manj točkami in izračunaj Q za ta niz in ga izriši z dodanim ukazom ax.plot(t1,Q1,'o'). (Rešitev je na koncu zvezka)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4CQGuyb83Tup",
        "colab_type": "text"
      },
      "source": [
        "## Hkraten izris toka in naboja na enem grafu\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OVYDLAPzCduC",
        "colab_type": "code",
        "outputId": "6a5b1ddc-633b-41d2-e99d-20267efeb7dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "# vnos knjižnic in izračun naboja smo opravili že v prejšnji celici\n",
        "# dodamo še izračun toka\n",
        "i=0.5*np.exp(-t/10)\n",
        "# Priprava slike\n",
        "fig, ax = plt.subplots()\n",
        "ax.plot(t, Q, label=\"$Q(t)$\") # oznake so lahko LaTex oblike\n",
        "ax.plot(t, i, label=\"$i(t$)\")\n",
        "# pozicioniranje legende\n",
        "ax.legend(bbox_to_anchor=(0.8,.5), loc='upper left',  borderaxespad=0.) \n",
        "\n",
        "ax.set(xlabel='čas (s)', ylabel='Naboj in Tok',\n",
        "       title='Naboj in tok v odvisnosti od časa')\n",
        "ax.grid()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wYECj8ZGRkRQVFbFr1y4yMzMBmDFjBqeddhoPP/wwY8eOBaC2tpYVK1Z4Ncb2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\n8MYYE6QswRtjTJD6/xsW0995QGGzAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "idcSK_8BgZsd",
        "colab_type": "text"
      },
      "source": [
        "<b> Zdaj pa ti: </b>\n",
        "* preoblikuj obliko krivulj, na primer barvo, debelino, črtkano črto itd. Pri tem si pomagaj s primeri na strani https://matplotlib.org/3.1.1/tutorials/introductory/pyplot.html ali spremeni ustrezno tole vrstico plt.plot( x,y, marker='o', markerfacecolor='blue', markersize=12, color='skyblue', linewidth=4)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JWunXwxYN3oG",
        "colab_type": "text"
      },
      "source": [
        "## Izris dveh krivulj na enem grafu z ločenima osema\n",
        "\n",
        "V prejšnjem primeru smo izrisali obe krivulji na istem grafu. Ker imata tok in naboj različne enote, je bolj primerno izrisati vsako krivuljo na svoji osi. Ena varianta je, da izrišemo na istem grafu dve krivulji vendar z različnima ordinatnima osema."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4tUi8TQVCdft",
        "colab_type": "code",
        "outputId": "2cf5b3ab-17e2-4fc8-a10d-6b3ad8a6d488",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "fig, ax1 = plt.subplots()\n",
        "\n",
        "color = 'tab:red'\n",
        "ax1.set_xlabel('Čas (s)')\n",
        "ax1.set_ylabel('Naboj (mC)', color=color)\n",
        "ax1.plot(t, Q, color=color)\n",
        "ax1.tick_params(axis='y', labelcolor=color)\n",
        "ax1.grid(axis='x')\n",
        "ax1.text(2, 2, r'$Q(t)$', fontsize=12)  # dodamo tekst na pozicijo x=2, y=2. Tekst je v obliki Latex formule, ker je znotraj $$\n",
        "\n",
        "ax2 = ax1.twinx()  # x os za drugi plot naj bo enaka x osi prvega plota\n",
        "color = 'tab:blue'\n",
        "ax2.set_ylabel('Tok (mA)', color=color)  \n",
        "ax2.plot(t, i, color=color)\n",
        "ax2.tick_params(axis='y', labelcolor=color)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7N4nvv0/w5dp3JiqE8Ag5OAfyfJnTG5idd3yWc35j2QzGRcB0YInRbis+Y15T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\n5jf/mNvlM9hYvgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RS3GlV1hjgIC",
        "colab_type": "text"
      },
      "source": [
        "<b> Zdaj pa ti: </b>\n",
        "* Z ax1.text smo dodali tekst Q(t) nad krivuljo, glede na koordinate krivulje. Q(t) je znotraj znakov $$, ker se jih vpisuje kot enačbo. Dodaj še i(t) na ustrezno mesto na grafu. Namig: Lahko dodamo še en ukaz ax1.text, s tem, da bo za kooridato lokacije upoštevana Y os od prvega plota. Če dodamo ukaz ax2.text, bo upoštevana Y os od drugega plota.\n",
        "* Namesto, da vpišemo koordinate lokacije teksta, lahko lokacijo določimo z indeksom v nizu časa in funkcije, torej kot t[indeks],Q[indeks]. Če imamo označene krivulje s tekstom, ne potrebujemo različnih barv. To je včasih koristno, npr. pri črno-belem tiskanju. (Rešitev je na koncu zvezka)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "USjq1N7rny2N",
        "colab_type": "text"
      },
      "source": [
        "## Izris obeh krivulj v ločenih grafih\n",
        "Namesto na enem grafu z dvema različnima osema narišimo krivulji v dveh grafih, npr. enega pod drugim. Pri tem dodamo še eno x os z manj točkami, ki omogoča izris krivulje in simbolov na krivulji. (Če bi izrisali simbole na originalni x osi, bi se prekrivali in bi delovalo kot ena debela črta. Če pa bi izrisali funkcijo le v malo točkah, ne bi delovala dovolj zvezno.)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gZakmV-Hn4tG",
        "colab_type": "code",
        "outputId": "d4f0ad30-2e7f-4765-9761-28f97e9b21b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "t1 = np.arange(0.0, 30.0, 5)\n",
        "# Izračun naboja in toka v manj točkah\n",
        "Q1= 5*(1-np.exp(-t1/10))\n",
        "i1= 0.5*np.exp(-t1/10)\n",
        "\n",
        "plt.figure()\n",
        "plt.subplot(211)\n",
        "plt.plot(t, Q, 'k', t1, Q1, 'bo')\n",
        "plt.ylabel('Naboj (mC)')\n",
        "plt.title('Naboj in tok v odvisnosti od časa')\n",
        "plt.grid()\n",
        "\n",
        "plt.subplot(212)\n",
        "plt.plot(t, i, 'k', t1, i1, 'bo')\n",
        "plt.ylabel('Tok (mA)')\n",
        "plt.xlabel('čas (s)')\n",
        "\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VW3wclzEenTx5kvXr1zN//nzmz5/PokWL2L59hcd5ReLZv/83oqKiKjhKY06PTxOBqj4B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\nJshZIjDGmCBnicAYY4KcJQJjjAly/w9kqAKEVGtwMwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KvK0uQUmw5gl",
        "colab_type": "text"
      },
      "source": [
        "<b> Zdaj pa ti: </b>\n",
        "* Včasih je primerno postaviti tudi en graf poleg drugega. Nadomesti plt.subplot(211) in plt.subplot(212) z plt.subplot(121) in plt.subplot(122).\n",
        "* Če je graf premajhen, ga povečaj z dodatkom figsize: npr. plt.figure(figsize=(15, 8)). Potem je potrebno ustrezno popraviti tudi označevanje osi. Dodatno: za vse grafe v zvezku se spremeni nastavitve velikosti grafov z plt.rcParams[\"figure.figsize\"] = (10,10)\n",
        "* Več o subplots na https://jakevdp.github.io/PythonDataScienceHandbook/04.08-multiple-subplots.html \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RothXFoVo2wx",
        "colab_type": "code",
        "outputId": "154ef1f2-bd5f-448c-cb4b-ba23dd9ce0cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        }
      },
      "source": [
        "t1 = np.arange(0.0, 30.0, 5)\n",
        "\n",
        "# Izračun naboja in toka v manj točkah\n",
        "Q1= 5*(1-np.exp(-t1/10))\n",
        "i1= 0.5*np.exp(-t1/10)\n",
        "\n",
        "plt.figure(figsize=(15, 8))\n",
        "plt.subplot(121)\n",
        "plt.plot(t, Q, 'k', t1, Q1, 'bo')\n",
        "plt.xlabel('čas (s)')\n",
        "plt.ylabel('Naboj (mC)')\n",
        "plt.title('Naboj v odvisnosti od časa')\n",
        "plt.grid()\n",
        "\n",
        "plt.suptitle('Graf naboja na levi in toka na desni')\n",
        "\n",
        "plt.subplot(122)\n",
        "plt.plot(t, i, 'k', t1, i1, 'bo')\n",
        "plt.ylabel('Tok (mA)')\n",
        "plt.xlabel('čas (s)')\n",
        "\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7h9DSwFPDy/Z+BizD487OcHwBQsvwNoU/zg5vO6IS8P3xgrj7IWAiodmwroRmjH7J4vBN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\nAABAhKO4AQAAAECEo7gBAAAAQIT7f5JsnIpkMG7LAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1080x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N6TcMKwOzlda",
        "colab_type": "text"
      },
      "source": [
        "## Več krivulj na enem grafu z različnimi parametri\n",
        "\n",
        "Pogosto nas zanima, kako določen parameter vpliva na spremembo oblike krivulje. Obstaja več načinov, kako to narediti."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7_7p8mTC0Rsx",
        "colab_type": "code",
        "outputId": "7696dbc8-e9de-4f1c-f710-acc9ef975310",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "# kreiranje časovnega niza od 0 do 30 s korakom 0.1\n",
        "t = np.arange(0.0, 30.0, 0.1)\n",
        "# Izračun naboja\n",
        "Q = 5*(1-np.exp(-t/10))\n",
        "Q2 = 5*(1-np.exp(-t/5))\n",
        "Q3 = 5*(1-np.exp(-t/1))\n",
        "\n",
        "# Priprava slike\n",
        "plt.figure()\n",
        "plt.plot(t, Q,label='Q1')\n",
        "plt.plot(t, Q2,label='Q2')\n",
        "plt.plot(t, Q3,label='Q3')\n",
        "\n",
        "\n",
        "plt.xlabel('čas (s)')\n",
        "plt.ylabel('Naboj (mC)')\n",
        "plt.title('Naboj v odvisnosti od časa')\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AVaoVQi7BO1wOcitzOX9QgM6g+fr3sOVjOPNPMOR0X0fjNS634c1lu3nii61U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\npP4foojS8N0B3bsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qvQGplZd44BS",
        "colab_type": "text"
      },
      "source": [
        "### Naboj kot funkcija"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IMfDdmsc4xbp",
        "colab_type": "code",
        "outputId": "30673c80-d6e1-4031-cae8-b757cd9bf769",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "# kreiranje časovnega niza od 0 do 30 s korakom 0.1\n",
        "t = np.arange(0.0, 30.0, 0.1)\n",
        "# Naboj kot funkcija \n",
        "def Q(tau):\n",
        "   return 5*(1-np.exp(-t/tau))\n",
        "\n",
        "tau=[10,5,1] # niz treh vrednosti tau\n",
        "labela=['Q1','Q2','Q3'] # niz treh oznak\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "# Zanka, ki se izvrši večkrat in iterira (povečuje) indeks i\n",
        "for i in range(3): \n",
        "  plt.plot(t, Q(tau[i]),label=labela[i])\n",
        "\n",
        "plt.xlabel('čas (s)')\n",
        "plt.ylabel('Naboj (mC)')\n",
        "plt.title('Naboj v odvisnosti od časa')\n",
        "\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AVaoVQi7BO1wOcitzOX9QgM6g+fr3sOVjOPNPMOR0X0fjNS634c1lu3nii61U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\npP4foojS8N0B3bsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ubA2UnAq-ZY-",
        "colab_type": "text"
      },
      "source": [
        "<b> Zdaj pa ti: </b>\n",
        "* Zapiši funkcijo toka in izriši krivulje za več vrednosti $\\tau$.\n",
        "* Spremeni oblike krivulj tako, da bodo vse črne in različnih oblik (črtkano, ...). Poglej https://matplotlib.org/gallery/lines_bars_and_markers/line_styles_reference.html . Rešitev je na koncu zvezka."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GLZLBH1_WY88",
        "colab_type": "text"
      },
      "source": [
        "## Rešitve"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cOtX9GJyqjhE",
        "colab_type": "text"
      },
      "source": [
        "***\n",
        "## Dodatne informacije\n",
        "\n",
        "Za osnovno delo z grafi si poglej spletno stran\n",
        "https://matplotlib.org/3.1.1/tutorials/introductory/pyplot.html#sphx-glr-tutorials-introductory-pyplot-py\n",
        "\n",
        "(kodo lahko spreminjate v tem zvezku (notebooku) ali pa kreirate novega z File/New Python 3 Notebook in preizkušate različne variante izrisov)\n",
        "\n",
        "Primeri iz Codelaba: https://colab.research.google.com/notebooks/charts.ipynb#scrollTo=WALI8x49GUpe"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xsbYEKaLze2T",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RDcRr__YxdS6",
        "colab_type": "text"
      },
      "source": [
        "***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L_we1hz-0Y0G",
        "colab_type": "text"
      },
      "source": [
        "## Rešitve"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qG_A5vTMWbTG",
        "colab_type": "code",
        "outputId": "350cab90-47f7-45d4-a478-296f7a72aac3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 150
        }
      },
      "source": [
        "## Niz vrednosti x in y\n",
        "x=np.linspace(0,2*np.pi,20)\n",
        "y=2*np.sin(x)\n",
        "print(x,y)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.         0.33069396 0.66138793 0.99208189 1.32277585 1.65346982\n",
            " 1.98416378 2.31485774 2.64555171 2.97624567 3.30693964 3.6376336\n",
            " 3.96832756 4.29902153 4.62971549 4.96040945 5.29110342 5.62179738\n",
            " 5.95249134 6.28318531] [ 0.00000000e+00  6.49398938e-01  1.22842543e+00  1.67433296e+00\n",
            "  1.93880053e+00  1.99316899e+00  1.83154665e+00  1.47144782e+00\n",
            "  9.51894786e-01  3.29189181e-01 -3.29189181e-01 -9.51894786e-01\n",
            " -1.47144782e+00 -1.83154665e+00 -1.99316899e+00 -1.93880053e+00\n",
            " -1.67433296e+00 -1.22842543e+00 -6.49398938e-01 -4.89858720e-16]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hr-LMhWX0aCn",
        "colab_type": "code",
        "outputId": "bb841f36-75db-4045-cf27-9900765d3dee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "# Izris dodatnih pik na krivulji\n",
        "# kreiranje časovnega niza od 0 do 30 s korakom 0.1\n",
        "t = np.arange(0.0, 30.0, 0.1)\n",
        "t1 = np.arange(0.0, 30.0, 5)\n",
        "# Izračun naboja\n",
        "Q = 5*(1-np.exp(-t/10))\n",
        "Q1= 5*(1-np.exp(-t1/10))\n",
        "\n",
        "# Priprava slike\n",
        "fig, ax = plt.subplots()\n",
        "ax.plot(t, Q)\n",
        "ax.plot(t1,Q1,'o')\n",
        "\n",
        "ax.set(xlabel='čas (s)', ylabel='Naboj (mC)', # Označitev osi\n",
        "       title='Naboj v odvisnosti od časa')\n",
        "ax.grid()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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F1meA5UBf4FeEns3aqLHwZpYCvAQ801ByT3R70rrVvqJdTssGIiJxozEJvpO7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\nKSV4EZE49f+x0F+Be42A8QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y2yZqbIG0Vy-",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YdPSbNpon7yx",
        "colab_type": "code",
        "outputId": "5f929ae8-df40-4dfe-f813-b0bb46ff60a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "# mali programerski izziv - tekstovna označitev krivulj\n",
        "i=0.5*np.exp(-t/10)\n",
        "fig, ax1 = plt.subplots()\n",
        "\n",
        "ax1.set_xlabel('Čas (s)')\n",
        "ax1.set_ylabel('Naboj (mC)')\n",
        "ax1.plot(t, Q)\n",
        "ax1.tick_params(axis='y')\n",
        "ax1.grid(axis='x')\n",
        "ax1.text(t[15], Q[10], r'$Q(t)$', fontsize=12)\n",
        "\n",
        "ax2 = ax1.twinx()  # x os za drugi plot naj bo enaka x osi prvega plota\n",
        "ax2.set_ylabel('Tok (mA)')  \n",
        "ax2.plot(t, i)\n",
        "ax2.tick_params(axis='y')\n",
        "ax2.text(t[15], i[10], r'$i(t)$', fontsize=12)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qCxHxAsZR3LP5iqYHqMjBCcC5Kw9LVQUR4e8j23E8PZcXvt1BQ28X+rZscMXX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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GWoIF2khoI-0",
        "colab_type": "code",
        "outputId": "8c8679dc-e4dc-4665-e020-08dd4d490602",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "# Naboj kot funkcija\n",
        "# kreiranje časovnega niza od 0 do 30 s korakom 0.1\n",
        "t = np.arange(0.0, 30.0, 0.1)\n",
        "# Naboj kot funkcija \n",
        "def Q(tau):\n",
        "   return 5*(1-np.exp(-t/tau))\n",
        "\n",
        "tau=[10,5,1]\n",
        "labela=['Q1','Q2','Q3']\n",
        "# labela=['$Q_1(t)$','$Q_2(t)$','$Q_3(t)$']  # Odkomentiraj za lepši izpis\n",
        "lstil=['-','--',':']\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "# Zanka, ki se izvrši večkrat in iterira (povečuje) indeks i\n",
        "for i in range(3): \n",
        "  plt.plot(t, Q(tau[i]),label=labela[i],ls=lstil[i],color='k')\n",
        "\n",
        "plt.xlabel('čas (s)')\n",
        "plt.ylabel('Naboj (mC)')\n",
        "plt.title('Naboj v odvisnosti od časa')\n",
        "\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FSvBaPXv2pFq1alSqVMnYoWQK+/fvp2vXrgQHB9O4cWNjh5PlSSk5ceIEK1as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\nmHI+AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nEjGQiD_H30v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}