{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Light Sensitivity Experiment: \n",
    "\n",
    "## Reporting a repeated treatment design with `ISA create mode`\n",
    "\n",
    "This example creates `ISA study descriptor` for study with sequential treatments organized in an arm. This shows how to use objects from the `isatools.create` component in a granular fashion. It creates each `Element` of the Study `Arm` at a time.\n",
    "Finally, the `study design plan` is shown by serializing the `ISA Study Design Model` content as an  `ISA_design` JSON document, which can be rendered in various ways (tables, figures)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Study metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# If executing the notebooks on `Google Colab`,uncomment the following command \n",
    "# and run it to install the required python libraries. Also, make the test datasets available.\n",
    "\n",
    "# !pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import datetime\n",
    "import json\n",
    "from collections import OrderedDict\n",
    "from isatools.model import (\n",
    "    Investigation,\n",
    "    Study,\n",
    "    Sample,\n",
    "    OntologyAnnotation,\n",
    "    StudyFactor,\n",
    "    FactorValue,\n",
    "    Characteristic,\n",
    "    Source,\n",
    "    Protocol,\n",
    "    Process\n",
    ")\n",
    "from isatools.create.model import (\n",
    "    Treatment,\n",
    "    NonTreatment,\n",
    "    StudyDesign,\n",
    "    StudyCell,\n",
    "    StudyArm,\n",
    "    ProductNode,\n",
    "    SampleAndAssayPlan,\n",
    "    AssayGraph\n",
    ")\n",
    "from isatools.create.constants import (\n",
    "    BASE_FACTORS,\n",
    "    SCREEN,\n",
    "    RUN_IN,\n",
    "    WASHOUT,\n",
    "    FOLLOW_UP,\n",
    "    SAMPLE,\n",
    "    EXTRACT,\n",
    "    LABELED_EXTRACT,\n",
    "    DATA_FILE\n",
    ")\n",
    "from isatools.isatab import dumps\n",
    "from isatools.isajson import ISAJSONEncoder\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "investigation = Investigation()\n",
    "investigation1 = Investigation() # to be used with the study create function\n",
    "study = Study(filename=\"s_study_xover.txt\")\n",
    "study.identifier = \"elifesprint2019-1\"\n",
    "study.title = \"elifesprint2019-1: light sensitivity\"\n",
    "study.description = \"a study about light sensitivity difference between a control population (n=10) and a genotype A population (n=10).\"\n",
    "study.submission_date = str(datetime.datetime.today())\n",
    "study.public_release_date = str(datetime.datetime.today())\n",
    "study.sources = [Source(name=\"source1\")]\n",
    "study.samples = [Sample(name=\"sample1\")]\n",
    "study.protocols = [Protocol(name=\"sample collection\")]\n",
    "study.process_sequence = [Process(executes_protocol=study.protocols[-1], inputs=[study.sources[-1]], outputs=[study.samples[-1]])]\n",
    "investigation.studies = [study]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Let's see the object :\n",
    "investigation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# print(dumps(investigation))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# print(json.dumps(investigation, cls=ISAJSONEncoder, sort_keys=True, indent=4, separators=(',', ': ')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Creation of the first `ISA Study Design Element` and setting *both* `element_type` AND `duration_unit` attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# IMPORTANT: note how duration_unit value is supplied as an OntologyAnnotation object\n",
    "nte1 = NonTreatment(element_type='screen', duration_unit=OntologyAnnotation(term=\"days\"))\n",
    "print(nte1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Creation of another `ISA Study Design Element`, of type `Treatment`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "te1 = Treatment()\n",
    "te1.type='radiological intervention'\n",
    "print(te1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 defining the first treatment as a vector of ISA factor values:\n",
    "\n",
    "Under \"ISA Study Design Create mode\", a `Study Design Element` of type `Treatment` needs to be defined by a vector of `Factors` and their respective associated `Factor Values`. This is done as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "\n",
    "f1 = StudyFactor(name='light', factor_type=OntologyAnnotation(term=\"electromagnetic energy\"))\n",
    "f1v = FactorValue(factor_name=f1, value=\"visible light at 3000K produced by LED array\")\n",
    "f2 = StudyFactor(name='dose', factor_type=OntologyAnnotation(term=\"quantity\"))\n",
    "\n",
    "# IMPORTANT: note how *FactorValue value* is supplied as an *numeral*\n",
    "f2v = FactorValue(factor_name=f2, value=250, unit=OntologyAnnotation(term='lux'))\n",
    "f3 = StudyFactor(name='duration', factor_type=OntologyAnnotation(term=\"time\"))\n",
    "f3v = FactorValue(factor_name=f3, value=1, unit=OntologyAnnotation(term='hr'))\n",
    "\n",
    "print(f1v,f2v)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#assigning the factor values declared above to the ISA treatment element\n",
    "te1.factor_values = [f1v,f2v,f3v]\n",
    "print(te1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Creation of a second  `ISA Study Design Element`, of type `Treatment`, following the same pattern."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "te3 = Treatment()\n",
    "te3.type = 'radiological intervention'\n",
    "rays = StudyFactor(name='light', factor_type=OntologyAnnotation(term=\"electromagnetic energy\"))\n",
    "\n",
    "raysv = FactorValue(factor_name=rays, value='visible light at 3000K produced by LED array')\n",
    "rays_intensity = StudyFactor(name='dose', factor_type=OntologyAnnotation(term=\"quantity\"))\n",
    "rays_intensityv= FactorValue(factor_name=rays_intensity, value = 250, unit=OntologyAnnotation(term='lux'))\n",
    "rays_duration =  StudyFactor(name = 'duration', factor_type=OntologyAnnotation(term=\"time\"))\n",
    "rays_durationv = FactorValue(factor_name=rays_duration, value=1, unit=OntologyAnnotation(term='hour'))\n",
    "\n",
    "te3.factor_values = [raysv,rays_intensityv,rays_durationv]\n",
    "print(te3)\n",
    "                "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Creation of 'wash out' period as an `ISA Study Design Element`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Creation of another ISA element, which is not a Treatment element, which is of type `screen` by default\n",
    "# nte2 = NonTreatment()\n",
    "# nte2.type = 'washout'\n",
    "# net2.duration_unit=OntologyAnnotation(term=\"days\")\n",
    "\n",
    "nte2 = NonTreatment(element_type='washout', duration_unit=OntologyAnnotation(term=\"days\"))\n",
    "print(nte2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# setting the factor values associated with 'default' DURATION Factor associated with such elements\n",
    "nte2.duration.value=2\n",
    "nte2.duration.unit=OntologyAnnotation(term=\"weeks\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Creation of 'follow-up' period as an `ISA Study Design Element`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "nte3 = NonTreatment(element_type='follow-up', duration_value=1, duration_unit=OntologyAnnotation(term=\"month\"))\n",
    "#print(nte3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Creation of the associated container, known as an ISA `Cell` for each ISA `Element`.\n",
    "In this example, a single `Element` is hosted by a `Cell`, which must be named. In more complex designs (e.g. study designs with assymetric arms), a `Cell` may contain more than one `Element`, hence the list attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "st_cl1= StudyCell(name=\"st_cl1\", elements=[nte1])\n",
    "st_cl2= StudyCell(name=\"st_cl2\", elements=[te1])\n",
    "st_cl3= StudyCell(name=\"st_cl3\", elements=[nte2])\n",
    "st_cl4= StudyCell(name=\"st_cl4\", elements=[te3])\n",
    "st_cl5= StudyCell(name=\"st_cl5\", elements=[nte3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Creation of an ISA `Study Arm` and setting the number of subjects associated to that unique sequence of ISA `Cell`s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "genotype_cat = OntologyAnnotation(term=\"genotype\")\n",
    "genotype_value1 = OntologyAnnotation(term=\"control - normal\")\n",
    "genotype_value2 = OntologyAnnotation(term=\"mutant\")\n",
    "\n",
    "arm1 = StudyArm(\n",
    "    name='Arm 1', \n",
    "    group_size=2\n",
    ")\n",
    "\n",
    "arm1.source_type=Characteristic(\n",
    "    category=genotype_cat,\n",
    "    value=genotype_value1\n",
    ")\n",
    "\n",
    "print(arm1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. Declaring an ISA `Sample Assay Plan`, defining which `Sample` are to be collected and which `Assay`s to be used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "whole_patient=ProductNode(\n",
    "    id_=\"MAT1\",\n",
    "    name=\"subject\",\n",
    "    node_type=SAMPLE,\n",
    "    size=1,\n",
    "    characteristics=[\n",
    "        Characteristic(\n",
    "            category=OntologyAnnotation(term='organism part'), \n",
    "            value=OntologyAnnotation(term='whole organism')\n",
    "        )\n",
    "    ]\n",
    ")\n",
    "\n",
    "saliva=ProductNode(\n",
    "    id_=\"MAT2\",\n",
    "    name=\"saliva\",\n",
    "    node_type=SAMPLE,\n",
    "    size=1,\n",
    "    characteristics=[\n",
    "        Characteristic(\n",
    "            category=OntologyAnnotation(term='organism part'),\n",
    "            value=OntologyAnnotation(term='saliva')\n",
    "        )\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we load an isa assay definition in the form of an ordered dictionary. It corresponds to an ISA configuration assay table but expressed in JSON."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now show how to create an new AssayGraph structure from scratch, as if we were defining a completely new assay type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "light_sensitivity_phenotyping_1 = OrderedDict([\n",
    "    ('measurement_type', OntologyAnnotation(term='melatonine concentration')),\n",
    "    ('technology_type', OntologyAnnotation(term='radioimmunoprecipitation assay')),\n",
    "     ('extraction', {}),\n",
    "            ('extract', [\n",
    "                {\n",
    "                    'node_type': EXTRACT,\n",
    "                    'characteristics_category': OntologyAnnotation(term='extract type'),\n",
    "                    'characteristics_value': OntologyAnnotation(term='extract'),\n",
    "                    'size': 1,\n",
    "                    'technical_replicates': None,\n",
    "                    'is_input_to_next_protocols': True\n",
    "                }]),\n",
    "                \n",
    "    ('radioimmunoprecipitation', {\n",
    "                OntologyAnnotation(term='instrument'): [OntologyAnnotation(term='Beckon Dickison XYZ')],\n",
    "                OntologyAnnotation(term='antibody'): [OntologyAnnotation(term='AbCam antiMelatonine ')],\n",
    "                OntologyAnnotation(term='time point'): [OntologyAnnotation(term='1 hr'),\n",
    "                                                        OntologyAnnotation(term='2 hr')]\n",
    "            }),\n",
    "            ('raw_data_file', [\n",
    "                {\n",
    "                    'node_type': DATA_FILE,\n",
    "                    'size': 1,\n",
    "                    'technical_replicates': 1,\n",
    "                    'is_input_to_next_protocols': False\n",
    "                }\n",
    "            ])\n",
    "])\n",
    "\n",
    "\n",
    "light_sensitivity_phenotyping_2 = OrderedDict([\n",
    "        ('measurement_type', OntologyAnnotation(term='light sensitivity')),\n",
    "        ('technology_type', OntologyAnnotation(term='electroencephalography')),\n",
    "            ('data_collection', {\n",
    "                OntologyAnnotation(term='instrument'): [OntologyAnnotation(term='Somnotouch')],\n",
    "                OntologyAnnotation(term='sampling_rate'): [OntologyAnnotation(term='200 Hz')],\n",
    "                OntologyAnnotation(term='time point'): [OntologyAnnotation(term='1 hr'),\n",
    "                                                        OntologyAnnotation(term='2 hr')]\n",
    "            }),\n",
    "            ('raw_data_file', [\n",
    "                {\n",
    "                    'node_type': DATA_FILE,\n",
    "                    'size': 1,\n",
    "                    'technical_replicates': 1,\n",
    "                    'is_input_to_next_protocols': False\n",
    "                }\n",
    "            ])\n",
    "])\n",
    "\n",
    "light_sensitivity_phenotyping_3 = OrderedDict([\n",
    "        ('measurement_type', OntologyAnnotation(term='light sensitivity phenotyping')),\n",
    "        ('technology_type', OntologyAnnotation(term='direct measurement')),\n",
    "            ('data_collection', {\n",
    "                OntologyAnnotation(term='variables'): [OntologyAnnotation(term='sleepiness'),\n",
    "                                                       OntologyAnnotation(term='heart rate'),\n",
    "                                                       OntologyAnnotation(term='pupilla size')],\n",
    "                OntologyAnnotation(term='time point'): [OntologyAnnotation(term='1 hr'),\n",
    "                                                        OntologyAnnotation(term='2 hr')]\n",
    "            }),\n",
    "            ('raw_data_file', [\n",
    "                {\n",
    "                    'node_type': DATA_FILE,\n",
    "                    'size': 1,\n",
    "                    'technical_replicates': 1,\n",
    "                    'is_input_to_next_protocols': False\n",
    "                }\n",
    "            ])\n",
    "])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "alterness_assay_graph = AssayGraph.generate_assay_plan_from_dict(light_sensitivity_phenotyping_1)\n",
    "melatonine_assay_graph = AssayGraph.generate_assay_plan_from_dict(light_sensitivity_phenotyping_2)\n",
    "general_phenotyping_assay_graph = AssayGraph.generate_assay_plan_from_dict(light_sensitivity_phenotyping_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "\n",
    "sap1 = SampleAndAssayPlan(name='sap1', sample_plan=[whole_patient,saliva],assay_plan=[alterness_assay_graph,melatonine_assay_graph,general_phenotyping_assay_graph])\n",
    "\n",
    "sap1.add_element_to_map(sample_node=saliva, assay_graph=melatonine_assay_graph)\n",
    "sap1.add_element_to_map(sample_node=whole_patient, assay_graph=alterness_assay_graph)\n",
    "sap1.add_element_to_map(sample_node=whole_patient,assay_graph=general_phenotyping_assay_graph)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9. Declaration of an ISA assay and linking specimen type and data acquisition plan for this assay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "sap1.sample_to_assay_map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10. Build an ISA `Study Design Arm` by adding the first set of ISA `Cells` and setting the `Sample Assay Plan`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "arm1.add_item_to_arm_map(st_cl1, sap1)\n",
    "# print(arm1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11 Now expanding the `Arm` by adding a new `Cell`, which uses the same `Sample Assay Plan` as the one used in Cell #1.\n",
    "Of course, the `Sample Assay Plan` for this new `Cell` could be different. It would have to be to built as shown before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "arm1.add_item_to_arm_map(st_cl2, sap1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Adding the last section of the Arm, with a cell which also uses the same sample assay plan.\n",
    "arm1.add_item_to_arm_map(st_cl3, sap1)\n",
    "arm1.add_item_to_arm_map(st_cl4, sap1)\n",
    "arm1.add_item_to_arm_map(st_cl5, sap1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 12. Creation of additional ISA Study Arms and setting the number of subjects associated to that unique sequence of ISA Cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "arm2 = StudyArm(name='Arm 2')\n",
    "arm2.group_size=2\n",
    "arm2.source_type=Characteristic(category=genotype_cat,\n",
    "                                value=genotype_value2)\n",
    "\n",
    "# st_cl6= StudyCell(name=\"st_cl6\", elements=[nte1])\n",
    "# st_cl7= StudyCell(name=\"st_cl7\", elements=[te1])\n",
    "# st_cl8= StudyCell(name=\"st_cl8\", elements=[nte2])\n",
    "# st_cl9= StudyCell(name=\"st_cl9\", elements=[te3])\n",
    "# st_cl10= StudyCell(name=\"st_cl10\", elements=[nte3])\n",
    "\n",
    "\n",
    "\n",
    "arm2.source_type.category\n",
    "arm2.add_item_to_arm_map(st_cl1,sap1)\n",
    "arm2.add_item_to_arm_map(st_cl4,sap1)\n",
    "arm2.add_item_to_arm_map(st_cl3,sap1)\n",
    "arm2.add_item_to_arm_map(st_cl2,sap1)\n",
    "arm2.add_item_to_arm_map(st_cl5,sap1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "arm3 = StudyArm(name='Arm 3')\n",
    "arm3.group_size=2\n",
    "arm3.source_type=Characteristic(category=genotype_cat,\n",
    "                                value=genotype_value1\n",
    "                               )\n",
    "arm3.add_item_to_arm_map(st_cl1,sap1)\n",
    "arm3.add_item_to_arm_map(st_cl2,sap1)\n",
    "arm3.add_item_to_arm_map(st_cl3,sap1)\n",
    "arm3.add_item_to_arm_map(st_cl4,sap1)\n",
    "arm3.add_item_to_arm_map(st_cl5,sap1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "arm4 = StudyArm(name='Arm 4')\n",
    "arm4.group_size=2\n",
    "arm4.source_type=Characteristic(category=genotype_cat,\n",
    "                                value=genotype_value2)\n",
    "\n",
    "arm4.add_item_to_arm_map(st_cl1,sap1)\n",
    "arm4.add_item_to_arm_map(st_cl4,None)\n",
    "arm4.add_item_to_arm_map(st_cl3,sap1)\n",
    "arm4.add_item_to_arm_map(st_cl2,None)\n",
    "arm4.add_item_to_arm_map(st_cl5,sap1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 14. We can now create the ISA `Study Design` object, which will receive the `Arms` defined by the user."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "study_design_final= StudyDesign(name='trial design #1')\n",
    "# print(sd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Adding a study arm to the study design object.\n",
    "study_design_final.add_study_arm(arm1)\n",
    "study_design_final.add_study_arm(arm2)\n",
    "study_design_final.add_study_arm(arm3)\n",
    "study_design_final.add_study_arm(arm4)\n",
    "\n",
    "study_finale = study_design_final.generate_isa_study()\n",
    "investigation1.studies.append(study_finale)\n",
    "# print(investigation1.studies[0].name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Let's now serialize the ISA study design to JSON\n",
    "from isatools.create.model import StudyDesignEncoder\n",
    "\n",
    "f=json.dumps(study_design_final, cls=StudyDesignEncoder, sort_keys=True, indent=4, separators=(',', ': '))\n",
    "\n",
    "final_dir = os.path.abspath(os.path.join('notebook-output', 'isa-study-custom-assay-light-sensitivity'))\n",
    "\n",
    "with open(os.path.join(final_dir,'./light-sensitivity-study_design_final.json'), 'w') as isa_sdf_jf:\n",
    "    json.dump(json.loads(f), isa_sdf_jf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# print(json.dumps(investigation, cls=ISAJSONEncoder, sort_keys=True, indent=4, separators=(',', ': ')))\n",
    "from isatools import isatab\n",
    "isatab.dump(investigation1, final_dir)\n",
    "\n",
    "from isatools.isatab import dump_tables_to_dataframes as dumpdf\n",
    "dataframes = dumpdf(investigation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## About this notebook\n",
    "\n",
    "- authors: philippe.rocca-serra@oerc.ox.ac.uk, massimiliano.izzo@oerc.ox.ac.uk\n",
    "- license: CC-BY 4.0\n",
    "- support: isatools@googlegroups.com\n",
    "- issue tracker: https://github.com/ISA-tools/isa-api/issues"
   ]
  }
 ],
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