{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Demo of benchmarking a 4-qubit parity check\n", "This notebook demonstrates how to compute the \"weight-X disturbance\" error metric between a *reference* and *test* data set using pyGSTi." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['CVXOPT', 'ECOS', 'GLPK', 'GLPK_MI', 'OSQP', 'SCS']\n" ] } ], "source": [ "import numpy as np\n", "import scipy as sp\n", "import itertools\n", "from matplotlib import pyplot as plt\n", "import seaborn as sns\n", "import time\n", "import cvxpy\n", "sns.set_style('white')\n", "%matplotlib inline\n", "\n", "import pygsti.extras.paritybenchmarking as pb\n", "#Note: if you don't want to install pyGSTi to run this notebook,\n", "# just grab the pygsti/extras/paritybenchmarking/disturbancecalc.py file, put it\n", "# in the same directory as this notebook, and use this line instead of the one above:\n", "# import disturbancecalc as pb\n", "\n", "print(cvxpy.installed_solvers())\n", "SOLVER='SCS' # if you see 'MOSEK' as an option below, change this to SOLVER='MOSEK' \n", " # as this is a better (but less widely available) solver" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup\n", "Here's some setup so we can easily work with probability distributions as arrays of probabilities, indexed by bitstring." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "n_bits = 4\n", "n_data_points = 10000\n", "\n", "class ProbabilityDistribution(np.ndarray):\n", " @classmethod\n", " def from_array(cls, ar):\n", " ret = cls(int(round(np.log2(len(ar)))))\n", " ret[:] = ar[:]\n", " return ret\n", "\n", " def __new__(cls, n_bits):\n", " self = super().__new__(cls, (2**n_bits,), float, np.zeros(2**n_bits, 'd'))\n", " self.n_bits = n_bits\n", " self.bs = [\"\".join(x) for x in itertools.product(*([('0', '1')] * n_bits))]\n", " self.bi = {s: i for i,s in enumerate(self.bs)}\n", " return self\n", " \n", " def __getitem__(self, key):\n", " if isinstance(key, str):\n", " return super().__getitem__(self.bi[key])\n", " else:\n", " return super().__getitem__(key)\n", " \n", " def __setitem__(self, key, val):\n", " if isinstance(key, str):\n", " return super().__setitem__(self.bi[key], val)\n", " else:\n", " return super().__setitem__(key, val)\n", " \n", " def display(self, my_name='', other_distributions=None):\n", " if other_distributions is None: other_distributions = {}\n", " other_names = list(other_distributions.keys())\n", " print(' ', ', '.join([my_name] + other_names))\n", " print('\\n'.join([\"%s: %s\" % (s, ' '.join([('%.3f' % x) for x in [v] + [other_distributions[nm][s] for nm in other_names]]))\n", " for s,v in zip(self.bs, self)]))\n", " \n", " def __str__(self):\n", " return '\\n'.join([\"%s: %g\" % (s, v) for s,v in zip(self.bs, self)]) + '\\n'\n", " \n", " def __array_finalize__(self, obj): # needed for creating new array instances\n", " if obj is None: return\n", " self.n_bits = getattr(obj, 'n_bits', None)\n", " self.bi = getattr(obj, 'bi', None)\n", " self.bs = getattr(obj, 'bs', None)\n", " \n", " def copy(self):\n", " ret = ProbabilityDistribution(self.n_bits)\n", " ret[:] = self[:]\n", " return ret" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The main demo: a simple example\n", "Let's start with an example problem, computing the weight-1 to weight-4 disturbances based on some data. This demonstrates the main interface to using the `disturbancecalc.py` code: the `compute_disturbances` function." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ideal, ref, test\n", "0000: 0.500 0.500 0.450\n", "0001: 0.000 0.000 0.025\n", "0010: 0.000 0.000 0.000\n", "0011: 0.000 0.000 0.000\n", "0100: 0.000 0.000 0.000\n", "0101: 0.000 0.000 0.000\n", "0110: 0.000 0.000 0.000\n", "0111: 0.000 0.000 0.000\n", "1000: 0.000 0.000 0.000\n", "1001: 0.000 0.000 0.000\n", "1010: 0.000 0.000 0.000\n", "1011: 0.000 0.000 0.000\n", "1100: 0.000 0.000 0.000\n", "1101: 0.000 0.000 0.000\n", "1110: 0.000 0.000 0.025\n", "1111: 0.500 0.500 0.500\n" ] } ], "source": [ "# Define the ideal distribution\n", "p_ideal = ProbabilityDistribution(n_bits)\n", "p_ideal['0000'] = .5\n", "p_ideal['1111'] = .5\n", "\n", "\n", "# Define the \"reference\" distribution. In this case, we just set it equal to the ideal distribution\n", "p_ref = p_ideal.copy()\n", "\n", "# Define several elementary errors that can happen to a single bit:\n", "ident1bit = np.identity(2) # do nothing to a single bit\n", "flip1bit = np.array([[0, 1],[1, 0]]) # flip a single bit\n", "set1bit = np.array([[0, 0], [1, 1]]) # set a single bit (to 1)\n", "reset1bit = np.array([[1, 1], [0, 0]]) # reset a single bit (to 0)\n", "\n", "# Define, for example, and for use later on, several 4-bit errors.\n", "def kron4(a, b, c, d):\n", " return np.kron(a, np.kron(b, np.kron(c, d)))\n", "ident4bit = np.identity(16)\n", "flip4bit = kron4(flip1bit, flip1bit, flip1bit, flip1bit)\n", "fliplastbit = kron4(ident1bit, ident1bit, ident1bit, flip1bit)\n", "setlastbit = np.kron(ident1bit, np.kron(ident1bit, np.kron(ident1bit, reset1bit)))\n", "set4bit = kron4(set1bit, set1bit, set1bit, set1bit)\n", "\n", "# Construct, for our example, a particular stochastic map that represents\n", "# the \"error\" or \"noise\" that occurs during the parity check experiment.\n", "parity_check_noise = 0.90 * ident4bit + 0.05 * set4bit + 0.05 * fliplastbit\n", "#parity_check_noise = 0.90 * ident4bit + 0.1 * fliplastbit # another possibility\n", "\n", "# The \"test\" distribution is thus defined by applying the above map to p_ref:\n", "p_test = ProbabilityDistribution.from_array(np.dot(parity_check_noise, p_ref))\n", "\n", "# We can now simulate data corresponding to these probability distributions.\n", "# Here we don't include proper finite sample error for simplicity, we just\n", "# round to the nearest integer:\n", "data_ref = np.array( p_ref * n_data_points, dtype='int') # no finite sample error\n", "data_test = np.array(p_test * n_data_points, dtype='int') # no finite sample error\n", "\n", "# This is what you would do to simulate finite sampling error:\n", "# data_ref = np.random.multinomial(n_data_points, p_ref)\n", "# data_test = np.random.multinomial(n_data_points, p_test)\n", "\n", "# Before computing the disturbances, let's take a look at the probability distributions:\n", "p_ideal.display('ideal', {'ref': p_ref, 'test': p_test})" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Computing base disturbances\n", "Analyzing bootstrap sample 1 of 20..." ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/enielse/.pyenv/versions/3.8.5/lib/python3.8/site-packages/cvxpy/problems/problem.py:1054: UserWarning: Solution may be inaccurate. Try another solver, adjusting the solver settings, or solve with verbose=True for more information.\n", " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " (0.5s)\n", "Analyzing bootstrap sample 2 of 20... (0.4s)\n", "Analyzing bootstrap sample 3 of 20... (0.4s)\n", "Analyzing bootstrap sample 4 of 20... (0.4s)\n", "Analyzing bootstrap sample 5 of 20... (0.5s)\n", "Analyzing bootstrap sample 6 of 20... (1.2s)\n", "Analyzing bootstrap sample 7 of 20... (0.5s)\n", "Analyzing bootstrap sample 8 of 20... (0.7s)\n", "Analyzing bootstrap sample 9 of 20... (0.5s)\n", "Analyzing bootstrap sample 10 of 20... (0.6s)\n", "Analyzing bootstrap sample 11 of 20... (0.5s)\n", "Analyzing bootstrap sample 12 of 20... (0.6s)\n", "Analyzing bootstrap sample 13 of 20... (0.5s)\n", "Analyzing bootstrap sample 14 of 20... (0.6s)\n", "Analyzing bootstrap sample 15 of 20... (0.5s)\n", "Analyzing bootstrap sample 16 of 20... (0.5s)\n", "Analyzing bootstrap sample 17 of 20... (0.5s)\n", "Analyzing bootstrap sample 18 of 20... (0.5s)\n", "Analyzing bootstrap sample 19 of 20... (0.4s)\n", "Analyzing bootstrap sample 20 of 20... (0.5s)\n" ] } ], "source": [ "# The actual disturbance computation\n", "disturbances = pb.compute_disturbances(n_bits, data_ref, data_test, solver=SOLVER)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Weight-1 disturbance = 0.025 +/- 0.00463123\n", "Weight-2 disturbance = -1.82279e-09 +/- 0.000378904\n", "Weight-3 disturbance = 0.0249996 +/- 0.00471013\n", "Weight-4 disturbance = 3.68479e-07 +/- 0.0045456\n" ] } ], "source": [ "# This shows how the returned `disturbances` list is interpreted (see the dostring too)\n", "for i,d in enumerate(disturbances):\n", " print(\"Weight-%d disturbance = %g +/- %g\" % (i+1,d[0],d[1]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**That's all there is to computing the disturbance metrics!** From this point on, this notebook demonstrates the more recent OVD-based corrections to the disturbances and then shows some of the other things the QPL has done to test and verify that the disturbance metrics are computed correctly and behave as they're supposed to.\n", "\n", "-----------------\n", "\n", "## OVD-corrected disturbances\n", "To make the disturbance metrics more robust to SPAM noise, you can apply a scaling factor that is the ratio of the original variation distance (OVD) to the usual total variational distance (TVD) between the reference and test distributions. The ratio can be computed independently, or, for convenience, methods exist which perform the scaling internally. Both are demonstrated below." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ideal, ref, test\n", "0000: 0.500 0.475 0.429\n", "0001: 0.000 0.025 0.046\n", "0010: 0.000 0.000 0.000\n", "0011: 0.000 0.000 0.000\n", "0100: 0.000 0.000 0.000\n", "0101: 0.000 0.000 0.000\n", "0110: 0.000 0.000 0.000\n", "0111: 0.000 0.000 0.000\n", "1000: 0.000 0.000 0.000\n", "1001: 0.000 0.000 0.000\n", "1010: 0.000 0.000 0.000\n", "1011: 0.000 0.000 0.000\n", "1100: 0.000 0.000 0.000\n", "1101: 0.000 0.000 0.000\n", "1110: 0.000 0.025 0.046\n", "1111: 0.500 0.475 0.479\n" ] } ], "source": [ "# Consider an example where the parity check circuit produces the same error\n", "# (`parity_check_noise` from above) but with SPAM noise, so that p_ref is no longer perfect.\n", "\n", "# We construct SPAM noise that is the tensor produce to a low-probability bit flip on the final qubit:\n", "lowprobflip1bit = 0.95 * ident1bit + 0.05 * flip1bit\n", "spam_noise = kron4(ident1bit, ident1bit, ident1bit, lowprobflip1bit)\n", " \n", "# The \"reference\" distribution is then defined by applying the above map to p_ideal:\n", "p_ref = ProbabilityDistribution.from_array(np.dot(spam_noise, p_ideal))\n", "\n", "# And the \"test\" distribution is defined by applying the same parity_check_noise from before to p_ref:\n", "p_test = ProbabilityDistribution.from_array(np.dot(parity_check_noise, p_ref))\n", "\n", "#We can then simulate data and view these distributions like we did before:\n", "data_ref = np.array( p_ref * n_data_points, dtype='int') # no finite sample error\n", "data_test = np.array(p_test * n_data_points, dtype='int') # no finite sample error\n", "\n", "# This is what you would do to simulate finite sampling error:\n", "# data_ref = np.random.multinomial(n_data_points, p_ref)\n", "# data_test = np.random.multinomial(n_data_points, p_test)\n", "\n", "p_ideal.display('ideal', {'ref': p_ref, 'test': p_test})" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OVD/TVD ratio = 1.0526315789473677\n" ] } ], "source": [ "# We next go on to compute the OVD/TVD ratio, the usual TVD-based disturbances, and the OVD-corrected disturbances.\n", "r = ovd_over_tvd = pb.compute_ovd_over_tvd_ratio(n_bits, data_ref, data_test, p_ideal)\n", "print(\"OVD/TVD ratio = \",r)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Computing base disturbances\n", "Analyzing bootstrap sample 1 of 20... (0.5s)\n", "Analyzing bootstrap sample 2 of 20... (0.5s)\n", "Analyzing bootstrap sample 3 of 20... (0.5s)\n", "Analyzing bootstrap sample 4 of 20... (0.5s)\n", "Analyzing bootstrap sample 5 of 20... (1.0s)\n", "Analyzing bootstrap sample 6 of 20... (0.6s)\n", "Analyzing bootstrap sample 7 of 20... (0.5s)\n", "Analyzing bootstrap sample 8 of 20... (0.6s)\n", "Analyzing bootstrap sample 9 of 20... (0.5s)\n", "Analyzing bootstrap sample 10 of 20... (0.5s)\n", "Analyzing bootstrap sample 11 of 20... (0.5s)\n", "Analyzing bootstrap sample 12 of 20... (0.5s)\n", "Analyzing bootstrap sample 13 of 20... (0.5s)\n", "Analyzing bootstrap sample 14 of 20... (0.5s)\n", "Analyzing bootstrap sample 15 of 20... (0.5s)\n", "Analyzing bootstrap sample 16 of 20... (0.5s)\n", "Analyzing bootstrap sample 17 of 20... (0.5s)\n", "Analyzing bootstrap sample 18 of 20... (0.5s)\n", "Analyzing bootstrap sample 19 of 20... (0.5s)\n", "Analyzing bootstrap sample 20 of 20... (0.5s)\n" ] } ], "source": [ "# Usual TVD-based disturbances\n", "disturbances = pb.compute_disturbances(n_bits, data_ref, data_test, solver=SOLVER)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Weight-1 disturbance = 0.0212092 +/- 0.00400825\n", "Weight-2 disturbance = -5.56941e-08 +/- 0.000393251\n", "Weight-3 disturbance = 0.0250039 +/- 0.00690518\n", "Weight-4 disturbance = 1.18704e-06 +/- 0.000803861\n" ] } ], "source": [ "# This shows how the returned `disturbances` list is interpreted (see the dostring too)\n", "for i,d in enumerate(disturbances):\n", " print(\"Weight-%d disturbance = %g +/- %g\" % (i+1,d[0],d[1]))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Computing base disturbances\n", "Analyzing bootstrap sample 1 of 20... (0.5s)\n", "Analyzing bootstrap sample 2 of 20... (0.5s)\n", "Analyzing bootstrap sample 3 of 20... (0.6s)\n", "Analyzing bootstrap sample 4 of 20... (0.5s)\n", "Analyzing bootstrap sample 5 of 20... (1.1s)\n", "Analyzing bootstrap sample 6 of 20... (0.5s)\n", "Analyzing bootstrap sample 7 of 20... (0.5s)\n", "Analyzing bootstrap sample 8 of 20... (0.5s)\n", "Analyzing bootstrap sample 9 of 20... (0.5s)\n", "Analyzing bootstrap sample 10 of 20... (0.5s)\n", "Analyzing bootstrap sample 11 of 20... (0.5s)\n", "Analyzing bootstrap sample 12 of 20... (0.5s)\n", "Analyzing bootstrap sample 13 of 20... (0.5s)\n", "Analyzing bootstrap sample 14 of 20... (0.5s)\n", "Analyzing bootstrap sample 15 of 20... (0.5s)\n", "Analyzing bootstrap sample 16 of 20... (0.5s)\n", "Analyzing bootstrap sample 17 of 20... (0.5s)\n", "Analyzing bootstrap sample 18 of 20... (0.5s)\n", "Analyzing bootstrap sample 19 of 20... (0.6s)\n", "Analyzing bootstrap sample 20 of 20... (0.5s)\n" ] } ], "source": [ "ovd_corrected_disturbances = pb.compute_ovd_corrected_disturbances(n_bits, data_ref, data_test, p_ideal, solver=SOLVER)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Weight-1 OVD-corrected disturbance = 0.0223255 +/- 0.00423156\n", "Weight-2 OVD-corrected disturbance = -5.86253e-08 +/- 0.000411076\n", "Weight-3 OVD-corrected disturbance = 0.0263199 +/- 0.00710352\n", "Weight-4 OVD-corrected disturbance = 1.24952e-06 +/- 0.000836451\n", "OVD/TVD ratio = 1.05263 +/- 0.0120014\n" ] } ], "source": [ "# This shows how the returned `disturbances` list is interpreted (see the dostring too)\n", "for i,d in enumerate(ovd_corrected_disturbances):\n", " if i < 4:\n", " print(\"Weight-%d OVD-corrected disturbance = %g +/- %g\" % (i+1,d[0],d[1]))\n", " else:\n", " print(\"OVD/TVD ratio = %g +/- %g\" % (d[0],d[1]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the above example, we note:\n", "\n", "1. the OVD/ratio is close to 1, and so the OVD-corrected vs uncorrected numbers are similar. More precisely, the ratio is ~1.05, and so the corrected values are slightly larger than the uncorrected values.\n", "\n", "2. the correction moves the disturbances closer to those the values given earlier when there was no SPAM error.\n", "\n", "-----\n", "\n", "### One more OVD-corrected disturbance example\n", "It's also possible to have SPAM noise *increase* the disturbances rather than reduce them. In the example below, which uses SPAM noise that afflicts all the qubits equally, the presence of the SPAM noise actually makes the disturbance values larger than they would be without the noise. In this case `r` is (slightly) less than 1, and the OVD-corrected disturbances are smaller than the uncorrected ones." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ideal, ref, test\n", "0000: 0.500 0.461 0.416\n", "0001: 0.000 0.009 0.032\n", "0010: 0.000 0.009 0.008\n", "0011: 0.000 0.000 0.001\n", "0100: 0.000 0.009 0.008\n", "0101: 0.000 0.000 0.001\n", "0110: 0.000 0.000 0.001\n", "0111: 0.000 0.009 0.008\n", "1000: 0.000 0.009 0.008\n", "1001: 0.000 0.000 0.001\n", "1010: 0.000 0.000 0.001\n", "1011: 0.000 0.009 0.008\n", "1100: 0.000 0.000 0.001\n", "1101: 0.000 0.009 0.008\n", "1110: 0.000 0.009 0.032\n", "1111: 0.500 0.461 0.466\n" ] } ], "source": [ "#All the steps here are similar to the example above, except the SPAM noise is different\n", "lowprobflip1bit = 0.98 * ident1bit + 0.02 * flip1bit\n", "spam_noise = kron4(lowprobflip1bit, lowprobflip1bit, lowprobflip1bit, lowprobflip1bit)\n", " \n", "p_ref = ProbabilityDistribution.from_array(np.dot(spam_noise, p_ideal))\n", "p_test = ProbabilityDistribution.from_array(np.dot(parity_check_noise, p_ref))\n", "\n", "data_ref = np.array( p_ref * n_data_points, dtype='int') # no finite sample error\n", "data_test = np.array(p_test * n_data_points, dtype='int') # no finite sample error\n", "\n", "p_ideal.display('ideal', {'ref': p_ref, 'test': p_test})" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OVD/TVD ratio = 0.9575080822905789\n", "Computing base disturbances\n", "Computing base disturbances\n", "\n", "Weight-1 disturbance = 0.0256046\n", "Weight-2 disturbance = 0.00328173\n", "Weight-3 disturbance = 0.0191035\n", "Weight-4 disturbance = 0.00365153\n", "\n", "Weight-1 OVD-corrected disturbance = 0.0245166\n", "Weight-2 OVD-corrected disturbance = 0.00314228\n", "Weight-3 OVD-corrected disturbance = 0.0182917\n", "Weight-4 OVD-corrected disturbance = 0.00349637\n", "OVD/TVD ratio = 0.957508\n" ] } ], "source": [ "r = ovd_over_tvd = pb.compute_ovd_over_tvd_ratio(n_bits, data_ref, data_test, p_ideal)\n", "print(\"OVD/TVD ratio = \",r)\n", "\n", "#We don't do any bootstrap samples so the calculation is faster, but there are no error bars\n", "disturbances = pb.compute_disturbances(n_bits, data_ref, data_test, solver=SOLVER, num_bootstrap_samples=0)\n", "ovd_corrected_disturbances = pb.compute_ovd_corrected_disturbances(n_bits, data_ref, data_test, p_ideal, solver=SOLVER, num_bootstrap_samples=0)\n", "print()\n", "\n", "for i,d in enumerate(disturbances):\n", " print(\"Weight-%d disturbance = %g\" % (i+1,d[0]))\n", " \n", "print()\n", " \n", "for i,d in enumerate(ovd_corrected_disturbances):\n", " if i < 4:\n", " print(\"Weight-%d OVD-corrected disturbance = %g\" % (i+1,d[0]))\n", " else:\n", " print(\"OVD/TVD ratio = %g\" % (d[0]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Testing and verification" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Test problems.\n", "Below we put what we did above in the \"main demo\" section into a function so it's easier to run, and perform a bunch of simple tests to ensure that the disturbance behavior agrees with our intuition." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "def gen_data(p_test_dict, p_ref_dict = None, include_finite_sample_error=False, seed=12345):\n", " # Define the reference distribution - in this case, it's equal to the target distribution\n", " p_ref = ProbabilityDistribution(n_bits)\n", " for k,v in p_ref_dict.items(): p_ref[k] = v\n", " \n", " if include_finite_sample_error:\n", " np.random.seed(seed)\n", " data_ref = np.random.multinomial(n_data_points, p_ref)\n", " else:\n", " data_ref = np.array( p_ref * n_data_points, dtype='int') # no finite sample error\n", " \n", " # Define the reference distribution - we get this by moving \n", " p_test = ProbabilityDistribution(n_bits)\n", " for k,v in p_test_dict.items(): p_test[k] = v\n", " \n", " if include_finite_sample_error:\n", " data_test = np.random.multinomial(n_data_points, p_test)\n", " else:\n", " data_test = np.array( p_test * n_data_points, dtype='int') # no finite sample error\n", " \n", " return data_ref, data_test\n", " \n", "\n", "def test(p_test_dict, p_ref_dict = None, include_finite_sample_error=False, seed=12345, confidence_percent=68):\n", " if p_ref_dict is None:\n", " p_ref_dict = {'0000': 0.5, '1111': 0.5}\n", " \n", " data_ref, data_test = gen_data(p_test_dict, p_ref_dict, include_finite_sample_error, seed)\n", " disturbances = pb.compute_disturbances(n_bits, data_ref, data_test, verbosity=0, solver=SOLVER)\n", " print(\"TEST (finite sampling = %s)\" % include_finite_sample_error)\n", " print(\"p_ref = \", p_ref_dict)\n", " print(\"p_test = \", p_test_dict)\n", " for i,d in enumerate(disturbances):\n", " if d[1] is None:\n", " print(\"Weight-%d disturbance = %g\" % (i+1,d[0]))\n", " else:\n", " print(\"Weight-%d disturbance = %g +/- %g\" % (i+1,d[0],d[1]))\n", " print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### No finite sample error\n", "The tests below have no sample error. In other words, we've just constructed fake \"data\" where the observed frequencies are equal to known probabilities for which we know what the disturbance metrics should be. So these tests are crafted to make it easy to interpret the outcome disturbances, and to make sure (for instance) that if we've constructed a pair of distributions that differ by a weight-2 error, we get a nonzero weight-2 disturbance (but approximately zero for the other disturbance metrics)." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TEST (finite sampling = False)\n", "p_ref = {'0000': 0.5, '1111': 0.5}\n", "p_test = {'0000': 0.45, '0001': 0.05, '1111': 0.5}\n", "Weight-1 disturbance = 0.05 +/- 0.00495419\n", "Weight-2 disturbance = 6.04827e-11 +/- 0.000394579\n", "Weight-3 disturbance = 0 +/- 0.00460903\n", "Weight-4 disturbance = 0 +/- 0.00481946\n", "\n", "TEST (finite sampling = False)\n", "p_ref = {'0000': 0.5, '1111': 0.5}\n", "p_test = {'0000': 0.45, '0011': 0.05, '1111': 0.5}\n", "Weight-1 disturbance = 0 +/- 0.000502418\n", "Weight-2 disturbance = 0.05 +/- 0.00189544\n", "Weight-3 disturbance = 3.37029e-09 +/- 0.000160759\n", "Weight-4 disturbance = 0 +/- 0.00401039\n", "\n", "TEST (finite sampling = False)\n", "p_ref = {'0000': 0.5, '1111': 0.5}\n", "p_test = {'0000': 0.45, '0111': 0.05, '1111': 0.5}\n", "Weight-1 disturbance = 6.93889e-18 +/- 0.00558415\n", "Weight-2 disturbance = -6.93889e-18 +/- 0.000411401\n", "Weight-3 disturbance = 0.0499999 +/- 0.0053436\n", "Weight-4 disturbance = 6.40582e-08 +/- 0.00422338\n", "\n", "TEST (finite sampling = False)\n", "p_ref = {'0000': 0.5, '1111': 0.5}\n", "p_test = {'0000': 0.45, '1111': 0.55}\n", "Weight-1 disturbance = 0 +/- 0.000301763\n", "Weight-2 disturbance = 0 +/- 0.000383953\n", "Weight-3 disturbance = 0 +/- 0.000321087\n", "Weight-4 disturbance = 0.05 +/- 0.0062984\n", "\n" ] } ], "source": [ "#Simple tests - no finite sample error\n", "test({'0000': 0.45, '0001': 0.05, '1111': 0.5}) # weight 1 movement \n", "test({'0000': 0.45, '0011': 0.05, '1111': 0.5}) # weight 2 movement \n", "test({'0000': 0.45, '0111': 0.05, '1111': 0.5}) # weight 3 movement\n", "test({'0000': 0.45, '1111': 0.55}) # weight 4 movement" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TEST (finite sampling = False)\n", "p_ref = {'0000': 0.5, '1111': 0.5}\n", "p_test = {'0000': 0.4, '0001': 0.05, '1111': 0.55}\n", "Weight-1 disturbance = 0.05 +/- 0.00242677\n", "Weight-2 disturbance = 0 +/- 0.00035457\n", "Weight-3 disturbance = 0 +/- 0.000332917\n", "Weight-4 disturbance = 0.05 +/- 0.00670465\n", "\n", "TEST (finite sampling = False)\n", "p_ref = {'0000': 0.5, '1111': 0.5}\n", "p_test = {'0000': 0.4, '0011': 0.05, '1111': 0.55}\n", "Weight-1 disturbance = 0 +/- 0.000313164\n", "Weight-2 disturbance = 0.0499999 +/- 0.00182238\n", "Weight-3 disturbance = 8.03188e-08 +/- 0.000408627\n", "Weight-4 disturbance = 0.05 +/- 0.00655976\n", "\n", "TEST (finite sampling = False)\n", "p_ref = {'0000': 0.5, '1111': 0.5}\n", "p_test = {'0000': 0.4, '0111': 0.05, '1111': 0.55}\n", "Weight-1 disturbance = 0 +/- 0.000258968\n", "Weight-2 disturbance = 0 +/- 0.000421522\n", "Weight-3 disturbance = 0.05 +/- 0.00197274\n", "Weight-4 disturbance = 0.05 +/- 0.00689537\n", "\n" ] } ], "source": [ "#More complex tests\n", "test({'0000': 0.40, '0001': 0.05, '1111': 0.55}) # weight 1+4 movement \n", "test({'0000': 0.40, '0011': 0.05, '1111': 0.55}) # weight 2+4 movement \n", "test({'0000': 0.40, '0111': 0.05, '1111': 0.55}) # weight 3+4 movement" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TEST (finite sampling = False)\n", "p_ref = {'0000': 0.5, '1111': 0.5}\n", "p_test = {'0000': 0.3, '0011': 0.1, '1111': 0.6}\n", "Weight-1 disturbance = 0 +/- 0.000322861\n", "Weight-2 disturbance = 0.1 +/- 0.00303067\n", "Weight-3 disturbance = -1.23933e-08 +/- 0.000320724\n", "Weight-4 disturbance = 0.1 +/- 0.00560226\n", "\n", "TEST (finite sampling = False)\n", "p_ref = {'0000': 0.5, '1111': 0.5}\n", "p_test = {'0000': 0.35, '0011': 0.08, '1111': 0.57}\n", "Weight-1 disturbance = 0 +/- 0.000316006\n", "Weight-2 disturbance = 0.080008 +/- 0.00262233\n", "Weight-3 disturbance = -1.86747e-07 +/- 0.00032341\n", "Weight-4 disturbance = 0.0699572 +/- 0.00610212\n", "\n", "TEST (finite sampling = False)\n", "p_ref = {'0000': 0.5, '1111': 0.5}\n", "p_test = {'0000': 0.4, '0011': 0.08, '1111': 0.52}\n", "Weight-1 disturbance = 0 +/- 0.000313116\n", "Weight-2 disturbance = 0.0799996 +/- 0.00216252\n", "Weight-3 disturbance = 3.74311e-07 +/- 0.000366607\n", "Weight-4 disturbance = 0.02 +/- 0.00644853\n", "\n" ] } ], "source": [ "#Different magnitudes of weight-2+4 movement\n", "test({'0000': 0.30, '0011': 0.1, '1111': 0.6})\n", "test({'0000': 0.35, '0011': 0.08, '1111': 0.57})\n", "test({'0000': 0.40, '0011': 0.08, '1111': 0.52})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### With finite sample error\n", "Now we add finite sample error. In other words, we start with *known* probability distributions (constructed by hand like the ones above), then simulate N=10^4 samples from those distributions, and analyze the resulting empirical frequency distributions. The point here is to (1) make sure that the estimated weight-2 disturbances are fairly close to the true ones, and (2) check that the error bar calculations correctly quantify the estimation error (i.e., provide valid confidence regions).\n", "\n", "We begin (next cell) by just making sure the results make sense." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TEST (finite sampling = True)\n", "p_ref = {'0000': 0.5, '1111': 0.5}\n", "p_test = {'0000': 0.45, '0001': 0.05, '1111': 0.5}\n", "Weight-1 disturbance = 0.0487 +/- 0.00488717\n", "Weight-2 disturbance = 2.13164e-08 +/- 0.000387132\n", "Weight-3 disturbance = 0.000699976 +/- 0.00471798\n", "Weight-4 disturbance = 4.84389e-08 +/- 0.00441123\n", "\n", "TEST (finite sampling = True)\n", "p_ref = {'0000': 0.5, '1111': 0.5}\n", "p_test = {'0000': 0.45, '0011': 0.05, '1111': 0.5}\n", "Weight-1 disturbance = 1.38778e-17 +/- 0.000499835\n", "Weight-2 disturbance = 0.0493999 +/- 0.00183174\n", "Weight-3 disturbance = 8.23359e-08 +/- 0.000134797\n", "Weight-4 disturbance = 0 +/- 0.00360386\n", "\n", "TEST (finite sampling = True)\n", "p_ref = {'0000': 0.5, '1111': 0.5}\n", "p_test = {'0000': 0.45, '0111': 0.05, '1111': 0.5}\n", "Weight-1 disturbance = 0.000698094 +/- 0.00559855\n", "Weight-2 disturbance = 1.86611e-06 +/- 0.000397603\n", "Weight-3 disturbance = 0.0486995 +/- 0.00529992\n", "Weight-4 disturbance = 5.38069e-07 +/- 0.00386414\n", "\n", "TEST (finite sampling = True)\n", "p_ref = {'0000': 0.5, '1111': 0.5}\n", "p_test = {'0000': 0.45, '1111': 0.55}\n", "Weight-1 disturbance = 0 +/- 0.00029923\n", "Weight-2 disturbance = 0 +/- 0.000385034\n", "Weight-3 disturbance = 0 +/- 0.000324079\n", "Weight-4 disturbance = 0.0487 +/- 0.00629815\n", "\n" ] } ], "source": [ "#Simple tests - *with* finite sample error\n", "test({'0000': 0.45, '0001': 0.05, '1111': 0.5}, include_finite_sample_error=True) # weight 1 movement \n", "test({'0000': 0.45, '0011': 0.05, '1111': 0.5}, include_finite_sample_error=True) # weight 2 movement \n", "test({'0000': 0.45, '0111': 0.05, '1111': 0.5}, include_finite_sample_error=True) # weight 3 movement\n", "test({'0000': 0.45, '1111': 0.55}, include_finite_sample_error=True) # weight 4 movement" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Check error bars\n", "Finally, we check that the error bars on the disturbances capture the \"true\" value of the disturbance an appropriate fraction of the time. The histograms at the end of each of the cells below should show that the mean deviation/errorbar-lengths is around 1.0 (it should be $\\chi^2_1$-distributed). **Note: these tests run a lot of calculations and can take a while to run.** (Increasing `num_checks` will give better estimates of the distribution but will take longer.)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "def test_errorbars(p_test_dict, p_ref_dict = None, num_checks=20, confidence_percent=68):\n", " if p_ref_dict is None:\n", " p_ref_dict = {'0000': 0.5, '1111': 0.5}\n", " \n", " data_ref, data_test = gen_data(p_test_dict, p_ref_dict, False)\n", " base_disturbances = pb.compute_disturbances(n_bits, data_ref, data_test, num_bootstrap_samples=0,\n", " verbosity=0, solver=SOLVER)\n", " for i,d in enumerate(base_disturbances):\n", " print(\"Base weight-%d disturbance = %g\" % (i+1,d[0]))\n", " \n", " deviation_over_ebs = {i: [] for i in range(4)}\n", " seeds = [ np.random.randint(1000000000) for i in range(num_checks) ]\n", " for i,s in enumerate(seeds):\n", " print(\"Check %d of %d\" % (i+1, num_checks))\n", " data_ref, data_test = gen_data(p_test_dict, p_ref_dict, True, seed=s)\n", " try:\n", " disturbances = pb.compute_disturbances(n_bits, data_ref, data_test, num_bootstrap_samples=10,\n", " verbosity=0, solver=SOLVER)\n", " except Exception as e:\n", " print(\"EXCEPTION: \", str(e))\n", " continue\n", "\n", " for i,(d,dbase) in enumerate(zip(disturbances, base_disturbances)):\n", " deviation_over_eb = abs((d[0] - dbase[0]) / (d[1] + 1e-6)) # add 1e-6 to avoid divide-by-zero errors\n", " deviation_over_ebs[i].append(deviation_over_eb)\n", " print(\"Weight-%d disturbance = %g +/- %g (deviation=%s)\" % (i+1,d[0],d[1],deviation_over_eb))\n", " \n", " print(\"Percentanges within %d%% confidence region:\" % confidence_percent)\n", " for i, pts in deviation_over_ebs.items():\n", " print(\"Weight %d: \" % (i+1), pts)\n", " plt.figure()\n", " plt.title(\"Weight %d ResidualTVD deviation distribution\" % (i+1))\n", " plt.xlabel(\"deviation / error_bar_length\")\n", " plt.ylabel(\"counts\")\n", " plt.hist(pts) \n", " print()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Base weight-1 disturbance = 0.05\n", "Base weight-2 disturbance = 6.04827e-11\n", "Base weight-3 disturbance = 0\n", "Base weight-4 disturbance = 0\n", "Check 1 of 20\n", "Weight-1 disturbance = 0.0494 +/- 0.00460002 (deviation=0.13040749998957704)\n", "Weight-2 disturbance = 7.6341e-09 +/- 0.000456376 (deviation=1.655883959978033e-05)\n", "Weight-3 disturbance = 0.000799984 +/- 0.00463315 (deviation=0.17262814538110377)\n", "Weight-4 disturbance = 1.56229e-08 +/- 0.00405963 (deviation=3.847421888800286e-06)\n", "Check 2 of 20\n", "Weight-1 disturbance = 0.0458 +/- 0.00138302 (deviation=3.034643466750717)\n", "Weight-2 disturbance = 5.62315e-09 +/- 0.000451567 (deviation=1.2291357451368336e-05)\n", "Weight-3 disturbance = -2.64545e-17 +/- 0.00139652 (deviation=1.8929638981497028e-14)\n", "Weight-4 disturbance = 0.0028 +/- 0.00679048 (deviation=0.4122810123143864)\n", "Check 3 of 20\n", "Weight-1 disturbance = 0.0463998 +/- 0.00520716 (deviation=0.6912557196715029)\n", "Weight-2 disturbance = 2.17257e-08 +/- 0.000483705 (deviation=4.469782196117712e-05)\n", "Weight-3 disturbance = 0.00359972 +/- 0.00503248 (deviation=0.7151551398557452)\n", "Weight-4 disturbance = 4.3058e-07 +/- 0.00275556 (deviation=0.00015620170156731812)\n", "Check 4 of 20\n", "Weight-1 disturbance = 0.0392 +/- 0.00630948 (deviation=1.711438568858322)\n", "Weight-2 disturbance = 7.60645e-10 +/- 0.000411212 (deviation=1.698548931299419e-06)\n", "Weight-3 disturbance = 0.0094996 +/- 0.00619116 (deviation=1.5341329993162802)\n", "Weight-4 disturbance = 3.97051e-07 +/- 0.000907713 (deviation=0.0004369379544307312)\n", "Check 5 of 20\n", "Weight-1 disturbance = 0.0491 +/- 0.00400089 (deviation=0.22489948793603173)\n", "Weight-2 disturbance = -1.33702e-07 +/- 0.000417098 (deviation=0.00031993188460831636)\n", "Weight-3 disturbance = 1.28012e-07 +/- 0.00406901 (deviation=3.1452644912367964e-05)\n", "Weight-4 disturbance = 0.000900029 +/- 0.00442147 (deviation=0.20351256664905235)\n", "Check 6 of 20\n", "Weight-1 disturbance = 0.0482 +/- 0.00479077 (deviation=0.3756442110481608)\n", "Weight-2 disturbance = 1.92187e-09 +/- 0.000438221 (deviation=4.237937857391526e-06)\n", "Weight-3 disturbance = 0.00499995 +/- 0.00440062 (deviation=1.1359328717951638)\n", "Weight-4 disturbance = 5.32516e-08 +/- 0.00329567 (deviation=1.6153146291952415e-05)\n", "Check 7 of 20\n", "Weight-1 disturbance = 0.0410996 +/- 0.00605107 (deviation=1.4706299437122539)\n", "Weight-2 disturbance = 3.50585e-07 +/- 0.000455917 (deviation=0.0007671528235064772)\n", "Weight-3 disturbance = 0.00769991 +/- 0.00585666 (deviation=1.3145017691407308)\n", "Weight-4 disturbance = 9.41379e-08 +/- 0.00130469 (deviation=7.209803865361063e-05)\n", "Check 8 of 20\n", "Weight-1 disturbance = 0.0437 +/- 0.00369769 (deviation=1.7033118048074751)\n", "Weight-2 disturbance = -7.12836e-09 +/- 0.000446259 (deviation=1.6073118227795782e-05)\n", "Weight-3 disturbance = 2.39058e-08 +/- 0.00382004 (deviation=6.256354525456113e-06)\n", "Weight-4 disturbance = 0.0014 +/- 0.00449275 (deviation=0.3115434906305298)\n", "Check 9 of 20\n", "Weight-1 disturbance = 0.049 +/- 0.00151733 (deviation=0.6586340043958501)\n", "Weight-2 disturbance = 2.25559e-08 +/- 0.000451989 (deviation=4.965998294268051e-05)\n", "Weight-3 disturbance = -4.17616e-09 +/- 0.000209756 (deviation=1.9815109886722774e-05)\n", "Weight-4 disturbance = 0.0172 +/- 0.00727269 (deviation=2.3646870966903855)\n", "Check 10 of 20\n", "Weight-1 disturbance = 0.051 +/- 0.00153494 (deviation=0.6510657351985843)\n", "Weight-2 disturbance = -6.49282e-08 +/- 0.000445419 (deviation=0.0001455779927869709)\n", "Weight-3 disturbance = 6.61187e-08 +/- 0.000244953 (deviation=0.00026882708498255655)\n", "Weight-4 disturbance = 0.0084 +/- 0.00726491 (deviation=1.1560839016000193)\n", "Check 11 of 20\n", "Weight-1 disturbance = 0.0478 +/- 0.00366742 (deviation=0.5997134029708048)\n", "Weight-2 disturbance = -2.04823e-07 +/- 0.000465137 (deviation=0.00043953378311990273)\n", "Weight-3 disturbance = -3.01867e-07 +/- 0.00377937 (deviation=7.985115200956247e-05)\n", "Weight-4 disturbance = 0.00150051 +/- 0.00444095 (deviation=0.33780404888116344)\n", "Check 12 of 20\n", "Weight-1 disturbance = 0.0505 +/- 0.00384426 (deviation=0.130029090095887)\n", "Weight-2 disturbance = -6.47883e-08 +/- 0.000438218 (deviation=0.0001476457863412457)\n", "Weight-3 disturbance = -1.98228e-08 +/- 0.00393113 (deviation=5.041241923229341e-06)\n", "Weight-4 disturbance = 0.00120009 +/- 0.0044351 (deviation=0.2705277790730263)\n", "Check 13 of 20\n", "Weight-1 disturbance = 0.0470999 +/- 0.00527421 (deviation=0.5497516180655774)\n", "Weight-2 disturbance = -5.6545e-08 +/- 0.000455973 (deviation=0.0001238704995554752)\n", "Weight-3 disturbance = 0.0039995 +/- 0.00508321 (deviation=0.786650419911193)\n", "Weight-4 disturbance = 6.12375e-07 +/- 0.002591 (deviation=0.0002362557262734456)\n", "Check 14 of 20\n", "Weight-1 disturbance = 0.0491 +/- 0.00151991 (deviation=0.5917537108130372)\n", "Weight-2 disturbance = -6.51713e-09 +/- 0.000417749 (deviation=1.5707761375916745e-05)\n", "Weight-3 disturbance = 8.06976e-09 +/- 0.000211464 (deviation=3.7981715807130665e-05)\n", "Weight-4 disturbance = 0.0075 +/- 0.0072104 (deviation=1.040019507550239)\n", "Check 15 of 20\n", "Weight-1 disturbance = 0.0396 +/- 0.00593066 (deviation=1.753310579785569)\n", "Weight-2 disturbance = 8.04692e-09 +/- 0.000439226 (deviation=1.814169758113655e-05)\n", "Weight-3 disturbance = 0.00689996 +/- 0.00571055 (deviation=1.2080715585897506)\n", "Weight-4 disturbance = 7.36639e-08 +/- 0.00151564 (deviation=4.857035400016532e-05)\n", "Check 16 of 20\n", "Weight-1 disturbance = 0.0515 +/- 0.00153019 (deviation=0.9796205525603602)\n", "Weight-2 disturbance = -7.012e-07 +/- 0.000465682 (deviation=0.0015026512882456682)\n", "Weight-3 disturbance = 7.17487e-07 +/- 0.000205005 (deviation=0.0034828684462739293)\n", "Weight-4 disturbance = 0.0089 +/- 0.0072665 (deviation=1.224629892609855)\n", "Check 17 of 20\n", "Weight-1 disturbance = 0.0498 +/- 0.00145146 (deviation=0.1376978334792446)\n", "Weight-2 disturbance = -1.90429e-08 +/- 0.00044312 (deviation=4.301395524442699e-05)\n", "Weight-3 disturbance = -6.2634e-07 +/- 0.000232764 (deviation=0.0026793638831068725)\n", "Weight-4 disturbance = 0.00670065 +/- 0.00713353 (deviation=0.9391849535197131)\n", "Check 18 of 20\n", "Weight-1 disturbance = 0.05 +/- 0.0013632 (deviation=3.762680271852993e-07)\n", "Weight-2 disturbance = -1.85191e-07 +/- 0.000466908 (deviation=0.00039591463856024535)\n", "Weight-3 disturbance = 3.14987e-08 +/- 0.000937694 (deviation=3.355586639363695e-05)\n", "Weight-4 disturbance = 0.00410015 +/- 0.00685753 (deviation=0.5978183257153782)\n", "Check 19 of 20\n", "Weight-1 disturbance = 0.0491 +/- 0.00452 (deviation=0.199071287721814)\n", "Weight-2 disturbance = -1.10186e-07 +/- 0.000466773 (deviation=0.00023568297034460035)\n", "Weight-3 disturbance = 0.000200053 +/- 0.00458175 (deviation=0.04365349680628607)\n", "Weight-4 disturbance = 5.75094e-08 +/- 0.00435963 (deviation=1.318831561575467e-05)\n", "Check 20 of 20\n", "Weight-1 disturbance = 0.0484 +/- 0.00261963 (deviation=0.6105394613122896)\n", "Weight-2 disturbance = -1.3648e-08 +/- 0.000448692 (deviation=3.048427891202695e-05)\n", "Weight-3 disturbance = 0.00129998 +/- 0.00273754 (deviation=0.47469993496888324)\n", "Weight-4 disturbance = 2.93917e-08 +/- 0.00582643 (deviation=5.043684836920456e-06)\n", "Percentanges within 68% confidence region:\n", "Weight 1: [0.13040749998957704, 3.034643466750717, 0.6912557196715029, 1.711438568858322, 0.22489948793603173, 0.3756442110481608, 1.4706299437122539, 1.7033118048074751, 0.6586340043958501, 0.6510657351985843, 0.5997134029708048, 0.130029090095887, 0.5497516180655774, 0.5917537108130372, 1.753310579785569, 0.9796205525603602, 0.1376978334792446, 3.762680271852993e-07, 0.199071287721814, 0.6105394613122896]\n", "Weight 2: [1.655883959978033e-05, 1.2291357451368336e-05, 4.469782196117712e-05, 1.698548931299419e-06, 0.00031993188460831636, 4.237937857391526e-06, 0.0007671528235064772, 1.6073118227795782e-05, 4.965998294268051e-05, 0.0001455779927869709, 0.00043953378311990273, 0.0001476457863412457, 0.0001238704995554752, 1.5707761375916745e-05, 1.814169758113655e-05, 0.0015026512882456682, 4.301395524442699e-05, 0.00039591463856024535, 0.00023568297034460035, 3.048427891202695e-05]\n", "Weight 3: [0.17262814538110377, 1.8929638981497028e-14, 0.7151551398557452, 1.5341329993162802, 3.1452644912367964e-05, 1.1359328717951638, 1.3145017691407308, 6.256354525456113e-06, 1.9815109886722774e-05, 0.00026882708498255655, 7.985115200956247e-05, 5.041241923229341e-06, 0.786650419911193, 3.7981715807130665e-05, 1.2080715585897506, 0.0034828684462739293, 0.0026793638831068725, 3.355586639363695e-05, 0.04365349680628607, 0.47469993496888324]\n", "Weight 4: [3.847421888800286e-06, 0.4122810123143864, 0.00015620170156731812, 0.0004369379544307312, 0.20351256664905235, 1.6153146291952415e-05, 7.209803865361063e-05, 0.3115434906305298, 2.3646870966903855, 1.1560839016000193, 0.33780404888116344, 0.2705277790730263, 0.0002362557262734456, 1.040019507550239, 4.857035400016532e-05, 1.224629892609855, 0.9391849535197131, 0.5978183257153782, 1.318831561575467e-05, 5.043684836920456e-06]\n", "\n" ] }, { "data": { "image/png": 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\n", 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "test_errorbars({'0000': 0.45, '0001': 0.05, '1111': 0.5}) # weight 1 movement " ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Base weight-1 disturbance = 0\n", "Base weight-2 disturbance = 0.05\n", "Base weight-3 disturbance = 3.37029e-09\n", "Base weight-4 disturbance = 0\n", "Check 1 of 20\n", "Weight-1 disturbance = 2.08167e-17 +/- 0.000320226 (deviation=6.48037959331721e-14)\n", "Weight-2 disturbance = 0.0501997 +/- 0.00186102 (deviation=0.10723393001260678)\n", "Weight-3 disturbance = 3.31702e-07 +/- 9.26582e-05 (deviation=0.0035056417084368204)\n", "Weight-4 disturbance = 0 +/- 0.00130098 (deviation=0.0)\n", "Check 2 of 20\n", "Weight-1 disturbance = 0 +/- 0.00032646 (deviation=0.0)\n", "Weight-2 disturbance = 0.0518 +/- 0.0021478 (deviation=0.8376683990201109)\n", "Weight-3 disturbance = 2.12999e-08 +/- 0.000127726 (deviation=0.00013928557711311918)\n", "Weight-4 disturbance = 0 +/- 0.0035868 (deviation=0.0)\n", "Check 3 of 20\n", "Weight-1 disturbance = 0 +/- 0.000292508 (deviation=0.0)\n", "Weight-2 disturbance = 0.0490996 +/- 0.00207274 (deviation=0.43418023406088485)\n", "Weight-3 disturbance = 3.81996e-07 +/- 0.000128251 (deviation=0.002929390154444396)\n", "Weight-4 disturbance = 0 +/- 0.0034272 (deviation=0.0)\n", "Check 4 of 20\n", "Weight-1 disturbance = 0 +/- 0.000169689 (deviation=0.0)\n", "Weight-2 disturbance = 0.0513991 +/- 0.00202709 (deviation=0.68986994068817)\n", "Weight-3 disturbance = 8.83218e-07 +/- 0.000244732 (deviation=0.0035805186522389915)\n", "Weight-4 disturbance = 0.0046 +/- 0.00757232 (deviation=0.6073955869545151)\n", "Check 5 of 20\n", "Weight-1 disturbance = 0 +/- 0.00032037 (deviation=0.0)\n", "Weight-2 disturbance = 0.0485999 +/- 0.00186878 (deviation=0.748798227837998)\n", "Weight-3 disturbance = 9.28049e-08 +/- 9.29751e-05 (deviation=0.0009516838963617885)\n", "Weight-4 disturbance = 0 +/- 0.00234772 (deviation=0.0)\n", "Check 6 of 20\n", "Weight-1 disturbance = 1.38778e-17 +/- 0.000320292 (deviation=4.3193639925188406e-14)\n", "Weight-2 disturbance = 0.0500997 +/- 0.00185449 (deviation=0.05374304319184878)\n", "Weight-3 disturbance = 2.83644e-07 +/- 2.0211e-05 (deviation=0.01321364954710524)\n", "Weight-4 disturbance = 0 +/- 0.000580414 (deviation=0.0)\n", "Check 7 of 20\n", "Weight-1 disturbance = 0 +/- 0.000154358 (deviation=0.0)\n", "Weight-2 disturbance = 0.0508999 +/- 0.00197497 (deviation=0.4554288344616766)\n", "Weight-3 disturbance = 8.8431e-08 +/- 0.000206231 (deviation=0.0004104628689849586)\n", "Weight-4 disturbance = 0.0041 +/- 0.00755506 (deviation=0.5426113066627822)\n", "Check 8 of 20\n", "Weight-1 disturbance = 0 +/- 0.000189332 (deviation=0.0)\n", "Weight-2 disturbance = 0.0526999 +/- 0.00216734 (deviation=1.2451642547686368)\n", "Weight-3 disturbance = -3.21197e-08 +/- 0.00017192 (deviation=0.000205239637224678)\n", "Weight-4 disturbance = 0.0072001 +/- 0.00621504 (deviation=1.1583097752696956)\n", "Check 9 of 20\n", "Weight-1 disturbance = 0 +/- 0.000154271 (deviation=0.0)\n", "Weight-2 disturbance = 0.0468997 +/- 0.00187521 (deviation=1.6524227561140488)\n", "Weight-3 disturbance = 2.96834e-08 +/- 0.000301676 (deviation=8.693521933442388e-05)\n", "Weight-4 disturbance = 0.0148003 +/- 0.00773558 (deviation=1.9130239674864657)\n", "Check 10 of 20\n", "Weight-1 disturbance = 0 +/- 0.000287494 (deviation=0.0)\n", "Weight-2 disturbance = 0.0502981 +/- 0.00203768 (deviation=0.1462356457145821)\n", "Weight-3 disturbance = 1.87535e-06 +/- 0.00012714 (deviation=0.014608793365297608)\n", "Weight-4 disturbance = 0 +/- 0.0050075 (deviation=0.0)\n", "Check 11 of 20\n", "Weight-1 disturbance = 6.93889e-18 +/- 0.000330877 (deviation=2.0908052497572605e-14)\n", "Weight-2 disturbance = 0.0512999 +/- 0.00199652 (deviation=0.6507797438281614)\n", "Weight-3 disturbance = 5.7407e-08 +/- 7.15794e-05 (deviation=0.0007445184141495582)\n", "Weight-4 disturbance = 0 +/- 0.00410364 (deviation=0.0)\n", "Check 12 of 20\n", "Weight-1 disturbance = 6.93889e-18 +/- 0.000292524 (deviation=2.3639955192213346e-14)\n", "Weight-2 disturbance = 0.0504 +/- 0.00204873 (deviation=0.1951397505805794)\n", "Weight-3 disturbance = 1.86378e-08 +/- 0.000127964 (deviation=0.0001183861386504354)\n", "Weight-4 disturbance = 0 +/- 0.00374556 (deviation=0.0)\n", "Check 13 of 20\n", "Weight-1 disturbance = 0 +/- 0.000380006 (deviation=0.0)\n", "Weight-2 disturbance = 0.0514999 +/- 0.00203012 (deviation=0.7384725697895684)\n", "Weight-3 disturbance = 7.5839e-08 +/- 1.44415e-05 (deviation=0.004693108147991298)\n", "Weight-4 disturbance = 0 +/- 0.000647869 (deviation=0.0)\n", "Check 14 of 20\n", "Weight-1 disturbance = 0 +/- 0.000260267 (deviation=0.0)\n", "Weight-2 disturbance = 0.0502 +/- 0.00206421 (deviation=0.09683471532737165)\n", "Weight-3 disturbance = 3.84514e-09 +/- 0.000136579 (deviation=3.4515300156166684e-06)\n", "Weight-4 disturbance = 0.00480002 +/- 0.00595947 (deviation=0.8053086482275309)\n", "Check 15 of 20\n", "Weight-1 disturbance = 4.85723e-17 +/- 0.000332456 (deviation=1.4566334150156198e-13)\n", "Weight-2 disturbance = 0.0508991 +/- 0.00197841 (deviation=0.4542454811212361)\n", "Weight-3 disturbance = 8.64566e-07 +/- 1.41023e-05 (deviation=0.057024011364594174)\n", "Weight-4 disturbance = 0 +/- 0.000647268 (deviation=0.0)\n", "Check 16 of 20\n", "Weight-1 disturbance = 2.77556e-17 +/- 0.000358271 (deviation=7.725524425319541e-14)\n", "Weight-2 disturbance = 0.0524 +/- 0.00201019 (deviation=1.1933056806059015)\n", "Weight-3 disturbance = 3.64353e-08 +/- 1.45614e-05 (deviation=0.002124805283091678)\n", "Weight-4 disturbance = 0 +/- 0.000647842 (deviation=0.0)\n", "Check 17 of 20\n", "Weight-1 disturbance = 0 +/- 0.000292737 (deviation=0.0)\n", "Weight-2 disturbance = 0.0509999 +/- 0.00211602 (deviation=0.472329503596532)\n", "Weight-3 disturbance = 7.29379e-08 +/- 0.000144074 (deviation=0.0004795323091813578)\n", "Weight-4 disturbance = 0 +/- 0.00354282 (deviation=0.0)\n", "Check 18 of 20\n", "Weight-1 disturbance = 6.93889e-18 +/- 0.000292655 (deviation=2.362943609187102e-14)\n", "Weight-2 disturbance = 0.0508994 +/- 0.00205872 (deviation=0.43666443355014845)\n", "Weight-3 disturbance = 5.94748e-07 +/- 9.28559e-05 (deviation=0.006300903570748281)\n", "Weight-4 disturbance = 0 +/- 0.0030315 (deviation=0.0)\n", "Check 19 of 20\n", "Weight-1 disturbance = 2.77556e-17 +/- 0.000320965 (deviation=8.620671012204619e-14)\n", "Weight-2 disturbance = 0.0511999 +/- 0.00205324 (deviation=0.5841302285150249)\n", "Weight-3 disturbance = 5.91564e-08 +/- 0.000127904 (deviation=0.00043277315884531007)\n", "Weight-4 disturbance = 0 +/- 0.00490614 (deviation=0.0)\n", "Check 20 of 20\n", "Weight-1 disturbance = 0 +/- 0.000331929 (deviation=0.0)\n", "Weight-2 disturbance = 0.043 +/- 0.00180785 (deviation=3.8698825657591676)\n", "Weight-3 disturbance = 2.95117e-08 +/- 9.33766e-05 (deviation=0.000276990626300082)\n", "Weight-4 disturbance = 0 +/- 0.00236553 (deviation=0.0)\n", "Percentanges within 68% confidence region:\n", "Weight 1: [6.48037959331721e-14, 0.0, 0.0, 0.0, 0.0, 4.3193639925188406e-14, 0.0, 0.0, 0.0, 0.0, 2.0908052497572605e-14, 2.3639955192213346e-14, 0.0, 0.0, 1.4566334150156198e-13, 7.725524425319541e-14, 0.0, 2.362943609187102e-14, 8.620671012204619e-14, 0.0]\n", "Weight 2: [0.10723393001260678, 0.8376683990201109, 0.43418023406088485, 0.68986994068817, 0.748798227837998, 0.05374304319184878, 0.4554288344616766, 1.2451642547686368, 1.6524227561140488, 0.1462356457145821, 0.6507797438281614, 0.1951397505805794, 0.7384725697895684, 0.09683471532737165, 0.4542454811212361, 1.1933056806059015, 0.472329503596532, 0.43666443355014845, 0.5841302285150249, 3.8698825657591676]\n", "Weight 3: [0.0035056417084368204, 0.00013928557711311918, 0.002929390154444396, 0.0035805186522389915, 0.0009516838963617885, 0.01321364954710524, 0.0004104628689849586, 0.000205239637224678, 8.693521933442388e-05, 0.014608793365297608, 0.0007445184141495582, 0.0001183861386504354, 0.004693108147991298, 3.4515300156166684e-06, 0.057024011364594174, 0.002124805283091678, 0.0004795323091813578, 0.006300903570748281, 0.00043277315884531007, 0.000276990626300082]\n", "Weight 4: [0.0, 0.0, 0.0, 0.6073955869545151, 0.0, 0.0, 0.5426113066627822, 1.1583097752696956, 1.9130239674864657, 0.0, 0.0, 0.0, 0.0, 0.8053086482275309, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n", "\n" ] }, { "data": { "image/png": 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\n", 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "test_errorbars({'0000': 0.45, '0011': 0.05, '1111': 0.5}) # weight 2 movement " ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Base weight-1 disturbance = 6.93889e-18\n", "Base weight-2 disturbance = -6.93889e-18\n", "Base weight-3 disturbance = 0.0499999\n", "Base weight-4 disturbance = 6.40582e-08\n", "Check 1 of 20\n", "Weight-1 disturbance = 0.0107 +/- 0.00735355 (deviation=1.454881238330978)\n", "Weight-2 disturbance = -2.63725e-09 +/- 0.000296416 (deviation=8.867232567089432e-06)\n", "Weight-3 disturbance = 0.0395 +/- 0.00666818 (deviation=1.5744026062431984)\n", "Weight-4 disturbance = 4.75707e-08 +/- 0.000755664 (deviation=2.178970913195027e-05)\n", "Check 2 of 20\n", "Weight-1 disturbance = 0.0037 +/- 0.00562772 (deviation=0.6573434556666093)\n", "Weight-2 disturbance = -4.42276e-07 +/- 0.000307611 (deviation=0.001433119022133044)\n", "Weight-3 disturbance = 0.0481001 +/- 0.00512665 (deviation=0.3705071575464912)\n", "Weight-4 disturbance = 3.37135e-07 +/- 0.0035827 (deviation=7.619955664119643e-05)\n", "Check 3 of 20\n", "Weight-1 disturbance = 0.004 +/- 0.00563871 (deviation=0.7092557790160376)\n", "Weight-2 disturbance = -3.25228e-08 +/- 0.000276748 (deviation=0.00011709444652051578)\n", "Weight-3 disturbance = 0.0450998 +/- 0.00518115 (deviation=0.9455743967476032)\n", "Weight-4 disturbance = 2.01269e-07 +/- 0.00338706 (deviation=4.049822637547276e-05)\n", "Check 4 of 20\n", "Weight-1 disturbance = 1.38778e-17 +/- 0.000890745 (deviation=7.781249194827357e-15)\n", "Weight-2 disturbance = -1.38778e-17 +/- 0.000285596 (deviation=2.421143972593007e-14)\n", "Weight-3 disturbance = 0.0513998 +/- 0.00302445 (deviation=0.4626835347068984)\n", "Weight-4 disturbance = 0.00460024 +/- 0.00617985 (deviation=0.7442623519064119)\n", "Check 5 of 20\n", "Weight-1 disturbance = 0.0072 +/- 0.00648459 (deviation=1.1101533795093905)\n", "Weight-2 disturbance = -2.27903e-07 +/- 0.000325502 (deviation=0.0006980145427165365)\n", "Weight-3 disturbance = 0.0414001 +/- 0.00596273 (deviation=1.4420178335939444)\n", "Weight-4 disturbance = 1.02746e-07 +/- 0.00186126 (deviation=2.0774927855684054e-05)\n", "Check 6 of 20\n", "Weight-1 disturbance = 0.0143 +/- 0.00747813 (deviation=1.9119880552459025)\n", "Weight-2 disturbance = 1.15014e-10 +/- 0.000310339 (deviation=3.694165013705633e-07)\n", "Weight-3 disturbance = 0.0357998 +/- 0.00673399 (deviation=2.108406861423403)\n", "Weight-4 disturbance = 1.54797e-07 +/- 0.000741027 (deviation=0.00012228447492531905)\n", "Check 7 of 20\n", "Weight-1 disturbance = 0 +/- 0.00104302 (deviation=6.64629879376936e-15)\n", "Weight-2 disturbance = 0 +/- 0.000268972 (deviation=2.5702256042809192e-14)\n", "Weight-3 disturbance = 0.0508996 +/- 0.00304178 (deviation=0.2956564833181069)\n", "Weight-4 disturbance = 0.00410045 +/- 0.00614243 (deviation=0.6674419554768625)\n", "Check 8 of 20\n", "Weight-1 disturbance = 0 +/- 0.00206695 (deviation=3.355447781054858e-15)\n", "Weight-2 disturbance = 0 +/- 0.000280079 (deviation=2.4686665449615382e-14)\n", "Weight-3 disturbance = 0.0527 +/- 0.00322692 (deviation=0.8364703003195016)\n", "Weight-4 disturbance = 0.0072 +/- 0.00651895 (deviation=1.1042931567171468)\n", "Check 9 of 20\n", "Weight-1 disturbance = 1.38778e-17 +/- 0.00027688 (deviation=2.497085786409489e-14)\n", "Weight-2 disturbance = -1.38778e-17 +/- 0.000282444 (deviation=2.4480618221926022e-14)\n", "Weight-3 disturbance = 0.0469 +/- 0.00282885 (deviation=1.0954430024073278)\n", "Weight-4 disturbance = 0.0148 +/- 0.00646437 (deviation=2.28910940161106)\n", "Check 10 of 20\n", "Weight-1 disturbance = 0.0006 +/- 0.00524845 (deviation=0.11429769894048605)\n", "Weight-2 disturbance = 6.13371e-11 +/- 0.000295207 (deviation=2.0707503531370385e-07)\n", "Weight-3 disturbance = 0.0496996 +/- 0.00486583 (deviation=0.06171302817649418)\n", "Weight-4 disturbance = 4.10634e-07 +/- 0.00518835 (deviation=6.678595495834595e-05)\n", "Check 11 of 20\n", "Weight-1 disturbance = 0.0052 +/- 0.00445506 (deviation=1.1669508086394962)\n", "Weight-2 disturbance = -2.31019e-08 +/- 0.000304199 (deviation=7.569464752946071e-05)\n", "Weight-3 disturbance = 0.0461 +/- 0.00603516 (deviation=0.6460963895518306)\n", "Weight-4 disturbance = 3.11564e-08 +/- 0.00253555 (deviation=1.297112487149386e-05)\n", "Check 12 of 20\n", "Weight-1 disturbance = 0.0033 +/- 0.00549937 (deviation=0.5999595201923575)\n", "Weight-2 disturbance = -4.71161e-08 +/- 0.000295396 (deviation=0.00015896340588380405)\n", "Weight-3 disturbance = 0.0470995 +/- 0.00507098 (deviation=0.571855715987774)\n", "Weight-4 disturbance = 5.54578e-07 +/- 0.00376842 (deviation=0.00013013134362142417)\n", "Check 13 of 20\n", "Weight-1 disturbance = 0.0158 +/- 0.00753053 (deviation=2.0978467834104877)\n", "Weight-2 disturbance = -8.05238e-08 +/- 0.000257282 (deviation=0.0003117671136982293)\n", "Weight-3 disturbance = 0.0356999 +/- 0.00674715 (deviation=2.1191091295571263)\n", "Weight-4 disturbance = 2.08135e-07 +/- 0.000740461 (deviation=0.0001943145857456431)\n", "Check 14 of 20\n", "Weight-1 disturbance = 0 +/- 0.00282355 (deviation=2.4566406668017653e-15)\n", "Weight-2 disturbance = 0 +/- 0.000261132 (deviation=2.647100980784843e-14)\n", "Weight-3 disturbance = 0.0502 +/- 0.00342669 (deviation=0.05835373857791067)\n", "Weight-4 disturbance = 0.00480005 +/- 0.00609452 (deviation=0.7874609276683512)\n", "Check 15 of 20\n", "Weight-1 disturbance = 0.0145 +/- 0.00742954 (deviation=1.9514067045663785)\n", "Weight-2 disturbance = -3.29946e-10 +/- 0.000310158 (deviation=1.0603801471510928e-06)\n", "Weight-3 disturbance = 0.0363998 +/- 0.00693423 (deviation=1.9610144070025162)\n", "Weight-4 disturbance = 1.50646e-07 +/- 0.000740431 (deviation=0.00011678510871164528)\n", "Check 16 of 20\n", "Weight-1 disturbance = 0.0146 +/- 0.00753976 (deviation=1.9361448038487765)\n", "Weight-2 disturbance = 1.38778e-17 +/- 0.000312821 (deviation=6.633303683597947e-14)\n", "Weight-3 disturbance = 0.0377999 +/- 0.0067226 (deviation=1.8145120169296816)\n", "Weight-4 disturbance = 1.21068e-07 +/- 0.000740531 (deviation=7.688112907243159e-05)\n", "Check 17 of 20\n", "Weight-1 disturbance = 0.0038 +/- 0.00562518 (deviation=0.6754143141013569)\n", "Weight-2 disturbance = 5.41529e-10 +/- 0.000298333 (deviation=1.8091205867831591e-06)\n", "Weight-3 disturbance = 0.0471995 +/- 0.00513949 (deviation=0.5447710692745095)\n", "Weight-4 disturbance = 4.54534e-07 +/- 0.00353124 (deviation=0.00011054618505305945)\n", "Check 18 of 20\n", "Weight-1 disturbance = 0.00509818 +/- 0.00589071 (deviation=0.8653133465883416)\n", "Weight-2 disturbance = 1.58135e-06 +/- 0.000304676 (deviation=0.0051732862618611266)\n", "Weight-3 disturbance = 0.0457997 +/- 0.00540828 (deviation=0.776488423711704)\n", "Weight-4 disturbance = 5.50161e-07 +/- 0.00287436 (deviation=0.00016905825958112104)\n", "Check 19 of 20\n", "Weight-1 disturbance = 0.0008 +/- 0.00527499 (deviation=0.1516304117892074)\n", "Weight-2 disturbance = -1.53444e-10 +/- 0.000289416 (deviation=5.283595940316372e-07)\n", "Weight-3 disturbance = 0.0503993 +/- 0.00487028 (deviation=0.0819911338575766)\n", "Weight-4 disturbance = 6.62272e-07 +/- 0.00510228 (deviation=0.00011722133137957083)\n", "Check 20 of 20\n", "Weight-1 disturbance = 0.007 +/- 0.00628485 (deviation=1.1136128520475674)\n", "Weight-2 disturbance = -1.23083e-10 +/- 0.00027245 (deviation=4.501106154744604e-07)\n", "Weight-3 disturbance = 0.0359999 +/- 0.00619172 (deviation=2.260730393447211)\n", "Weight-4 disturbance = 1.2943e-07 +/- 0.00191162 (deviation=3.417898792828288e-05)\n", "Percentanges within 68% confidence region:\n", "Weight 1: [1.454881238330978, 0.6573434556666093, 0.7092557790160376, 7.781249194827357e-15, 1.1101533795093905, 1.9119880552459025, 6.64629879376936e-15, 3.355447781054858e-15, 2.497085786409489e-14, 0.11429769894048605, 1.1669508086394962, 0.5999595201923575, 2.0978467834104877, 2.4566406668017653e-15, 1.9514067045663785, 1.9361448038487765, 0.6754143141013569, 0.8653133465883416, 0.1516304117892074, 1.1136128520475674]\n", "Weight 2: [8.867232567089432e-06, 0.001433119022133044, 0.00011709444652051578, 2.421143972593007e-14, 0.0006980145427165365, 3.694165013705633e-07, 2.5702256042809192e-14, 2.4686665449615382e-14, 2.4480618221926022e-14, 2.0707503531370385e-07, 7.569464752946071e-05, 0.00015896340588380405, 0.0003117671136982293, 2.647100980784843e-14, 1.0603801471510928e-06, 6.633303683597947e-14, 1.8091205867831591e-06, 0.0051732862618611266, 5.283595940316372e-07, 4.501106154744604e-07]\n", "Weight 3: [1.5744026062431984, 0.3705071575464912, 0.9455743967476032, 0.4626835347068984, 1.4420178335939444, 2.108406861423403, 0.2956564833181069, 0.8364703003195016, 1.0954430024073278, 0.06171302817649418, 0.6460963895518306, 0.571855715987774, 2.1191091295571263, 0.05835373857791067, 1.9610144070025162, 1.8145120169296816, 0.5447710692745095, 0.776488423711704, 0.0819911338575766, 2.260730393447211]\n", "Weight 4: [2.178970913195027e-05, 7.619955664119643e-05, 4.049822637547276e-05, 0.7442623519064119, 2.0774927855684054e-05, 0.00012228447492531905, 0.6674419554768625, 1.1042931567171468, 2.28910940161106, 6.678595495834595e-05, 1.297112487149386e-05, 0.00013013134362142417, 0.0001943145857456431, 0.7874609276683512, 0.00011678510871164528, 7.688112907243159e-05, 0.00011054618505305945, 0.00016905825958112104, 0.00011722133137957083, 3.417898792828288e-05]\n", "\n" ] }, { "data": { "image/png": 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "test_errorbars({'0000': 0.45, '0111': 0.05, '1111': 0.5}) # weight 3 movement " ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Base weight-1 disturbance = 0\n", "Base weight-2 disturbance = 0\n", "Base weight-3 disturbance = 0\n", "Base weight-4 disturbance = 0.05\n", "Check 1 of 20\n", "Weight-1 disturbance = 0 +/- 0.000223133 (deviation=0.0)\n", "Weight-2 disturbance = 0 +/- 0.00027576 (deviation=0.0)\n", "Weight-3 disturbance = 0 +/- 0.000312568 (deviation=0.0)\n", "Weight-4 disturbance = 0.0395 +/- 0.00661217 (deviation=1.5877420124959105)\n", "Check 2 of 20\n", "Weight-1 disturbance = 0 +/- 0.000223139 (deviation=0.0)\n", "Weight-2 disturbance = 0 +/- 0.000275264 (deviation=0.0)\n", "Weight-3 disturbance = 0 +/- 0.000315445 (deviation=0.0)\n", "Weight-4 disturbance = 0.0481 +/- 0.00661172 (deviation=0.28732501691130974)\n", "Check 3 of 20\n", "Weight-1 disturbance = 0 +/- 0.000222312 (deviation=0.0)\n", "Weight-2 disturbance = 0 +/- 0.000275191 (deviation=0.0)\n", "Weight-3 disturbance = 0 +/- 0.000315324 (deviation=0.0)\n", "Weight-4 disturbance = 0.0451 +/- 0.00661149 (deviation=0.7410214778902107)\n", "Check 4 of 20\n", "Weight-1 disturbance = 0 +/- 0.000223168 (deviation=0.0)\n", "Weight-2 disturbance = 0 +/- 0.000273215 (deviation=0.0)\n", "Weight-3 disturbance = 0 +/- 0.000338002 (deviation=0.0)\n", "Weight-4 disturbance = 0.056 +/- 0.00773358 (deviation=0.7757368190117534)\n", "Check 5 of 20\n", "Weight-1 disturbance = 0 +/- 0.000223199 (deviation=0.0)\n", "Weight-2 disturbance = 0 +/- 0.000246704 (deviation=0.0)\n", "Weight-3 disturbance = 0 +/- 0.000326236 (deviation=0.0)\n", "Weight-4 disturbance = 0.0414 +/- 0.00661187 (deviation=1.3004947893168723)\n", "Check 6 of 20\n", "Weight-1 disturbance = 0 +/- 0.000223244 (deviation=0.0)\n", "Weight-2 disturbance = 0 +/- 0.000275624 (deviation=0.0)\n", "Weight-3 disturbance = 0 +/- 0.000312728 (deviation=0.0)\n", "Weight-4 disturbance = 0.0358 +/- 0.0066118 (deviation=2.1473511479132927)\n", "Check 7 of 20\n", "Weight-1 disturbance = 0 +/- 0.000222007 (deviation=0.0)\n", "Weight-2 disturbance = 0 +/- 0.000274695 (deviation=0.0)\n", "Weight-3 disturbance = 0 +/- 0.00033473 (deviation=0.0)\n", "Weight-4 disturbance = 0.055 +/- 0.00773436 (deviation=0.6463825878742214)\n", "Check 8 of 20\n", "Weight-1 disturbance = 0 +/- 0.000222213 (deviation=0.0)\n", "Weight-2 disturbance = 2.77556e-17 +/- 0.000275795 (deviation=1.0027476856163692e-13)\n", "Weight-3 disturbance = -2.77556e-17 +/- 0.000311755 (deviation=8.874555980810666e-14)\n", "Weight-4 disturbance = 0.0599 +/- 0.00661126 (deviation=1.4972197269124894)\n", "Check 9 of 20\n", "Weight-1 disturbance = 0 +/- 0.000222464 (deviation=0.0)\n", "Weight-2 disturbance = 0 +/- 0.000281361 (deviation=0.0)\n", "Weight-3 disturbance = 0 +/- 0.000315589 (deviation=0.0)\n", "Weight-4 disturbance = 0.0617 +/- 0.0077288 (deviation=1.513623578666049)\n", "Check 10 of 20\n", "Weight-1 disturbance = 0 +/- 0.00021946 (deviation=0.0)\n", "Weight-2 disturbance = 0 +/- 0.000276702 (deviation=0.0)\n", "Weight-3 disturbance = 0 +/- 0.000312045 (deviation=0.0)\n", "Weight-4 disturbance = 0.0497 +/- 0.00661173 (deviation=0.045367032650432154)\n", "Check 11 of 20\n", "Weight-1 disturbance = 0 +/- 0.000223199 (deviation=0.0)\n", "Weight-2 disturbance = 1.38778e-17 +/- 0.000265554 (deviation=5.206370696523145e-14)\n", "Weight-3 disturbance = -1.38778e-17 +/- 0.000319565 (deviation=4.3291641409384156e-14)\n", "Weight-4 disturbance = 0.0461 +/- 0.00774207 (deviation=0.5036761252635799)\n", "Check 12 of 20\n", "Weight-1 disturbance = 0 +/- 0.000222423 (deviation=0.0)\n", "Weight-2 disturbance = 6.93889e-18 +/- 0.000276431 (deviation=2.5011276473195888e-14)\n", "Weight-3 disturbance = -6.93889e-18 +/- 0.000312102 (deviation=2.2161793896502998e-14)\n", "Weight-4 disturbance = 0.0471 +/- 0.00661158 (deviation=0.43855776440960176)\n", "Check 13 of 20\n", "Weight-1 disturbance = 0 +/- 0.000222348 (deviation=0.0)\n", "Weight-2 disturbance = 0 +/- 0.000270045 (deviation=0.0)\n", "Weight-3 disturbance = 0 +/- 0.000336146 (deviation=0.0)\n", "Weight-4 disturbance = 0.0357 +/- 0.00661204 (deviation=2.1623953001266445)\n", "Check 14 of 20\n", "Weight-1 disturbance = 0 +/- 0.000223202 (deviation=0.0)\n", "Weight-2 disturbance = 0 +/- 0.00027528 (deviation=0.0)\n", "Weight-3 disturbance = 0 +/- 0.000315537 (deviation=0.0)\n", "Weight-4 disturbance = 0.055 +/- 0.00661129 (deviation=0.7561681634414716)\n", "Check 15 of 20\n", "Weight-1 disturbance = 0 +/- 0.000222948 (deviation=0.0)\n", "Weight-2 disturbance = 0 +/- 0.000301141 (deviation=0.0)\n", "Weight-3 disturbance = 0 +/- 0.000301014 (deviation=0.0)\n", "Weight-4 disturbance = 0.0364 +/- 0.00680892 (deviation=1.9970852638291023)\n", "Check 16 of 20\n", "Weight-1 disturbance = 0 +/- 0.000223246 (deviation=0.0)\n", "Weight-2 disturbance = 0 +/- 0.000273596 (deviation=0.0)\n", "Weight-3 disturbance = 0 +/- 0.00033148 (deviation=0.0)\n", "Weight-4 disturbance = 0.0378 +/- 0.00661186 (deviation=1.8448900254282627)\n", "Check 17 of 20\n", "Weight-1 disturbance = 0 +/- 0.000223223 (deviation=0.0)\n", "Weight-2 disturbance = 0 +/- 0.000275921 (deviation=0.0)\n", "Weight-3 disturbance = 0 +/- 0.000312081 (deviation=0.0)\n", "Weight-4 disturbance = 0.0472 +/- 0.00661175 (deviation=0.4234243292833587)\n", "Check 18 of 20\n", "Weight-1 disturbance = 0 +/- 0.000223079 (deviation=0.0)\n", "Weight-2 disturbance = 2.77556e-17 +/- 0.000275356 (deviation=1.0043421246816683e-13)\n", "Weight-3 disturbance = -2.77556e-17 +/- 0.000315514 (deviation=8.769159276775844e-14)\n", "Weight-4 disturbance = 0.0458 +/- 0.0066118 (deviation=0.6351320581700877)\n", "Check 19 of 20\n", "Weight-1 disturbance = 0 +/- 0.000222484 (deviation=0.0)\n", "Weight-2 disturbance = 0 +/- 0.00027661 (deviation=0.0)\n", "Weight-3 disturbance = 0 +/- 0.000315487 (deviation=0.0)\n", "Weight-4 disturbance = 0.0504 +/- 0.00661144 (deviation=0.06049207861490999)\n", "Check 20 of 20\n", "Weight-1 disturbance = 0 +/- 0.000223209 (deviation=0.0)\n", "Weight-2 disturbance = 0 +/- 0.000301627 (deviation=0.0)\n", "Weight-3 disturbance = 0 +/- 0.000298022 (deviation=0.0)\n", "Weight-4 disturbance = 0.036 +/- 0.00680901 (deviation=2.0557975908568977)\n", "Percentanges within 68% confidence region:\n", "Weight 1: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n", "Weight 2: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0027476856163692e-13, 0.0, 0.0, 5.206370696523145e-14, 2.5011276473195888e-14, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0043421246816683e-13, 0.0, 0.0]\n", "Weight 3: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 8.874555980810666e-14, 0.0, 0.0, 4.3291641409384156e-14, 2.2161793896502998e-14, 0.0, 0.0, 0.0, 0.0, 0.0, 8.769159276775844e-14, 0.0, 0.0]\n", "Weight 4: [1.5877420124959105, 0.28732501691130974, 0.7410214778902107, 0.7757368190117534, 1.3004947893168723, 2.1473511479132927, 0.6463825878742214, 1.4972197269124894, 1.513623578666049, 0.045367032650432154, 0.5036761252635799, 0.43855776440960176, 2.1623953001266445, 0.7561681634414716, 1.9970852638291023, 1.8448900254282627, 0.4234243292833587, 0.6351320581700877, 0.06049207861490999, 2.0557975908568977]\n", "\n" ] }, { "data": { "image/png": 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\n", 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\n", 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\n", 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sOszMrLEiruG7H/CypGERsRrwBDChqn0QsL+kxwtYt5mZtaCI8L8RuCnf7gEsqGkfBIyKiI8BEyX9pIAazMysiU4f85f0hqQ5EdGX9CJwYs0s44GRwBBg64gY2tk1mJlZc4V84BsRnwQeBMZJurZqeg/gXEmzJc0DJgIDi6jBzMwaK+ID3zWBe4HDJd1f09wPmBYRA4A3Sb3/sZ1dg5mZNVfEmP9o4MPASRFxUp42BlhZ0qURMZr0ruBt4H5JdxZQg5mZNdHp4S/pSODIJu3jgHGdvV4zM2udf+RlZlZCDn8zsxJy+JuZlVDLY/4R0Q9YBOwF3CHplcKqMjOzQrUU/hExHrgD2JL0buGrpBcBMzPrhlod9llL0tXAAEkjgb4F1mRmZgVrNfxXjIivAk9HxBo4/M3MurVWx/zPBL4FHA0cAZxaWEVmZla4VsN/dUn75NsnR8QRRRVkZmbFaxr+EfEtYHdgh4gYkif3BDYEzi+4NjMzK0hHPf+7gb8BqwOX5GmLgGeLLMrMzIrVNPzzd/knAZMi4qNAn1YeZ2Zmy7ZWv+d/IbAr8FfS1bkWk77zb2Zm3VCrPfjBwLqSFhVZjJmZdY1Wv+c/gyVDPmZm1s212vP/FPB8RMzI9xdL8rCPmVk31Wr4f6vQKszMrEu1Gv4H1JnmX/mamXVTrYb/i/n/HsCXaPJZQUT0Jl2UvT+wEvBjSROq2ncDTgYWAGMljVn6ss3M7INoKfwlXVJ9PyLuajL7fsDLkoZFxGrAE8CE/LjewDnApsCbwJSImCDpxUYLMzOzztfq9/zXr7r7cWCdJrPfCNyUb/cg9fArBgAzKheCiYjJwLb5MYWYO38hfXr3KmrxZm3TrmPbf1PLh1aHfap7/nOBYxrNKOkNgIjoS3oROLGquR/wWtX9OcCqLdbwvvTp3Yv+x08schUNzTxj17as18qhXce2j+vlQ6vDPjtExOrAZ4DnJM1uNn9EfBK4FfilpGurml7n3dcC6Au8ulQVm5nZB9bSj7wi4uvAI8BoYGpE7Ndk3jWBe4HjJI2taZ4OrBcRq0XEiqQhn0ffV+VmZva+tTrsczQwSNIbeTjnAeDqBvOOBj4MnBQRJ+VpY4CVJV0aEUcD95BeeMZKeuH9l29mZu9Hq+G/qDKWL2lORMxtNKOkI4Ejm7TfDty+VFWamVmnajX8n4uInwEPAdvg8/mbmXVrrZ7Y7RLgH8BOwEHALwqryMzMCtdq+J8DjJd0OOkHWj8vriQzMytaq+E/X9KzAJKeI13K0czMuqlWx/yfj4jTSV/L3AzwN3TMzLqxVnv+BwEvAV8BZgEHF1aRmZkVrtVf+M4Fzi22FDMz6yqt9vzNzGw54vA3Myshh7+ZWQk5/M3MSsjhb2ZWQg5/M7MScvibmZWQw9/MrIQc/mZmJeTwNzMrIYe/mVkJtXpWz6UWEYOBMyVtXzP9KGA46QRxAIdKUlF1mJnZexUS/hFxLDAMeLNO8yBgf0mPF7FuMzPrWFHDPs8CX23QNggYFRGTI2JUQes3M7MmCgl/STcD8xs0jwdGAkOArSNiaBE1mJlZY136gW9E9ADOlTRb0jxgIjCwK2swM7MCP/BtoB8wLSIGkD4PGAKM7eIazMxKr0vCPyL2BVaRdGlEjAYeBN4G7pd0Z1fUYGZmSxQW/pJmApvn29dWTR8HjCtqvWZm1jH/yMvMrIQc/mZmJeTwNzMrIYe/mVkJOfzNzErI4W9mVkIOfzOzEnL4m5mVkMPfzKyEHP5mZiXk8DczKyGHv5lZCTn8zcxKyOFvZlZCDn8zsxJy+JuZlZDD38yshAoL/4gYHBGT6kzfLSL+KyIejYhDilq/mZk1Vkj4R8SxwGVAn5rpvYFzgJ2B7YAREbFmETWYmVljRfX8nwW+Wmf6AGCGpFckzQMmA9sWVIOZmTVQSPhLuhmYX6epH/Ba1f05wKpF1GDWFebOX9juErpcO7e5jPu7KCt08fpeB/pW3e8LvNrFNZh1mj69e9H/+IltWffMM3Zty3rbvc3tWHe79nWRujr8pwPrRcRqwBukIZ+zu7gGM7PS65Lwj4h9gVUkXRoRRwP3kIacxkp6oStqMDOzJQoLf0kzgc3z7Wurpt8O3F7Ues3MrGP+kZeZWQk5/M3MSsjhb2ZWQg5/M7MScvibmZWQw9/MrIQc/mZmJeTwNzMrIYe/mVkJOfzNzErI4W9mVkIOfzOzEnL4m5mVkMPfzKyEHP5mZiXk8DczKyGHv5lZCTn8zcxKqJDLOEZET+CXwBeBt4HhkmZUtZ8HbA3MyZP2kPRaEbWYmdl7FXUN3z2BPpK2iIjNgZ8Be1S1DwJ2kTS7oPWbmVkTRQ37bA3cDSBpKrBJpSG/K1gPuDQipkTEwQXVYGZmDRQV/v2A6mGchRFReZexMnABsB/wL8B3I2KjguowM7M6igr/14G+1euRtCDffgs4T9JbkuYAD5A+GzAzsy5SVPhPAb4CkMf8n6xqWx+YEhG9IqI3aYjodwXVYWZmdRT1ge+twE4R8QjQAzgoIo4GZkiaEBHjgKnAfOAqSU8VVIeZmdVRSPhLWgSMrJn8TFX7WcBZRazbzMw65h95mZmVkMPfzKyEHP5mZiXk8DczKyGHv5lZCTn8zcxKyOFvZlZCDn8zsxJy+JuZlZDD38yshBz+ZmYl5PA3Myshh7+ZWQk5/M3MSsjhb2ZWQg5/M7MScvibmZWQw9/MrIQKuYxjRPQEfgl8EXgbGC5pRlX7IcChwALgx5LuKKIOMzOrr6ie/55AH0lbAMcDP6s0RMTHgCOArYBdgJ9ExEoF1WFmZnUUFf5bA3cDSJoKbFLVthkwRdLbkl4DZgAbFVSHmZnV0WPx4sWdvtCIuAy4WdJd+f7/B9aVtCAi9gM2lHRcbrsKuErSfzZZ3izg+U4v1Mxs+baOpI/UayhkzB94Hehbdb+npAUN2voCrzZbWKPizczs/Slq2GcK8BWAiNgceLKq7bfANhHRJyJWBQYA0wqqw8zM6ihq2KfybZ+NgB7AQaQXgxmSJuRv+4wgvficLunmTi/CzMwaKiT8zcxs2eYfeZmZlZDD38yshBz+ZmYlVNRXPQvh00Y018L+OY/0A7w5edIe+Yd2pRIRg4EzJW1fM3034GTS8TNW0pg2lNd2TfbPUcBwYFaedKgkdXF5bRMRvYGxQH9gJVLGTKhq71bHT7cKf6pOG5G/QvozYA9412kjNgH6AJMj4j5Jb7er2DbYkwb7JxsE7CJpdjuKWxZExLHAMODNmum9gXOATXPblIiYIOnFrq+yfRrtn2wQsL+kx7u2qmXGfsDLkoZFxGrAE8AE6J7HT3cb9vFpI5pruH/yu4L1gEsjYkpEHNyeEtvuWeCrdaYPIH0V+RVJ84DJwLZdWtmyodH+gRT+oyJickSM6sKalhU3Aifl2z1IPfyKbnf8dLfw7wdUD1MsjIgVGrTNAVbtqsKWEc32z8rABaTey78A342Isr04kn9TMr9Ok48fmu4fgPHASGAIsHVEDO2ywpYBkt6QNCci+gI3ASdWNXe746e7hX+nnjZiOdRs/7wFnCfpLUlzgAdInw1Y4uOniYjoAZwraXbu2U4EBra5rC4XEZ8EHgTGSbq2qqnbHT/dbcx/CrAbcEOD00acFhF9SB/GlPG0Ec32z/rA9RExkPSivzVwZdeXuMyaDqyXx3LfIL1lP7u9JS1T+gHTImIAaUx7COnDz9KIiDWBe4HDJd1f09ztjp/uFv63AjtFxCPk00ZExNEsOW3E+cDDpHA7QdLcNtbaDh3tn3HAVNLb+qskPdXGWpcJEbEvsIqkS/O+uod0/IyV9EJ7q2u/mv0zmtTrfRu4X9Kd7a2uy40GPgycFBGVsf8xwMrd8fjx6R3MzEqou435m5lZJ3D4m5mVkMPfzKyEHP5mZiXk8DczKyGHvxUiX6Zz5lI+ZuOIOLmDZQ7Ptw+MiN0/YI29IuK2D7KMD7j+mfl3KZ21vEkR8blOWtaGEbFtvt2pddqyobt9z9+WY5KeIJ0sq5GPkc4qeZmkKzphlVsBj3TCcpZHewN/Bx5qdyFWDIe/dZqIWAW4hvRDmOpTSW8InE/64dnLwMHAD4HfS7oyn5F1InAMMFLSNyPicNIJxlYGZgN7AScAG+R3Bz2Bv0u6OCJ+RvrFMsC1ks6LiCtIP0bqD3wcOFDS72pKHgpcUbMN9WodCJwJzAMuBY4D/pjvjwSuJv0CdgXgREkPRMS0yjySvtlkt10SEf2BF4EDgN7AZcCHgLWACyVdFBGTgJeA1UhnZl3YaIERsSpwObB6nnSEpCcj4k+kX4FHXt/ewIrAVXldfyH9MnVT4EBgXkRU9tlFEfHpfHsvSa802SbrBjzsY51pJDBN0rbAJVXTxwCH5fPD3wkcSwq4A3L7MOBXlZnzGUhXB3aUNJgUqpsCpwFPSzq1at6hwKeBzUkvAPvmAAd4XtIupBPajahT7wBJT9dMq1crpFNlbyNpHLAK8KMc6icC9+Vt/jpweT4PTvU8zVwkaTtgJnAI8FlgvKSdgZ2Bo6vmvU7Sjs2CPxtN+gXuDnm7L8rT1wVOkrQF8BHSPh0B/FnSVsApwJr5l6lXAD+X9Nv82MvzPpkJ7NTB+q0bcPhbZ1qfdI4lJP2GJWeHHAD8MvdeDwbWzqG7QkSsA3yD1HsmP3YRqVd9XURcDnyC1COuZwDwsKTFkuaTTl+xQW777/z/X0jXeHhHRKwLPNdgee+qtVJWzXyqmv+hXPcLpBN8fbTBY2rNy6fehjT8VOmR7xkRV5NeWKq3u9ULp2wIHJy3YQzp3QLAbEl/ybcr+2RAXjeSnmHJhVpqVc7h/3fgn1usw5ZhDn/rTE8DWwDkE8hVgkuki4BsT+pJV66wdjnwU1Jv/tXKQvKppveU9A3ge6TjtAewiPces9PJQz75ghpbAn/Kbc3OXTKUNNRUq1Gti2rmq9yfDmyT1782acjr5QaPqbViRGycb29DOhHhMcCjkvYjnT+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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "test_errorbars({'0000': 0.45, '1111': 0.55}) # weight 4 movement " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 2 }