psignifit_issue_demo

ci_error.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import psignifit\n",
        "from psignifit import psigniplot\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt"
      ],
      "outputs": [],
      "execution_count": 22,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2024-09-24T19:43:08.833Z",
          "iopub.execute_input": "2024-09-24T19:43:08.836Z",
          "iopub.status.idle": "2024-09-24T19:43:08.840Z",
          "shell.execute_reply": "2024-09-24T19:43:08.843Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "width = 10\n",
        "stim_range = [14, 14 + width * 3]\n",
        "threshold = 30\n",
        "eta =  0.3\n",
        "\n",
        "lambda_ = 0.1\n",
        "gamma = 0.5 # guess rate\n",
        "nsteps = 4\n",
        "num_trials = 30\n",
        "sigmoid = \"norm\"\n",
        "\n",
        "options = {}\n",
        "options['sigmoid'] = sigmoid \n",
        "options['experiment_type'] = 'yes/no'\n",
        "options['fixed_parameters'] = {'gamma': 0.5}\n",
        "options['stimulus_range'] = stim_range\n",
        "options['CI_method'] = 'percentiles'"
      ],
      "outputs": [],
      "execution_count": 33,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2024-09-24T19:47:56.458Z",
          "iopub.execute_input": "2024-09-24T19:47:56.461Z",
          "iopub.status.idle": "2024-09-24T19:47:56.471Z",
          "shell.execute_reply": "2024-09-24T19:47:56.473Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "seed = 123123180"
      ],
      "outputs": [],
      "execution_count": 34,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2024-09-24T19:47:56.867Z",
          "iopub.execute_input": "2024-09-24T19:47:56.876Z",
          "iopub.status.idle": "2024-09-24T19:47:56.881Z",
          "shell.execute_reply": "2024-09-24T19:47:56.884Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(seed)\n",
        "random_state = np.random.RandomState(seed)\n",
        "\n",
        "stimulus_level = np.linspace(stim_range[0], stim_range[1], nsteps)\n",
        "perccorr = psignifit.tools.psychometric_with_eta(stimulus_level, threshold, width, gamma, lambda_, sigmoid, eta, random_state=random_state)\n",
        "ntrials = np.ones(nsteps) * num_trials\n",
        "hits = (perccorr * ntrials).astype(int)\n",
        "data = np.dstack([stimulus_level, hits, ntrials]).squeeze()\n",
        "\n",
        "res = psignifit.psignifit(data, **options)\n",
        "#print(res.parameter_estimate[\"threshold\"], res.confidence_intervals[\"threshold\"])\n",
        "\n",
        "for i in res.confidence_intervals[\"threshold\"]:\n",
        "  assert res.parameter_estimate[\"threshold\"]>i[0]\n",
        "  assert res.parameter_estimate[\"threshold\"]<i[1]\n",
        "  "
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "123123180\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "AssertionError",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[35], line 15\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m res\u001b[38;5;241m.\u001b[39mconfidence_intervals[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthreshold\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[1;32m     14\u001b[0m   \u001b[38;5;28;01massert\u001b[39;00m res\u001b[38;5;241m.\u001b[39mparameter_estimate[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthreshold\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m>\u001b[39mi[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m---> 15\u001b[0m   \u001b[38;5;28;01massert\u001b[39;00m res\u001b[38;5;241m.\u001b[39mparameter_estimate[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthreshold\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m<\u001b[39mi[\u001b[38;5;241m1\u001b[39m]\n",
            "\u001b[0;31mAssertionError\u001b[0m: "
          ]
        }
      ],
      "execution_count": 35,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2024-09-24T19:47:57.088Z",
          "iopub.execute_input": "2024-09-24T19:47:57.096Z",
          "iopub.status.idle": "2024-09-24T19:47:31.178Z",
          "shell.execute_reply": "2024-09-24T19:47:31.182Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(res.parameter_estimate[\"threshold\"])"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32.93912458971731\n"
          ]
        }
      ],
      "execution_count": 36,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2024-09-24T19:47:58.656Z",
          "iopub.execute_input": "2024-09-24T19:47:58.658Z",
          "iopub.status.idle": "2024-09-24T19:47:58.667Z",
          "shell.execute_reply": "2024-09-24T19:47:58.671Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(res.confidence_intervals[\"threshold\"])"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[24.60496885313708, 35.73536759413769], [25.309362325079807, 33.726255527766014], [26.941014699095206, 31.988957664460678]]\n"
          ]
        }
      ],
      "execution_count": 37,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2024-09-24T19:47:59.287Z",
          "iopub.execute_input": "2024-09-24T19:47:59.289Z",
          "iopub.status.idle": "2024-09-24T19:47:59.300Z",
          "shell.execute_reply": "2024-09-24T19:47:59.303Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "datana"
    },
    "language_info": {
      "name": "python",
      "version": "3.10.6",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kernelspec": {
      "argv": [
        "/Users/lisa/Documents/virtual_envs/datana/bin/python3",
        "-m",
        "ipykernel_launcher",
        "-f",
        "{connection_file}"
      ],
      "display_name": "datana",
      "language": "python",
      "metadata": {
        "debugger": true
      },
      "name": "datana"
    },
    "nteract": {
      "version": "0.28.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}