nb/2021-12-08__biology_vs_Izh_subhtr.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4abb0877",
   "metadata": {},
   "source": [
    "# 2021-12-08 • Biology vs Izhikevich subthreshold"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3a98705",
   "metadata": {},
   "source": [
    "## [Prelude]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f4c90d83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preloading: numpy, numba, matplotlib.pyplot, seaborn.\n",
      "Importing from submodules … ✔\n",
      "Imported `np`, `mpl`, `plt`, `sns`, `pd`\n",
      "Imported codebase (`voltage_to_wiring_sim`) as `v`\n",
      "Imported `*` from `v.support.units`\n",
      "Setup autoreload\n"
     ]
    }
   ],
   "source": [
    "from voltage_to_wiring_sim.notebook_init import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "23ef5982",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "This cell was last run by `tfiers` on `yoga` on **Wed 19 Jan** 2022, at 02:40 (UTC+0000).<br>\n",
       "[Last git commit](https://github.com/tfiers/voltage-to-wiring-sim/tree/0fb11d4d81503d2e90e6a4bc907dff0fd0edabda\n",
       ") (Tue 18 Jan 2022, 09:06).\n",
       "Uncommited changes to 56 files."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "v.print_reproducibility_info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29111b86",
   "metadata": {},
   "source": [
    "## $dV/dt$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "664c3795",
   "metadata": {},
   "source": [
    "We reproduce dV curve from Badel 2008, fig 2 B.  \n",
    "(fig 2 is for cortical neuron, fig 1 for something else).\n",
    "\n",
    "EIF paremeters are taken from fig 2F, for time since last spike > 200 ms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "25641653",
   "metadata": {},
   "outputs": [],
   "source": [
    "E_L = -57 * mV\n",
    "τ_m = 17 * ms\n",
    "V_rh = -42 * mV\n",
    "Δ_T = 1.5 * mV\n",
    "C = 200 * pF\n",
    "\n",
    "dV_dt_EIF = lambda V, I: (E_L - V + Δ_T*np.exp((V - V_rh) / Δ_T)) / τ_m + I / C;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a957a56f",
   "metadata": {},
   "source": [
    "Fit Izhikevich neuron to Badel's EIF neuron: same resting potential, same spiking threshold.  \n",
    "<!-- Parabola's curvature chosen by adapting $R k$ until slope at $E_L$ is the same. -->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1b5ebaa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.optimize import newton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "21f0875a",
   "metadata": {},
   "outputs": [],
   "source": [
    "V_t = newton(partial(dV_dt_EIF, I=0), -40*mV);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "38c13d84",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rk = 53\n",
    "\n",
    "k = 0.7 * (nS / mV)\n",
    "\n",
    "dV_dt_Izh = lambda V, I: (k * (V - E_L) * (V - V_t) + I) / C;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "48251f78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x312 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 290,
       "width": 486
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.axhline(y=0, color='k')\n",
    "ax.set_xlabel(\"$V$ (mV)\")\n",
    "v.horizontal_ylabel(ax, \"$\\dot{V}$ (mV/mS)\")\n",
    "V = np.linspace(-78 * mV, -32 * mV, num=1000)\n",
    "# V = np.linspace(E_L - 0.1*mV, E_L + 0.1*mV, num=1000)\n",
    "ax.plot(V / mV, dV_dt_Izh(V, I=0), label=\"Izhikevich\")\n",
    "ax.plot(V / mV, dV_dt_EIF(V, I=0), label=\"Experimental\")\n",
    "ax.set_xticks(E_L/mV + np.array([-20, -10, 0, 10, 20]))\n",
    "ax.plot(-70, 0, \"k>\", ms=7)\n",
    "ax.plot(-47, 0, \"k<\", ms=7)\n",
    "ax.plot(-35, 0, \"k>\", ms=7)\n",
    "ax.plot(E_L/mV, 0, \"ko\", ms=7)\n",
    "ax.plot(V_t/mV, 0, \"ko\", ms=7, markerfacecolor=\"white\")\n",
    "ax.set_xlim(-78, -32)\n",
    "ax.set_ylim(-0.9, 1.6)\n",
    "label_lines(ax.lines, xvals=[-68, -72]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93f7231d",
   "metadata": {},
   "source": [
    "## Simulation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23237386",
   "metadata": {},
   "source": [
    "### Input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b4c27188",
   "metadata": {},
   "outputs": [],
   "source": [
    "from voltage_to_wiring_sim.support.test_current import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "231d8c78",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = 0.1 * ms\n",
    "duration = 1700 * ms\n",
    "\n",
    "timesteps = v.to_indices(duration, dt)\n",
    "\n",
    "I = np.zeros(timesteps)\n",
    "\n",
    "Q = 0.22 * nA * 10 * ms\n",
    "\n",
    "T = 200*ms\n",
    "τ=10*ms\n",
    "add_δ(1.7*Q, (t_δ_up := 100*ms), I, dt)\n",
    "add_δ(-1.7*Q, (t_δ_down := 200*ms), I, dt)\n",
    "add_pulse_with_decay(Q, 300*ms, T, I, dt, τ)\n",
    "add_pulse_with_decay(-Q, 450*ms, T, I, dt, τ)\n",
    "add_pulse_with_decay(2*Q, 600*ms, T, I, dt, τ)\n",
    "add_pulse_with_decay(-2*Q, 750*ms, T, I, dt, τ)\n",
    "add_plateau(4*Q, 900*ms, T, I, dt)\n",
    "add_ramp(4*Q, 1200*ms, T, I, dt)\n",
    "add_pulse_with_decay(1.1*Q, 1500 * ms, T-100*ms, I, dt)\n",
    "add_pulse_with_decay(1.1*Q, 1530 * ms, T-100*ms, I, dt)\n",
    "add_pulse_with_decay(1.1*Q, 1560 * ms, T-100*ms, I, dt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "584194f9",
   "metadata": {},
   "source": [
    "### Integration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cfaba592",
   "metadata": {},
   "outputs": [],
   "source": [
    "V_EIF = np.ones(timesteps) * E_L\n",
    "V_Izh = np.ones(timesteps) * E_L\n",
    "\n",
    "for i in range(timesteps - 1):\n",
    "    V_EIF[i+1] = V_EIF[i] + dV_dt_EIF(V_EIF[i], I[i]) * dt\n",
    "    V_Izh[i+1] = V_Izh[i] + dV_dt_Izh(V_Izh[i], I[i]) * dt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6e60532",
   "metadata": {},
   "source": [
    "### Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9e5663ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x556 with 3 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 455,
       "width": 826
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=3, sharex=True, **v.figsize(aspect=1.8, width=1000),\n",
    "                         gridspec_kw=dict(height_ratios=[1, 1, 0.7]))\n",
    "\n",
    "t = np.linspace(0, duration / ms, timesteps, False)\n",
    "axs[1].plot(t, V_EIF / mV, c=\"C1\")\n",
    "axs[0].plot(t, V_EIF / mV, c=\"C1\", alpha=0.3, label=\"EIF\")\n",
    "axs[0].plot(t, V_Izh / mV, c=\"C0\", label=\"Izhikevich\")\n",
    "v.add_reordered_legend(axs[0], [1,0])\n",
    "v.ylabel_inside(axs[0], \"Membrane voltage $V$ (mV)\", ylim_shift=18)\n",
    "v.ylabel_inside(axs[1], \"EIF membrane voltage (mV)\", ylim_shift=18)\n",
    "V_ticks = E_L / mV + np.arange(-20, 21, 10)\n",
    "axs[0].set_yticks(V_ticks)\n",
    "axs[1].set_yticks(V_ticks)\n",
    "axs[1].set_ylim(axs[0].get_ylim())\n",
    "plot_test_current(axs[2], t, I/nA, (-0.53, 0.53), yδ=0.8)\n",
    "v.ylabel_inside(axs[2], \"Input current $I$ (nA)\", ylim_shift=16)\n",
    "axs[0].set_xlim(left=0, right=duration/ms);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4efcd2f",
   "metadata": {},
   "source": [
    "## [Reproducibility]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b1f165f5",
   "metadata": {
    "tags": [
     "output_scroll"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "This cell was last run by `tfiers` on `yoga`<br>on **Wed 19 Jan** 2022, at 02:40 (UTC+0000)."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "[Last git commit](https://github.com/tfiers/voltage-to-wiring-sim/tree/0fb11d4d81503d2e90e6a4bc907dff0fd0edabda\n",
       ") (Tue 18 Jan 2022, 09:06)."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Uncommited changes to:\n",
       "```\n",
       "M  .gitmodules\n",
       " D Manifest.toml\n",
       "MM Project.toml\n",
       "MM ReadMe.md\n",
       "R  dev/Distributions -> codebase/julia/dev/Distributions\n",
       "RD src/VoltageToMap.jl -> julia/src/VoltageToMap.jl\n",
       "RD src/myplotlib/colors.jl -> julia/src/myplotlib/colors.jl\n",
       "RD src/myplotlib/imports.jl -> julia/src/myplotlib/imports.jl\n",
       "RD src/myplotlib/plot.jl -> julia/src/myplotlib/plot.jl\n",
       "RD src/myplotlib/set.jl -> julia/src/myplotlib/set.jl\n",
       "RD src/myplotlib/style.jl -> julia/src/myplotlib/style.jl\n",
       "RD src/myplotlib/ticks.jl -> julia/src/myplotlib/ticks.jl\n",
       "RD src/myplotlib/typecompat.jl -> julia/src/myplotlib/typecompat.jl\n",
       "RD src/show.jl -> julia/src/show.jl\n",
       "RD src/signal.jl -> julia/src/signal.jl\n",
       "RD src/unitfuldistributions.jl -> julia/src/unitfuldistributions.jl\n",
       "M  notebooks/2022-01-08__1000_to_1.ipynb\n",
       "M  notebooks/2022-01-08__1000_to_1.jl\n",
       "RD codebase/tests/test_run_embedded_tests.py -> python/tests/test_run_embedded_tests.py\n",
       "RD codebase/voltage_to_wiring_sim/__init__.py -> python/voltage_to_wiring_sim/__init__.py\n",
       "RD codebase/voltage_to_wiring_sim/conntest/STA.py -> python/voltage_to_wiring_sim/conntest/STA.py\n",
       "RD codebase/voltage_to_wiring_sim/conntest/__init__.py -> python/voltage_to_wiring_sim/conntest/__init__.py\n",
       "RD codebase/voltage_to_wiring_sim/conntest/classification.py -> python/voltage_to_wiring_sim/conntest/classification.py\n",
       "RD codebase/voltage_to_wiring_sim/conntest/classification_IE.py -> python/voltage_to_wiring_sim/conntest/classification_IE.py\n",
       "RD codebase/voltage_to_wiring_sim/conntest/permutation_test.py -> python/voltage_to_wiring_sim/conntest/permutation_test.py\n",
       "RD codebase/voltage_to_wiring_sim/experiments/N_to_1.py -> python/voltage_to_wiring_sim/experiments/N_to_1.py\n",
       "RD codebase/voltage_to_wiring_sim/experiments/N_to_1_IE.py -> python/voltage_to_wiring_sim/experiments/N_to_1_IE.py\n",
       "RD codebase/voltage_to_wiring_sim/experiments/__init__.py -> python/voltage_to_wiring_sim/experiments/__init__.py\n",
       "RD codebase/voltage_to_wiring_sim/notebook_init.py -> python/voltage_to_wiring_sim/notebook_init.py\n",
       "RD codebase/voltage_to_wiring_sim/sim/__init__.py -> python/voltage_to_wiring_sim/sim/__init__.py\n",
       "RD codebase/voltage_to_wiring_sim/sim/imaging.py -> python/voltage_to_wiring_sim/sim/imaging.py\n",
       "RD codebase/voltage_to_wiring_sim/sim/izhikevich_neuron.py -> python/voltage_to_wiring_sim/sim/izhikevich_neuron.py\n",
       "RD codebase/voltage_to_wiring_sim/sim/neuron_params.py -> python/voltage_to_wiring_sim/sim/neuron_params.py\n",
       "RD codebase/voltage_to_wiring_sim/sim/poisson_spikes.py -> python/voltage_to_wiring_sim/sim/poisson_spikes.py\n",
       "RD codebase/voltage_to_wiring_sim/sim/synapses.py -> python/voltage_to_wiring_sim/sim/synapses.py\n",
       "RD codebase/voltage_to_wiring_sim/support/__init__.py -> python/voltage_to_wiring_sim/support/__init__.py\n",
       "RD codebase/voltage_to_wiring_sim/support/array_wrapper.py -> python/voltage_to_wiring_sim/support/array_wrapper.py\n",
       "RD codebase/voltage_to_wiring_sim/support/high_performance.py -> python/voltage_to_wiring_sim/support/high_performance.py\n",
       "RD codebase/voltage_to_wiring_sim/support/misc.py -> python/voltage_to_wiring_sim/support/misc.py\n",
       "RD codebase/voltage_to_wiring_sim/support/plot_style.py -> python/voltage_to_wiring_sim/support/plot_style.py\n",
       "RD codebase/voltage_to_wiring_sim/support/plot_util.py -> python/voltage_to_wiring_sim/support/plot_util.py\n",
       "RD codebase/voltage_to_wiring_sim/support/printing.py -> python/voltage_to_wiring_sim/support/printing.py\n",
       "RD codebase/voltage_to_wiring_sim/support/reproducibility.py -> python/voltage_to_wiring_sim/support/reproducibility.py\n",
       "RD codebase/voltage_to_wiring_sim/support/scalebar.py -> python/voltage_to_wiring_sim/support/scalebar.py\n",
       "RD codebase/voltage_to_wiring_sim/support/signal.py -> python/voltage_to_wiring_sim/support/signal.py\n",
       "RD codebase/voltage_to_wiring_sim/support/spike_train.py -> python/voltage_to_wiring_sim/support/spike_train.py\n",
       "RD codebase/voltage_to_wiring_sim/support/test_current.py -> python/voltage_to_wiring_sim/support/test_current.py\n",
       "RD codebase/voltage_to_wiring_sim/support/units.py -> python/voltage_to_wiring_sim/support/units.py\n",
       "D  runtime.txt\n",
       " M setup.py\n",
       "?? JuliaManifest.toml\n",
       "?? JuliaProject.toml\n",
       "?? codebase/julia/Manifest.toml\n",
       "?? codebase/julia/Project.toml\n",
       "?? codebase/julia/src/\n",
       "?? codebase/python/\n",
       "```"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Platform:"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Windows-10\n",
      "CPython 3.9.7 (C:\\conda\\python.exe)\n",
      "Intel(R) Core(TM) i7-10510U CPU @ 1.80GHz\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "Dependencies of `voltage_to_wiring_sim` and their installed versions:"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numpy                1.21.4\n",
      "matplotlib           3.4.3\n",
      "numba                0.53.1\n",
      "joblib               1.0.1\n",
      "seaborn              0.11.1\n",
      "scipy                1.7.2\n",
      "preload              2.2\n",
      "nptyping             1.4.0\n",
      "matplotlib-label-lines 0.4.3\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "Full conda list:"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "```\n",
       "# packages in environment at C:\\conda:\n",
       "#\n",
       "# Name                    Version                   Build  Channel\n",
       "anyio                     3.3.4            py39hcbf5309_1    conda-forge\n",
       "appdirs                   1.4.4              pyh9f0ad1d_0    conda-forge\n",
       "argon2-cffi               21.1.0           py39hb82d6ee_2    conda-forge\n",
       "async_generator           1.10                       py_0    conda-forge\n",
       "atomicwrites              1.4.0              pyh9f0ad1d_0    conda-forge\n",
       "attrs                     20.3.0             pyhd3deb0d_0    conda-forge\n",
       "babel                     2.9.1              pyh44b312d_0    conda-forge\n",
       "backcall                  0.2.0              pyh9f0ad1d_0    conda-forge\n",
       "backports                 1.0                        py_2    conda-forge\n",
       "backports.functools_lru_cache 1.6.4              pyhd8ed1ab_0    conda-forge\n",
       "black                     20.8b1                     py_1    conda-forge\n",
       "bleach                    4.1.0              pyhd8ed1ab_0    conda-forge\n",
       "bokeh                     2.4.1            py39hcbf5309_2    conda-forge\n",
       "brian2                    2.5.0.1          py39h2e07f2f_0    conda-forge\n",
       "brotlipy                  0.7.0           py39hb82d6ee_1003    conda-forge\n",
       "bzip2                     1.0.8                h8ffe710_4    conda-forge\n",
       "ca-certificates           2021.10.8            h5b45459_0    conda-forge\n",
       "certifi                   2021.10.8        py39hcbf5309_1    conda-forge\n",
       "cffi                      1.15.0           py39h0878f49_0    conda-forge\n",
       "chardet                   4.0.0            py39hcbf5309_2    conda-forge\n",
       "charset-normalizer        2.0.0              pyhd8ed1ab_0    conda-forge\n",
       "click                     7.1.2              pyh9f0ad1d_0    conda-forge\n",
       "cloudpickle               2.0.0              pyhd8ed1ab_0    conda-forge\n",
       "colorama                  0.4.4              pyh9f0ad1d_0    conda-forge\n",
       "conda                     4.11.0           py39hcbf5309_0    conda-forge\n",
       "conda-package-handling    1.7.3            py39hb3671d1_1    conda-forge\n",
       "console_shortcut          0.1.1                         4  \n",
       "cryptography              35.0.0           py39h7bc7c5c_2    conda-forge\n",
       "cycler                    0.11.0             pyhd8ed1ab_0    conda-forge\n",
       "cython                    0.29.26          py39h415ef7b_0    conda-forge\n",
       "cytoolz                   0.11.2           py39hb82d6ee_1    conda-forge\n",
       "dask                      2021.1.0           pyhd8ed1ab_0    conda-forge\n",
       "dask-core                 2021.1.0           pyhd8ed1ab_0    conda-forge\n",
       "dataclasses               0.8                pyhc8e2a94_3    conda-forge\n",
       "debugpy                   1.5.1            py39h415ef7b_0    conda-forge\n",
       "decorator                 5.1.0              pyhd8ed1ab_0    conda-forge\n",
       "defusedxml                0.7.1              pyhd8ed1ab_0    conda-forge\n",
       "distributed               2021.1.1         py39hcbf5309_0    conda-forge\n",
       "docutils                  0.16                     pypi_0    pypi\n",
       "entrypoints               0.3             pyhd8ed1ab_1003    conda-forge\n",
       "freetype                  2.10.4               h546665d_1    conda-forge\n",
       "fsspec                    2021.11.0          pyhd8ed1ab_0    conda-forge\n",
       "gitdb                     4.0.9              pyhd8ed1ab_0    conda-forge\n",
       "gitpython                 3.1.24             pyhd8ed1ab_0    conda-forge\n",
       "gsl                       2.7                  hdfb1a43_0    conda-forge\n",
       "heapdict                  1.0.1                      py_0    conda-forge\n",
       "icu                       68.2                 h0e60522_0    conda-forge\n",
       "idna                      3.1                pyhd3deb0d_0    conda-forge\n",
       "importlib-metadata        4.8.2            py39hcbf5309_0    conda-forge\n",
       "importlib_metadata        4.8.2                hd3eb1b0_0  \n",
       "importlib_resources       5.4.0              pyhd8ed1ab_0    conda-forge\n",
       "iniconfig                 1.1.1              pyh9f0ad1d_0    conda-forge\n",
       "intel-openmp              2021.4.0          h57928b3_3556    conda-forge\n",
       "ipykernel                 6.5.0            py39h832f523_1    conda-forge\n",
       "ipython                   7.29.0           py39h832f523_2    conda-forge\n",
       "ipython_genutils          0.2.0                      py_1    conda-forge\n",
       "ipywidgets                7.6.3              pyhd3deb0d_0    conda-forge\n",
       "jbig                      2.1               h8d14728_2003    conda-forge\n",
       "jedi                      0.18.0           py39hcbf5309_3    conda-forge\n",
       "jinja2                    3.0.3              pyhd8ed1ab_0    conda-forge\n",
       "joblib                    1.0.1                    pypi_0    pypi\n",
       "jpeg                      9d                   h8ffe710_0    conda-forge\n",
       "json5                     0.9.5              pyh9f0ad1d_0    conda-forge\n",
       "jsonschema                4.2.1              pyhd8ed1ab_0    conda-forge\n",
       "jupyter_client            7.0.6              pyhd8ed1ab_0    conda-forge\n",
       "jupyter_contrib_core      0.3.3                      py_2    conda-forge\n",
       "jupyter_contrib_nbextensions 0.5.1              pyhd8ed1ab_2    conda-forge\n",
       "jupyter_core              4.9.1            py39hcbf5309_1    conda-forge\n",
       "jupyter_highlight_selected_word 0.2.0           py39hcbf5309_1002    conda-forge\n",
       "jupyter_latex_envs        1.4.6           pyhd8ed1ab_1002    conda-forge\n",
       "jupyter_nbextensions_configurator 0.4.1            py39hcbf5309_2    conda-forge\n",
       "jupyter_server            1.11.2             pyhd8ed1ab_0    conda-forge\n",
       "jupyterlab                3.0.5              pyhd8ed1ab_0    conda-forge\n",
       "jupyterlab-classic        0.1.3              pyhd8ed1ab_0    conda-forge\n",
       "jupyterlab-sublime        0.4.1                    pypi_0    pypi\n",
       "jupyterlab_pygments       0.1.2              pyh9f0ad1d_0    conda-forge\n",
       "jupyterlab_server         2.8.2              pyhd8ed1ab_0    conda-forge\n",
       "jupyterlab_widgets        1.0.2              pyhd8ed1ab_0    conda-forge\n",
       "jupytext                  1.10.0             pyh44b312d_0    conda-forge\n",
       "jupytray                  0.7.post10+dirty           dev_0    <develop>\n",
       "keyring                   21.8.0                   pypi_0    pypi\n",
       "kiwisolver                1.3.2            py39h2e07f2f_1    conda-forge\n",
       "krb5                      1.19.2               h20d022d_3    conda-forge\n",
       "lcms2                     2.12                 h2a16943_0    conda-forge\n",
       "lerc                      3.0                  h0e60522_0    conda-forge\n",
       "libarchive                3.5.2                hb45042f_1    conda-forge\n",
       "libblas                   3.9.0                     8_mkl    conda-forge\n",
       "libcblas                  3.9.0                     8_mkl    conda-forge\n",
       "libclang                  11.1.0          default_h5c34c98_1    conda-forge\n",
       "libcurl                   7.80.0               h789b8ee_0    conda-forge\n",
       "libdeflate                1.8                  h8ffe710_0    conda-forge\n",
       "libiconv                  1.16                 he774522_0    conda-forge\n",
       "liblapack                 3.9.0                     8_mkl    conda-forge\n",
       "libpng                    1.6.37               h1d00b33_2    conda-forge\n",
       "libsodium                 1.0.18               h8d14728_1    conda-forge\n",
       "libsolv                   0.7.19               h7755175_5    conda-forge\n",
       "libssh2                   1.10.0               h680486a_2    conda-forge\n",
       "libtiff                   4.3.0                hd413186_2    conda-forge\n",
       "libxml2                   2.9.12               hf5bbc77_1    conda-forge\n",
       "libxslt                   1.1.33               h65864e5_3    conda-forge\n",
       "libzlib                   1.2.11            h8ffe710_1013    conda-forge\n",
       "llvmlite                  0.36.0           py39ha0cd8c8_0    conda-forge\n",
       "locket                    0.2.0                      py_2    conda-forge\n",
       "lxml                      4.6.4            py39h4fd7cdf_0    conda-forge\n",
       "lz4-c                     1.9.3                h8ffe710_1    conda-forge\n",
       "lzo                       2.10              he774522_1000    conda-forge\n",
       "m2w64-gcc-libgfortran     5.3.0                         6    conda-forge\n",
       "m2w64-gcc-libs            5.3.0                         7    conda-forge\n",
       "m2w64-gcc-libs-core       5.3.0                         7    conda-forge\n",
       "m2w64-gmp                 6.1.0                         2    conda-forge\n",
       "m2w64-libwinpthread-git   5.0.0.4634.697f757               2    conda-forge\n",
       "mamba                     0.7.14           py39h006a82b_0    conda-forge\n",
       "markdown-it-py            0.6.2              pyhd8ed1ab_0    conda-forge\n",
       "markdown2                 2.4.0                    pypi_0    pypi\n",
       "markupsafe                2.0.1            py39hb82d6ee_1    conda-forge\n",
       "matplotlib-base           3.4.3            py39h581301d_2    conda-forge\n",
       "matplotlib-inline         0.1.3              pyhd8ed1ab_0    conda-forge\n",
       "matplotlib-label-lines    0.4.3                    pypi_0    pypi\n",
       "mdit-py-plugins           0.2.5              pyhd8ed1ab_0    conda-forge\n",
       "menuinst                  1.4.18           py39hcbf5309_1    conda-forge\n",
       "mistune                   0.8.4           py39hb82d6ee_1005    conda-forge\n",
       "mkl                       2020.4             hb70f87d_311    conda-forge\n",
       "more-itertools            8.11.0             pyhd8ed1ab_0    conda-forge\n",
       "mpmath                    1.2.1              pyhd8ed1ab_0    conda-forge\n",
       "msgpack-python            1.0.2            py39h2e07f2f_2    conda-forge\n",
       "msys2-conda-epoch         20160418                      1    conda-forge\n",
       "multipledispatch          0.6.0                    pypi_0    pypi\n",
       "multitasking              0.0.9                    pypi_0    pypi\n",
       "mypy_extensions           0.4.3            py39hcbf5309_4    conda-forge\n",
       "nb_conda_kernels          2.3.1            py39hcbf5309_1    conda-forge\n",
       "nbclassic                 0.2.6              pyhd8ed1ab_0    conda-forge\n",
       "nbclient                  0.5.8              pyhd8ed1ab_0    conda-forge\n",
       "nbconvert                 6.3.0            py39hcbf5309_1    conda-forge\n",
       "nbdime                    2.1.0                      py_0    conda-forge\n",
       "nbformat                  5.0.8                      py_0    conda-forge\n",
       "nest-asyncio              1.5.1              pyhd8ed1ab_0    conda-forge\n",
       "notebook                  6.4.5              pyha770c72_0    conda-forge\n",
       "nptyping                  1.4.0                    pypi_0    pypi\n",
       "numba                     0.53.1           py39hb8cd55e_1    conda-forge\n",
       "numpy                     1.21.4           py39h6635163_0    conda-forge\n",
       "oauthlib                  3.1.0                    pypi_0    pypi\n",
       "oboe                      0.2                      pypi_0    pypi\n",
       "olefile                   0.46               pyh9f0ad1d_1    conda-forge\n",
       "openjpeg                  2.4.0                hb211442_1    conda-forge\n",
       "openssl                   1.1.1l               h8ffe710_0    conda-forge\n",
       "packaging                 21.2               pyhd8ed1ab_1    conda-forge\n",
       "pandas                    1.3.4            py39h2e25243_1    conda-forge\n",
       "pandoc                    2.16.1               h8ffe710_0    conda-forge\n",
       "pandocfilters             1.5.0              pyhd8ed1ab_0    conda-forge\n",
       "parso                     0.8.2              pyhd8ed1ab_0    conda-forge\n",
       "partd                     1.2.0              pyhd8ed1ab_0    conda-forge\n",
       "pathspec                  0.9.0              pyhd8ed1ab_0    conda-forge\n",
       "patsy                     0.5.2              pyhd8ed1ab_0    conda-forge\n",
       "pickleshare               0.7.5                   py_1003    conda-forge\n",
       "pillow                    8.4.0            py39h916092e_0    conda-forge\n",
       "pint                      0.18               pyhd8ed1ab_0    conda-forge\n",
       "pip                       21.3.1             pyhd8ed1ab_0    conda-forge\n",
       "pkginfo                   1.6.1                    pypi_0    pypi\n",
       "pluggy                    1.0.0            py39hcbf5309_2    conda-forge\n",
       "powershell_shortcut       0.0.1                         3  \n",
       "preload                   2.2                      pypi_0    pypi\n",
       "prometheus_client         0.12.0             pyhd8ed1ab_0    conda-forge\n",
       "prompt-toolkit            3.0.22             pyha770c72_0    conda-forge\n",
       "psutil                    5.8.0            py39hb82d6ee_2    conda-forge\n",
       "py                        1.11.0             pyh6c4a22f_0    conda-forge\n",
       "py-cpuinfo                8.0.0              pyhd8ed1ab_0    conda-forge\n",
       "pycosat                   0.6.3           py39hb82d6ee_1009    conda-forge\n",
       "pycparser                 2.21               pyhd8ed1ab_0    conda-forge\n",
       "pygments                  2.10.0             pyhd8ed1ab_0    conda-forge\n",
       "pyopenssl                 21.0.0             pyhd8ed1ab_0    conda-forge\n",
       "pypandoc                  1.6.3                    pypi_0    pypi\n",
       "pyparsing                 2.4.7              pyhd8ed1ab_1    conda-forge\n",
       "pyqt                      5.12.3           py39hb0d2dfa_4    conda-forge\n",
       "pyqt5-sip                 4.19.18                  pypi_0    pypi\n",
       "pyqtchart                 5.12                     pypi_0    pypi\n",
       "pyqtwebengine             5.12.1                   pypi_0    pypi\n",
       "pyrsistent                0.18.0           py39hb82d6ee_0    conda-forge\n",
       "pysocks                   1.7.1            py39hcbf5309_4    conda-forge\n",
       "pytest                    6.2.5            py39hcbf5309_1    conda-forge\n",
       "python                    3.9.7           h7840368_3_cpython    conda-forge\n",
       "python-dateutil           2.8.2              pyhd8ed1ab_0    conda-forge\n",
       "python_abi                3.9                      2_cp39    conda-forge\n",
       "pytz                      2021.3             pyhd8ed1ab_0    conda-forge\n",
       "pywin32                   302              py39hb82d6ee_2    conda-forge\n",
       "pywin32-ctypes            0.2.0                    pypi_0    pypi\n",
       "pywinpty                  1.1.6            py39h99910a6_0    conda-forge\n",
       "pyyaml                    6.0              py39hb82d6ee_3    conda-forge\n",
       "pyzmq                     22.3.0           py39he46f08e_1    conda-forge\n",
       "qt                        5.12.9               h5909a2a_4    conda-forge\n",
       "readme-renderer           28.0                     pypi_0    pypi\n",
       "regex                     2021.11.10       py39hb82d6ee_0    conda-forge\n",
       "reproc                    14.2.3               h8ffe710_0    conda-forge\n",
       "reproc-cpp                14.2.3               h0e60522_0    conda-forge\n",
       "requests                  2.26.0             pyhd8ed1ab_0    conda-forge\n",
       "requests-oauthlib         1.3.0                    pypi_0    pypi\n",
       "requests-toolbelt         0.9.1                    pypi_0    pypi\n",
       "rfc3986                   1.4.0                    pypi_0    pypi\n",
       "ruamel_yaml               0.15.80         py39hb82d6ee_1006    conda-forge\n",
       "scikit-learn              1.0.1            py39he931e04_2    conda-forge\n",
       "scipy                     1.7.2            py39hc0c34ad_0    conda-forge\n",
       "seaborn                   0.11.1               h57928b3_0    conda-forge\n",
       "seaborn-base              0.11.1             pyhd8ed1ab_1    conda-forge\n",
       "send2trash                1.8.0              pyhd8ed1ab_0    conda-forge\n",
       "setuptools                59.1.1           py39hcbf5309_0    conda-forge\n",
       "setuptools-scm            5.0.1                    pypi_0    pypi\n",
       "six                       1.16.0             pyh6c4a22f_0    conda-forge\n",
       "smmap                     3.0.5              pyh44b312d_0    conda-forge\n",
       "snakeviz                  2.1.0              pyh9f0ad1d_0    conda-forge\n",
       "sniffio                   1.2.0            py39hcbf5309_2    conda-forge\n",
       "sortedcontainers          2.4.0              pyhd8ed1ab_0    conda-forge\n",
       "sqlite                    3.36.0               h8ffe710_2    conda-forge\n",
       "statsmodels               0.13.1           py39h5d4886f_0    conda-forge\n",
       "sympy                     1.9              py39hcbf5309_1    conda-forge\n",
       "tblib                     1.7.0              pyhd8ed1ab_0    conda-forge\n",
       "terminado                 0.12.1           py39hcbf5309_1    conda-forge\n",
       "testpath                  0.5.0              pyhd8ed1ab_0    conda-forge\n",
       "tfiers                    0.3.2                     dev_0    <develop>\n",
       "threadpoolctl             3.0.0              pyh8a188c0_0    conda-forge\n",
       "tk                        8.6.11               h8ffe710_1    conda-forge\n",
       "toml                      0.10.2             pyhd8ed1ab_0    conda-forge\n",
       "toolz                     0.11.2             pyhd8ed1ab_0    conda-forge\n",
       "tornado                   6.1              py39hb82d6ee_2    conda-forge\n",
       "tqdm                      4.62.3             pyhd8ed1ab_0    conda-forge\n",
       "traitlets                 5.1.1              pyhd8ed1ab_0    conda-forge\n",
       "tweepy                    3.10.0                   pypi_0    pypi\n",
       "twine                     3.3.0                    pypi_0    pypi\n",
       "typed-ast                 1.5.0            py39hb82d6ee_0    conda-forge\n",
       "typing_extensions         3.10.0.2           pyha770c72_0    conda-forge\n",
       "typish                    1.9.1                    pypi_0    pypi\n",
       "tzdata                    2021e                he74cb21_0    conda-forge\n",
       "ucrt                      10.0.20348.0         h57928b3_0    conda-forge\n",
       "unitlib                   0.3                      pypi_0    pypi\n",
       "unyt                      2.8.0                      py_0    conda-forge\n",
       "urllib3                   1.26.7             pyhd8ed1ab_0    conda-forge\n",
       "vc                        14.2                 hb210afc_5    conda-forge\n",
       "voltage-to-wiring-sim     0.1                       dev_0    <develop>\n",
       "vs2015_runtime            14.29.30037          h902a5da_5    conda-forge\n",
       "wcwidth                   0.2.5              pyh9f0ad1d_2    conda-forge\n",
       "webencodings              0.5.1                      py_1    conda-forge\n",
       "websocket-client          1.2.1            py39hcbf5309_0    conda-forge\n",
       "wheel                     0.37.0             pyhd8ed1ab_1    conda-forge\n",
       "widgetsnbextension        3.5.2            py39hcbf5309_0    conda-forge\n",
       "win_inet_pton             1.1.0            py39hcbf5309_3    conda-forge\n",
       "windows-curses            2.2.0                    pypi_0    pypi\n",
       "winpty                    0.4.3                         4    conda-forge\n",
       "winshell                  0.6                      pypi_0    pypi\n",
       "xz                        5.2.5                h62dcd97_1    conda-forge\n",
       "yaml                      0.2.5                he774522_0    conda-forge\n",
       "yfinance                  0.1.55                   pypi_0    pypi\n",
       "zeromq                    4.3.4                h0e60522_1    conda-forge\n",
       "zict                      2.0.0                      py_0    conda-forge\n",
       "zipp                      3.6.0              pyhd8ed1ab_0    conda-forge\n",
       "zlib                      1.2.11            h8ffe710_1013    conda-forge\n",
       "zstd                      1.5.0                h6255e5f_0    conda-forge\n",
       "```"
      ]
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