Adaptation du code au nouvel arbre

eb8e33c1 · Aina · 7ddb5365 · eb8e33c1 · eb8e33c1 · eb8e33c1
Commit eb8e33c1 authored 6 years ago by Aina
--- a/.old/variables_list_types.old
+++ b/.old/variables_list_types.old
--- a/graphs/W3_graph.ipynb
+++ b/graphs/W3_graph.ipynb
--- a/parsers/PySD_Vensim_splitter.ipynb
+++ b/parsers/PySD_Vensim_splitter.ipynb
--- a/parsers/W3_json_generator.ipynb
+++ b/parsers/W3_json_generator.ipynb
@@ -5,6 +5,18 @@
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
+   "source": [
+    "import os\n",
+    "w3_path = os.path.join('..', 'world3.py')\n",
+    "output_path = os.path.join('..', 'data', 'world3-03_variables.json')\n",
+    "w3_72_vars_lists_dir = os.path.join('..', 'models', 'WRLD3-72')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
   "source": [
    "def dependency_visitor(nc):\n",
    "    dependencies = []\n",
@@ -29,7 +41,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
@@ -46,7 +58,7 @@
    "    'name': None,\n",
    "    'description': None\n",
    "}}\n",
-    "t = ast.parse(open('world3.py').read())\n",
+    "t = ast.parse(open(w3_path).read())\n",
    "for n in t.body:\n",
    "    if isinstance(n, ast.FunctionDef): # Fonctions définies avec un \"def\"\n",
    "        func_body = n.body\n",
@@ -87,7 +99,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -113,7 +125,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
@@ -554,7 +566,7 @@
    "\n",
    "for t72, t03 in types.items():\n",
    "    display(Markdown('# ' + t72))\n",
-    "    abbrs, codes = import_w3_72_list('w3-72_' + t72 + '.txt')\n",
+    "    abbrs, codes = import_w3_72_list(os.path.join(w3_72_vars_lists_dir, 'w3-72_' + t72 + '.txt'))\n",
    "\n",
    "    for i in range(len(codes)):\n",
    "        result = [k for k, v in p_db.items() if v['code'] != None and v['code'] == codes[i]]\n",
@@ -592,7 +604,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
@@ -751,12 +763,12 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
-    "with open('world3-03_variables.json', 'w') as fp:\n",
+    "with open(output_path, 'w') as fp:\n",
    "    json.dump(p_db, fp, indent = 4, sort_keys = True)"
   ]
  }

 %% Cell type:code id: tags:
 ``` python
+import os
+w3_path = os.path.join('..', 'world3.py')
+output_path = os.path.join('..', 'data', 'world3-03_variables.json')
+w3_72_vars_lists_dir = os.path.join('..', 'models', 'WRLD3-72')
+```
+%% Cell type:code id: tags:
+``` python
 def dependency_visitor(nc):
    dependencies = []
    numeric_const = None
    for ncc in ast.walk(nc):
        # Hypothèse sur le code généré par PySD :
        # Toutes les variables du modèle sont des fonctions en Python
        if isinstance(ncc, ast.Call) and isinstance(ncc.func, ast.Name):
            dep_name = ncc.func.id
            dependencies.append(dep_name)
        elif isinstance(ncc, ast.Num):
            if numeric_const == None:
                numeric_const = ncc.n
        elif isinstance(ncc, ast.Call) and ncc.func.attr == 'lookup':
            numeric_const = []
            for arg in ncc.args:
                if isinstance(arg, ast.List):
                    numeric_const.append([n.n for n in arg.elts if isinstance(n, ast.Num)])
    return numeric_const, list(set(dependencies))
 ```
 %% Cell type:code id: tags:
 ``` python
 import re, ast
 p_db = {'time': {
    'type' : 'other',
    'dependencies': None,
    'code': None,
    'in72': None,
    'abbr': None,
    'name': None,
    'description': None
 }}
-t = ast.parse(open('world3.py').read())
+t = ast.parse(open(w3_path).read())
 for n in t.body:
    if isinstance(n, ast.FunctionDef): # Fonctions définies avec un "def"
        func_body = n.body
        name = n.name
        p_db[name] = {'in72': None}
        for nc in func_body:
            if isinstance(nc, ast.Return):
                value, dependencies = dependency_visitor(nc)
                if len(dependencies) == 0:
                    p_db[name]['value'] = value
                    p_db[name]['dependencies'] = None
                else:
                    p_db[name]['value'] = None
                    p_db[name]['dependencies'] = dependencies
            if isinstance(nc, ast.Expr):
                comment = list(map(lambda s: s.strip(), nc.value.s.strip().split('\n\n')))
                p_db[name]['name'] = comment[0]
                p_db[name]['type'] = comment[4]
                p_db[name]['description'] = p_db[name]['abbr'] = p_db[name]['code'] = None
                if len(comment) >= 6:
                    description = re.sub('\s+', ' ', comment[5])
                    p_db[name]['description'] = description
                    code_search = re.search(r'\((.*)\)', description)
                    if code_search != None and len(code_search[1].split('#')) >= 2:
                        p_db[name]['abbr'], p_db[name]['code'] = code_search[1].split('#')[:2]
    if isinstance(n, ast.Assign): # Fonctions définies avec un "=" (fonctions "séparées") : FONCTIONS D'ÉTAT
        target = n.targets[0]
        name = target.id
        _, dependencies = dependency_visitor(n)
        result = {k: v for k, v in p_db.items() if v['dependencies'] and name in v['dependencies']}
        for r in result:
            p_db[r]['dependencies'].remove(name)
            p_db[r]['dependencies'].extend(dependencies)
            p_db[r]['type'] = 'stateful'
 ```
 %% Cell type:code id: tags:
 ``` python
 def import_w3_72_list(filename):
    '''
    Importe une liste de variables de W3-72 :
    sur chaque ligne, l'acronyme de la variable et son identifiant numérique séparés d'un espace
    retourne la liste des identifiants numériques
    '''
    w3_72_list_file = open(filename)
    w3_72_abbr = []
    w3_72_code = []
    for l in w3_72_list_file.read().split('\n'):
        try:
            abbr, code = l.split(' ')
            w3_72_abbr.append(abbr)
            w3_72_code.append(code)
        except:
            pass
    w3_72_list_file.close()
    return w3_72_abbr, w3_72_code
 ```
 %% Cell type:code id: tags:
 ``` python
 types_72 = ['parameter', 'table', 'state']
 types_03 = ['constant', 'lookup', 'stateful']
 types = dict(zip(types_72, types_03))
 from IPython.display import Markdown, HTML
 import tabulate
 for t72, t03 in types.items():
    display(Markdown('# ' + t72))
-    abbrs, codes = import_w3_72_list('w3-72_' + t72 + '.txt')
+    abbrs, codes = import_w3_72_list(os.path.join(w3_72_vars_lists_dir, 'w3-72_' + t72 + '.txt'))
    for i in range(len(codes)):
        result = [k for k, v in p_db.items() if v['code'] != None and v['code'] == codes[i]]
        if(len(result) == 0):
            print(f'{abbrs[i]}#{codes[i]} not found in 2003 version')
        elif len(result) > 1:
            for r in result:
                p_db[r]['in72'] = t72
            print(f'{abbrs[i]}#{codes[i]} several variables found in 2003 version')
        elif len(result) == 1:
            p_db[result[0]]['in72'] = t72
    lists = {'New': {}, 'Not in 2003': {}, 'In both': {}}
    for c_id in p_db:
        correspond_in03 = (
            p_db[c_id]['type'] == t03
        )
        correspond_in72 = p_db[c_id]['in72'] == t72
        if correspond_in03 and not correspond_in72:
            lists['New'][c_id] = p_db[c_id]
        elif not correspond_in03 and correspond_in72:
            lists['Not in 2003'][c_id] = p_db[c_id]
        elif correspond_in03 and correspond_in72:
            lists['In both'][c_id] = p_db[c_id]
    for l in lists:
        display(Markdown(f'## {l}'))
        table = []
        for c_id in sorted(lists[l]):
            table.append([p_db[c_id]["abbr"], p_db[c_id]["code"], p_db[c_id]['name']])
        print(tabulate.tabulate(table, tablefmt='plain'))
 ```
 %% Output
    # parameter
    ## New
    ARPTM    --     air pollution policy implementation time
    AHL70    146.1  assimilation half life in 1970
    ALAI1    100.1  average life of agricultural inputs 1
    ALAI2    100.2  average life of agricultural inputs 2
    ALIC1    54.1   average life of industrial capital 1
    ALIC2    54.2   average life of industrial capital 2
    ALSC1    69.1   average life of service capital 1
    ALSC2    69.2   average life of service capital 2
    DFR      --     desired food ratio
    DPOLX    --     desired persistent pollution index
    DNRUR    --     desired resource use rate
    FCEST    45.1   fertility control effectiveness time
                    FINAL TIME
    FCAORTM  --     fraction of industrial capital allocated to obtaining resources switch time
    FIOAC1   58.1   fraction of industrial output allocated to consumption constant 1
    FIOAC2   58.2   fraction of industrial output allocated to consumption constant 2
                    GDP pc unit
                    ha per Gha
                    ha per unit of pollution
    ICOR1    51.1   industrial capital output ratio 1
    IET      57.1   industrial equilibrium time
    ALI      85.2   initial arable land
    ICI      52.1   initial industrial capital
    LFERTI   121.2  initial land fertility
    NRI      129.2  initial nonrenewable resources
    PPOLI    142.2  initial persistent pollution
    P1I      2.2    initial population 0 to 14
    P2I      6.2    initial population 15 to 44
    P3I      10.2   initial population 54 to 64
    P4I      14.2   initial population 65 plus
    PALI     86.2   initial potentially arable land
    SCI      67.2   initial service capital
                    INITIAL TIME
    UILI     120.1  initial urban and industrial land
    LLMYTM   --     land life policy implementation time
    LYF1     104.1  land yield factor 1
                    one year
    PPGF1    138.1  persistent pollution generation factor 1
    PYEAR    150.1  POLICY YEAR
    PET      30.2   population equilibrium time
                    PRICE OF FOOD
                    Ref Hi GDP
                    Ref Lo GDP
    NRUF1    131.1  resource use factor 1
    SCOR1    72.1   service capital output ratio 1
    SCOR2    72.2   service capital output ratio 2
    TDD      --     technology development delay
                    THOUSAND
                    TIME STEP
                    Total Land
                    unit agricultural input
                    unit population
    ZPGT     38.1   zero population growth time
    ## Not in 2003
    AHL    146  assimilation half life
    ALAI   100  average life agricultural inputs
    ALSC    69  average life of service capital
    FIOAC   57  fraction of industrial output allocated to consumption
    ICOR    51  industrial capital output ratio
    LYF    104  land yield multiplier from technology
    PPGF   138  persistent pollution generation factor
    NRUF   131  resource use factor
    SCOR    72  service capital output ratio
    ## In both
    AMTI    140.2  agricultural material toxicity index
    ALLN    112.1  average life of land normal
    DCFSN    38.2  desired completed family size normal
    FSPD    128.2  food shortage perception delay
    FIPM    140.1  fraction of agricultural inputs from persistent materials
    FRPM    139.1  fraction of resources from persistent materials
    HSID     22.1  health services impact delay
    IEAT     43.1  income expectation averaging time
    IO70    107.2  IND OUT IN 1970
    IMEF    139.2  industrial material emissions factor
    IMTI    139.3  industrial material toxicity index
    IOPCD    59.2  industrial output per capita desired
    ILF     124.1  inherent land fertility
    LFPF     80.1  labor force participation fraction
    LUFDT    82.1  labor utilization fraction delay time
    LFH      87.1  land fraction harvested
    LEN      19.1  life expectancy normal
    LPD      37.1  lifetime perception delay
    MTFN     33.1  maximum total fertility normal
    PPOL70  143.1  persistent pollution in 1970
    PPTD    141.1  persistent pollution transmission delay
    PALT     84.1  potentially arable land total
    PL       87.2  processing loss
    RLT      30.1  reproductive lifetime
    SAD      40.1  social adjustment delay
    SD      109.1  social discount
    SFPC    127.1  subsistence food per capita
    UILDT   119.1  urban and industrial land development time
    # table
    ## New
    AHLMT    145.1  assimilation half life mult table
    CUFT     83.2   capacity utilization fraction table
    CMPLET   36.1   completed multiplier from perceived lifetime table
    CMIT     27.1   crowding multiplier from industry table
    DCPHT    97.1   development cost per hectare table
                    Education Index LOOKUP
    FRSNT    41.1   family response to social norm table
    FMT      34.1   fecundity multiplier table
    FCET     45.2   fertility control effectiveness table
    FIOAA1T  94.1   fraction industrial output allocated to agriculture table 1
    FIOAA2T  95.1   fraction industrial output allocated to agriculture table 2
    FIALDT   108.1  fraction of agricultural inputs allocated to land development table
    FALMT    126.1  fraction of agricultural inputs for land maintenance table
    FCAOR1T  135.1  fraction of capital allocated to obtaining resources 1 table
    FCAOR2T  136.1  fraction of capital allocated to obtaining resources 2 table
    FIOACVT  59.1   fraction of industrial output allocated to consumption variable table
    FIOAS1T  64.1   fraction of industrial output allocated to services table 1
    FIOAS2T  65.1   fraction of industrial output allocated to services table 2
    FPUT     26.1   fraction of population urban table
    FSAFCT   48.1   fraction services allocated to fertility control table
                    GDP per capita LOOKUP
    HSAPCT   21.1   health services per capita table
    IFPC1T   90.1   indicated food per capita table 1
    IFPC2T   90.2   indicated food per capita table 2
    ISOPC1T  61.1   indicated services output per capita table 1
    ISOPC2T  62.2   indicated services output per capita table 2
    COPMT    --     industrial capital output ratio multiplier from pollution table
    ICOR2TT  --     industrial capital output ratio multiplier from resource table
    COYMT    --     industrial capital output ratio multiplier table
    JPHT     79.1   jobs per hectare table
    JPICUT   75.1   jobs per industrial capital unit table
    JPSCUT   77.1   jobs per service capital unit table
    LFDRT    122.1  land fertility degredation rate table
    LFRTT    125.1  land fertility regeneration time table
    LLMY1T   114.1  land life multiplier from land yield table 1
    LLMY2T   115.1  land life multiplier from land yield table 2
    LYMAP1T  106.1  land yield multipler from air pollution table 1
    LYMAP2T  107.1  land yield multipler from air pollution table 2
    LYMCT    102.1  land yield multiplier from capital table
    LYCMT    --     land yield technology change rate multiplier table
                    Life Expectancy Index LOOKUP
    LMFT     20.1   lifetime multiplier from food table
    LMHS1T   24.1   lifetime multiplier from health services 1 table
    LMHS2T   25.1   lifetime multiplier from health services 2 table
    LMPT     29.1   lifetime multiplier from persistent pollution table
    MLYMCT   111.1  marginal land yield multiplier from capital table
    M1T      4.1    mortality 0 to 14 table
    M2T      8.1    mortality 15 to 44 table
    M2T      12.1   mortality 45 to 64 table
    M4T      16.1   mortality 65 plus table
    PCRUMT   132.1  per capita resource use mult table
    POLGFMT  --     persistent pollution technology change mult table
    NRCMT    --     resource technology change mult table
    SFNT     39.1   social family size normal table
    UILPCT   117.1  urban and industrial land required per capita table
    ## Not in 2003
    AHLM   145  assimilation half life multiplier
    CUF     83  capacity utilization fraction
    CMPLE   36  completed multiplier from perceived lifetime
    CMI     27  crowding multiplier from industry
    DCPH    97  development cost per hectare
    FRSN    41  family response to social norm
    FM      34  fecundity multiplier
    FCE     45  fertility control effectiveness
    FIALD  108  fraction of agricultural inputs allocated to land development
    FALM   126  fraction of agricultural inputs for land maintenance
    FCAOR  134  fraction of industrial capital allocated to obtaining resources
    FIOAA   93  fraction of industrial output allocated to agriculture
    FIOAS   63  fraction of industrial output allocated to services
    FPU     26  fraction of population urban
    FSAFC   48  fraction services allocated to fertility control
    HSAPC   21  health services per capita
    IFPC    89  indicated food per capita
    ISOPC   60  indicated services output per capita
    JPH     79  jobs per hectare
    JPICU   75  jobs per industrial capital unit
    JPSCU   77  jobs per service capital unit
    LFDR   122  land fertility degredation rate
    LFRT   125  land fertility regeneration time
    LLMY   113  land life multiplier from land yield
    LYMAP  105  land yield multiplier from air pollution
    LYMC   102  land yield multiplier from capital
    LMF     20  lifetime multiplier from food
    LMHS    23  lifetime multiplier from health services
    LMP     29  lifetime multiplier from persistent pollution
    MLYMC  111  marginal land yield multiplier from capital
    M1       4  mortality 0 to 14
    M2       8  mortality 15 to 44
    M3      12  mortality 45 to 64
    M4      16  mortality 65 plus
    PCRUM  132  per capita resource use multiplier
    SFN     39  social family size normal
    UILPC  117  urban and industrial land required per capita
    ## In both
    # state
    LV1#0 not found in 2003 version
    LV2#0 not found in 2003 version
    LV3#0 not found in 2003 version
    LV4#0 not found in 2003 version
    LV5#0 not found in 2003 version
    LV6#0 not found in 2003 version
    LV7#0 not found in 2003 version
    LV8#0 not found in 2003 version
    LV9#0 not found in 2003 version
    ## New
    LYF2   104.2  land yield factor 2
    LYTD   --     Land Yield Technology
    PPAPR  141    persistent pollution appearance rate
    PPGF2  138.2  persistent pollution generation factor 2
    PTD    --     Persistent Pollution Technology
    NRTD   --     Resource Conservation Technology
    NRUF2  131.2  resource use fact 2
    ## Not in 2003
    ## In both
    AI      99  Agricultural Inputs
    AL      85  Arable Land
    AIOPC   43  average industrial output per capita
    DIOPC   40  delayed industrial output per capita
    LUFD    82  Delayed Labor Utilization Fraction
    EHSPC   22  effective health services per capita
    FCFPC   46  fertility control facilities per capita
    IC      52  Industrial Capital
    LFERT  121  Land Fertility
    NR     129  Nonrenewable Resources
    PFR    128  Perceived Food Ratio
    PLE     37  perceived life expectancy
    PPOL   142  Persistent Pollution
    P1       2  Population 0 To 14
    P2       6  Population 15 To 44
    P3      10  Population 45 To 64
    P4      14  Population 65 Plus
    PAL     86  Potentially Arable Land
    SC      67  Service Capital
    UIL    120  Urban and Industrial Land
 %% Cell type:code id: tags:
 ``` python
 display(Markdown('## Constant dependencies (or dependencies without dependencies) or components'))
 table = []
 key_table = []
 for k in sorted([k for k, v in p_db.items()
 if v['type'] == 'component']):
    for c_id in p_db[k]['dependencies']:
        if p_db[c_id]['type'] == 'constant' or p_db[c_id]['dependencies'] == None or len(p_db[c_id]['dependencies']) == 0:
            key_table.append(c_id)
 key_table = set(key_table)
 for c_id in sorted(key_table):
    table.append([p_db[c_id]["abbr"], p_db[c_id]["code"], p_db[c_id]['name']])
 print(tabulate.tabulate(list(table), tablefmt='plain'))
 ```
 %% Output
    ## Constant dependencies (or dependencies without dependencies) or components
    AMTI     140.2  agricultural material toxicity index
    ARPTM    --     air pollution policy implementation time
    AHL70    146.1  assimilation half life in 1970
    AHLMT    145.1  assimilation half life mult table
    ALAI1    100.1  average life of agricultural inputs 1
    ALAI2    100.2  average life of agricultural inputs 2
    ALIC1    54.1   average life of industrial capital 1
    ALIC2    54.2   average life of industrial capital 2
    ALLN     112.1  average life of land normal
    ALSC1    69.1   average life of service capital 1
    ALSC2    69.2   average life of service capital 2
    CUFT     83.2   capacity utilization fraction table
    CMPLET   36.1   completed multiplier from perceived lifetime table
    CMIT     27.1   crowding multiplier from industry table
    DCFSN    38.2   desired completed family size normal
    DFR      --     desired food ratio
    DPOLX    --     desired persistent pollution index
    DNRUR    --     desired resource use rate
    DCPHT    97.1   development cost per hectare table
                    Education Index LOOKUP
    FRSNT    41.1   family response to social norm table
    FMT      34.1   fecundity multiplier table
    FCET     45.2   fertility control effectiveness table
    FCEST    45.1   fertility control effectiveness time
    FIOAA1T  94.1   fraction industrial output allocated to agriculture table 1
    FIOAA2T  95.1   fraction industrial output allocated to agriculture table 2
    FIALDT   108.1  fraction of agricultural inputs allocated to land development table
    FALMT    126.1  fraction of agricultural inputs for land maintenance table
    FIPM     140.1  fraction of agricultural inputs from persistent materials
    FCAOR1T  135.1  fraction of capital allocated to obtaining resources 1 table
    FCAOR2T  136.1  fraction of capital allocated to obtaining resources 2 table
    FCAORTM  --     fraction of industrial capital allocated to obtaining resources switch time
    FIOAC1   58.1   fraction of industrial output allocated to consumption constant 1
    FIOAC2   58.2   fraction of industrial output allocated to consumption constant 2
    FIOACVT  59.1   fraction of industrial output allocated to consumption variable table
    FIOAS1T  64.1   fraction of industrial output allocated to services table 1
    FIOAS2T  65.1   fraction of industrial output allocated to services table 2
    FPUT     26.1   fraction of population urban table
    FRPM     139.1  fraction of resources from persistent materials
    FSAFCT   48.1   fraction services allocated to fertility control table
                    GDP pc unit
                    GDP per capita LOOKUP
                    ha per Gha
                    ha per unit of pollution
    HSAPCT   21.1   health services per capita table
    IO70     107.2  IND OUT IN 1970
    IFPC1T   90.1   indicated food per capita table 1
    IFPC2T   90.2   indicated food per capita table 2
    ISOPC1T  61.1   indicated services output per capita table 1
    ISOPC2T  62.2   indicated services output per capita table 2
    ICOR1    51.1   industrial capital output ratio 1
    COPMT    --     industrial capital output ratio multiplier from pollution table
    ICOR2TT  --     industrial capital output ratio multiplier from resource table
    COYMT    --     industrial capital output ratio multiplier table
    IET      57.1   industrial equilibrium time
    IMEF     139.2  industrial material emissions factor
    IMTI     139.3  industrial material toxicity index
    IOPCD    59.2   industrial output per capita desired
    ILF      124.1  inherent land fertility
    NRI      129.2  initial nonrenewable resources
    JPHT     79.1   jobs per hectare table
    JPICUT   75.1   jobs per industrial capital unit table
    JPSCUT   77.1   jobs per service capital unit table
    LFPF     80.1   labor force participation fraction
    LFDRT    122.1  land fertility degredation rate table
    LFRTT    125.1  land fertility regeneration time table
    LFH      87.1   land fraction harvested
    LLMY1T   114.1  land life multiplier from land yield table 1
    LLMY2T   115.1  land life multiplier from land yield table 2
    LLMYTM   --     land life policy implementation time
    LYF1     104.1  land yield factor 1
    LYMAP1T  106.1  land yield multipler from air pollution table 1
    LYMAP2T  107.1  land yield multipler from air pollution table 2
    LYMCT    102.1  land yield multiplier from capital table
    LYCMT    --     land yield technology change rate multiplier table
                    Life Expectancy Index LOOKUP
    LEN      19.1   life expectancy normal
    LMFT     20.1   lifetime multiplier from food table
    LMHS1T   24.1   lifetime multiplier from health services 1 table
    LMHS2T   25.1   lifetime multiplier from health services 2 table
    LMPT     29.1   lifetime multiplier from persistent pollution table
    MLYMCT   111.1  marginal land yield multiplier from capital table
    MTFN     33.1   maximum total fertility normal
    M1T      4.1    mortality 0 to 14 table
    M2T      8.1    mortality 15 to 44 table
    M2T      12.1   mortality 45 to 64 table
    M4T      16.1   mortality 65 plus table
                    one year
    PCRUMT   132.1  per capita resource use mult table
    PPGF1    138.1  persistent pollution generation factor 1
    PPOL70   143.1  persistent pollution in 1970
    POLGFMT  --     persistent pollution technology change mult table
    PYEAR    150.1  POLICY YEAR
    PET      30.2   population equilibrium time
    PALT     84.1   potentially arable land total
                    PRICE OF FOOD
    PL       87.2   processing loss
                    Ref Hi GDP
                    Ref Lo GDP
    RLT      30.1   reproductive lifetime
    NRCMT    --     resource technology change mult table
    NRUF1    131.1  resource use factor 1
    SCOR1    72.1   service capital output ratio 1
    SCOR2    72.2   service capital output ratio 2
    SD       109.1  social discount
    SFNT     39.1   social family size normal table
    SFPC     127.1  subsistence food per capita
                    THOUSAND
                    TIME STEP
                    Total Land
                    unit agricultural input
                    unit population
    UILDT    119.1  urban and industrial land development time
    UILPCT   117.1  urban and industrial land required per capita table
    ZPGT     38.1   zero population growth time
 %% Cell type:code id: tags:
 ``` python
 import json
-with open('world3-03_variables.json', 'w') as fp:
+with open(output_path, 'w') as fp:
    json.dump(p_db, fp, indent = 4, sort_keys = True)
 ```

--- a/parsers/W3_xmile_parser.ipynb
+++ b/parsers/W3_xmile_parser.ipynb
@@ -5,6 +5,17 @@
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
+   "source": [
+    "import os\n",
+    "xmile_file_path = os.path.join('..', 'models', 'WRLD3-03', 'world3.stmx')\n",
+    "w3_72_vars_lists_dir = os.path.join('..', 'models', 'WRLD3-72')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
   "source": [
    "from xml.dom import minidom\n",
    "import re\n",
@@ -26,8 +37,11 @@
    "        i.getElementsByTagName('eqn')[0].childNodes[0].nodeValue \n",
    "        for i in aux_items \n",
    "        if i.attributes['name'].value.endswith('_' + p)\n",
-    "    ][0]\n",
+    "    ]\n",
-    "    return re.sub(r'\\W', ' ', eqn).split(' ')\n",
+    "    if len(eqn) > 0:\n",
+    "        return re.sub(r'\\W', ' ', eqn[0]).split(' ')\n",
+    "    else:\n",
+    "        return 'no equation'\n",
    "\n",
    "def not_in_list(w3_03_names, w3_72_nos):\n",
    "    '''\n",
@@ -125,7 +139,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
@@ -344,7 +358,7 @@
    }
   ],
   "source": [
-    "xmldoc = minidom.parse('world3.stmx')\n",
+    "xmldoc = minidom.parse(xmile_file_path)\n",
    "\n",
    "aux_items = xmldoc.getElementsByTagName('aux')\n",
    "aux_vars_dict = item_list_to_names_values(aux_items)\n",
@@ -357,7 +371,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
@@ -437,6 +451,24 @@
       "\n",
       "* fert_cont_facil_pc_46\n",
       "\n",
+       "* \n",
+       "\n",
+       "* \n",
+       "\n",
+       "* \n",
+       "\n",
+       "* \n",
+       "\n",
+       "* \n",
+       "\n",
+       "* \n",
+       "\n",
+       "* \n",
+       "\n",
+       "* \n",
+       "\n",
+       "* \n",
+       "\n",
       "* lifet_perc_del_37, perc_life_expectancy_37\n"
      ],
      "text/plain": [
@@ -448,7 +480,7 @@
    }
   ],
   "source": [
-    "w3_72_s = import_w3_72_list('w3-72_state.txt')\n",
+    "w3_72_s = import_w3_72_list(os.path.join(w3_72_vars_lists_dir, 'w3-72_state.txt'))\n",
    "\n",
    "disp('Nombre de paramètres dans w3 72 : ' + str(len(w3_72_s)))\n",
    "\n",
@@ -467,7 +499,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
@@ -586,7 +618,7 @@
    }
   ],
   "source": [
-    "w3_72_l = import_w3_72_list('w3-72_tables.txt')\n",
+    "w3_72_l = import_w3_72_list(os.path.join(w3_72_vars_lists_dir, 'w3-72_table.txt'))\n",
    "\n",
    "disp('Nombre de paramètres dans w3 72 : ' + str(len(w3_72_l)))\n",
    "\n",
@@ -605,7 +637,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
@@ -723,7 +755,7 @@
    }
   ],
   "source": [
-    "w3_72_p = import_w3_72_list('w3-72_parameters.txt')\n",
+    "w3_72_p = import_w3_72_list(os.path.join(w3_72_vars_lists_dir, 'w3-72_parameter.txt'))\n",
    "\n",
    "def isfloat(value):\n",
    "    '''\n",

 %% Cell type:code id: tags:
 ``` python
+import os
+xmile_file_path = os.path.join('..', 'models', 'WRLD3-03', 'world3.stmx')
+w3_72_vars_lists_dir = os.path.join('..', 'models', 'WRLD3-72')
+```
+%% Cell type:code id: tags:
+``` python
 from xml.dom import minidom
 import re
 from functools import reduce
 def disp(text):
    '''
    Affiche du texte à partir de la source Markdown
    '''
    from IPython.display import Markdown
    return display(Markdown(text))
 def getVariablesByIdFrom(names_list, var_id):
    return filter(lambda a: a.endswith('_' + var_id), names_list)
 def getDependenciesOfVarByIdFrom(p):
    global aux_items
    eqn = [
        i.getElementsByTagName('eqn')[0].childNodes[0].nodeValue
        for i in aux_items
        if i.attributes['name'].value.endswith('_' + p)
-    ][0]
+    ]
-    return re.sub(r'\W', ' ', eqn).split(' ')
+    if len(eqn) > 0:
+        return re.sub(r'\W', ' ', eqn[0]).split(' ')
+    else:
+        return 'no equation'
 def not_in_list(w3_03_names, w3_72_nos):
    '''
    Retourne deux listes : les paramètres effectivement non trouvés, et une liste de tuples contenant
    le numéro des paramètres non trouvés en première position et une liste de ses dépendances en
    deuxième position SI les dépendances se trouvent dans la liste de W3 2003
    '''
    not_found = []
    has_dep = []
    found = list(
        map(
            lambda a: a.split('_')[-1],
            w3_03_names
        )
    )
    for p in w3_72_nos:
        if p not in found:
            all_dependencies = getDependenciesOfVarByIdFrom(p)
            deps = [_ for _ in all_dependencies if _ in w3_03_names]
            if len(deps):
                has_dep.append((p, deps))
            else:
                nf_name = ", ".join(getVariablesByIdFrom(all_names, p))
                not_found.append(nf_name)
    return not_found, has_dep
 def item_list_to_names_values(item_list):
    items_names = list([e.attributes['name'].value for e in item_list])
    items_values = list([e.getElementsByTagName('eqn')[0].childNodes[0].nodeValue for e in item_list])
    return dict(zip(items_names, items_values))
 def disp_item_list(var_dict, list_name = 'List', w3_72_nos = None):
    '''
    Affiche une liste d'Element DOM avec un titre list_name
    '''
    items_names = var_dict.keys()
    items_values = var_dict.values()
    disp('## ' + list_name + ' (' + str(len(items_names)) + ')')
    not_found = has_dep = None
    if w3_72_nos != None:
        def color_w3_72(p):
            '''
            Coloration en bleu des paramètres nouveaux dans W3-03 et en rouge de ceux qui sont dépendant
            de ceux sélectionnés dans l'AS de W3-72
            '''
            if w3_72_nos != None and p.split('_')[-1] in w3_72_nos:
                return p
            elif has_dep != None and p in deps:
                return '<span style="color: red">' + p + '</span>'
            else:
                return '<span style="color: blue">' + p + '</span>'
        not_found, has_dep = not_in_list(items_names, w3_72_nos)
        deps = sum([d[1] for d in has_dep], [])
        disp_items_names = map(color_w3_72, items_names)
    else:
        disp_items_names = items_names
    disp_items_values = items_values
    disp_items = list(zip(disp_items_names, disp_items_values))
    to_disp = reduce(lambda s, i: str(s) + '* ' + str(i[0]) + ' (' + str(i[1]) + ') \n', disp_items)
    disp(to_disp)
    return items_names, not_found, has_dep
 def import_w3_72_list(filename):
    '''
    Importe une liste de variables de W3-72 :
    sur chaque ligne, l'acronyme de la variable et son identifiant numérique séparés d'un espace
    retourne la liste des identifiants numériques
    '''
    w3_72_list_file = open(filename)
    w3_72_list = list(
        map(
            lambda a: a.split(" ")[1].split('.', 1)[0],
            filter(
                lambda a: a != '',
                w3_72_list_file.read().split('\n')
            )
        )
    )
    w3_72_list_file.close()
    return w3_72_list
 ```
 %% Cell type:code id: tags:
 ``` python
-xmldoc = minidom.parse('world3.stmx')
+xmldoc = minidom.parse(xmile_file_path)
 aux_items = xmldoc.getElementsByTagName('aux')
 aux_vars_dict = item_list_to_names_values(aux_items)
 aux_items_name = disp_item_list(aux_vars_dict)
 all_vars_dict = item_list_to_names_values(sum([xmldoc.getElementsByTagName('aux'), xmldoc.getElementsByTagName('stock'), xmldoc.getElementsByTagName('flow')], []))
 all_names = all_vars_dict.keys()
 #print(all_names)
 ```
 %% Output
    ## List (192)
    ('deaths_17', 'deaths_0_to_14_3+deaths_15_to_44_7+deaths_45_to_64_11+deaths_65_plus_15')* death_rate_18 (1000*deaths_17/population_1)
    * labor_force_partic_80 (.75)
    * population_1 (pop_0_to_14_2+pop_15_to_44_6+pop_45_to_64_10+pop_65_plus_14)
    * labor_force_80 ((pop_15_to_44_6+pop_45_to_64_10)*labor_force_partic_80)
    * mortal_65_plus_16 (life_expectancy_19)
    * mortal_45_to_64_12 (life_expectancy_19)
    * mortal_15_to_44_8 (life_expectancy_19)
    * mortal_0_to_14_4 (life_expectancy_19)
    * reproductive_lifetime_30 (30)
    * birth_rt_31 (1000*births_30/population_1)
    * total_fertility_32 (MIN(max_tot_fert_33, (max_tot_fert_33*(1-fert_cont_eff_45)+des_tot_fert_35*fert_cont_eff_45)))
    * life_expectancy_19 (life_expect_norm_19*lifet_mlt_food_20*lifet_mult_hlth_serv_23*lifet_mlt_ppoll_29*lifet_mlt_crowd_28)
    * life_expect_norm_19 (28)
    * lifet_mlt_ppoll_29 (ppoll_index_143)
    * lifet_mult_hlth_serv_23 (IF (time > 1940) then lifet_mlt_hlth_serv_2_25 else lifet_mlt_hlth_serv_1_24)
    * ind_out_pc_des_59 (400)
    * lifet_mlt_food_20 (food_pc__88/subsist_food_pc_127)
    * lifet_mlt_crowd_28 (1-(crowd_mult_ind_27*fr_pop_urban_26))
    * eff_hlth_serv_pc_22 (SMTH1(hlth_serv_al_pc_21,hlth_serv_impact_del_22))
    * hlth_serv_al_pc_21 (serv_out_pc_71)
    * crowd_mult_ind_27 (ind_out_pc_49)
    * fr_pop_urban_26 (population_1)
    * hlth_serv_impact_del_22 (20)
    * fert_cont_eff_45 (if (time > t_fert_cont_eff_time_45) then 1 else fert_cont_eff_table_45)
    * fert_cont_facil_pc_46 (SMTH3(fert_cont_al_pc_47,hlth_serv_impact_del_22))
    * des_compl_fam_size_38 (if ( time > t_zero_pop_grow_time_38) then 2.0 else (des_compl_fam_size_norm_38*fam_resp_to_soc_norm_41*soc_fam_size_norm_39))
    * fert_cont_al_pc_47 (fr_serv_al_fert_cont_48*serv_out_pc_71)
    * fam_income_expect_42 ((ind_out_pc_49-avg_ind_out_pc_43)/avg_ind_out_pc_43)
    * income_expect_avg_time_43 (3)
    * avg_ind_out_pc_43 (SMTH1(ind_out_pc_49,income_expect_avg_time_43))
    * fam_resp_to_soc_norm_41 (fam_income_expect_42)
    * soc_fam_size_norm_39 (del_ind_out_pc_40)
    * social_adj_del_40 (20)
    * del_ind_out_pc_40 (SMTH3(ind_out_pc_49,social_adj_del_40))
    * lifet_perc_del_37 (20)
    * perc_life_expectancy_37 (SMTH3(life_expectancy_19,lifet_perc_del_37))
    * compl_mlt_perc_lifet_36 (perc_life_expectancy_37)
    * max_tot_fert_norm_33 (12)
    * fecundity_mult_34 (life_expectancy_19)
    * need_for_fert_cont_44 ((max_tot_fert_33/des_tot_fert_35)-1)
    * fr_serv_al_fert_cont_48 (need_for_fert_cont_44)
    * des_tot_fert_35 (des_compl_fam_size_38*compl_mlt_perc_lifet_36)
    * max_tot_fert_33 (max_tot_fert_norm_33*fecundity_mult_34)
    * des_compl_fam_size_norm_38 (3.8)
    * s_avg_life_serv_cap_69 (if (time > t_policy_year_150) then p_avg_life_serv_cap_2_69 else p_avg_life_serv_cap_1_69)
    * s_avg_life_ind_cap_54 (if (time > t_policy_year_150) then p_avg_life_ind_cap_2_54 else p_avg_life_ind_cap_1_54)
    * s_fioa_serv_63 (if (time > t_policy_year_150) then p_fr_io_al_serv_2_65 else p_fr_io_al_serv_1_64)
    * service_output_70 ((service_capital_67*capacity_util_fr_83)/s_serv_cap_out_ratio_72)
    * s_serv_cap_out_ratio_72 (if (time > t_policy_year_150) then p_serv_cap_out_ratio_2_72 else p_serv_cap_out_ratio_1_72)
    * s_indic_serv_pc_60 (if (time > t_policy_year_150) then p_indic_serv_pc_2_62 else p_indic_serv_pc_1_61)
    * fioa_ind_56 ((1-s_fioa_agr_93-s_fioa_serv_63-s_fioa_cons_57))
    * s_fioa_cons_57 (if (time > t_ind_equil_time_57) then p_fioa_cons_var_59 else s_fioa_cons_const_58)
    * industrial_output_50 (industrial_capital_52*(1-s_fr_cap_al_obt_res_134)*capacity_util_fr_83/s_ind_cap_out_ratio_51)
    * s_ind_cap_out_ratio_51 (if (time > t_policy_year_150) then p_ind_cap_out_ratio_2_51 else p_ind_cap_out_ratio_1_51)
    * pot_jobs_ind_sector_74 (industrial_capital_52*jobs_per_ind_cap_unit_75)
    * jobs_per_ind_cap_unit_75 (ind_out_pc_49)
    * ind_out_pc_49 (industrial_output_50/population_1)
    * capacity_util_fr_83 (labor_util_fr_del_82)
    * serv_out_pc_71 (service_output_70/population_1)
    * jobs_per_serv_cap_unit_77 (serv_out_pc_71)
    * pot_jobs_in_serv_sector_76 (service_capital_67*jobs_per_serv_cap_unit_77)
    * jobs_73 (pot_jobs_ind_sector_74+pot_jobs_agr_sector_78+pot_jobs_in_serv_sector_76)
    * labor_util_fr_del_time_82 (2)
    * labor_util_fr_81 (jobs_73/labor_force_80)
    * pot_jobs_agr_sector_78 (jobs_per_hect_79*arable_land_85)
    * jobs_per_hect_79 (agr_inp_per_hect_101)
    * t_pop_equil_time_30 (4000)
    * lifet_mlt_hlth_serv_1_24 (eff_hlth_serv_pc_22)
    * lifet_mlt_hlth_serv_2_25 (eff_hlth_serv_pc_22)
    * fert_cont_eff_table_45 (fert_cont_facil_pc_46)
    * t_fert_cont_eff_time_45 (4000)
    * t_zero_pop_grow_time_38 (4000)
    * pc_res_use_mlt_132 (ind_out_pc_49)
    * s_nr_res_use_fact_131 (if (time > t_policy_year_150) then p_nr_res_use_fact_2_131 else p_nr_res_use_fact_1_131)
    * nr_res_fr_remain_133 (nr_resources_129/init(nr_resources_129))
    * s_fr_cap_al_obt_res_134 (if (time > t_fcaor_time) then p_fr_cap_al_obt_res_2_136 else p_fr_cap_al_obt_res_1_135)
    * s_ppoll_gen_fact_138 (if (time > t_policy_year_150) then p_ppoll_gen_fact_2_138 else p_ppoll_gen_fact_1_138)
    * ppoll_gen_rt_137 ((ppoll_gen_ind_139+ppoll_gen_agr_140)*s_ppoll_gen_fact_138)
    * ppoll_gen_ind_139 (pc_res_use_mlt_132*population_1*frac_res_pers_mtl_139*ind_mtl_emiss_fact_139*ind_mtl_toxic_index_139)
    * ind_mtl_emiss_fact_139 (.1)
    * ind_mtl_toxic_index_139 (10)
    * frac_res_pers_mtl_139 (.02)
    * fr_agr_inp_pers_mtl_140 (.001)
    * ppoll_gen_agr_140 (agr_inp_per_hect_101*arable_land_85*fr_agr_inp_pers_mtl_140*agr_mtl_toxic_index_140)
    * agr_mtl_toxic_index_140 (1)
    * ppoll_trans_del_141 (20)
    * assim_half_life_146 (assim_half_life_1970_146*assim_half_life_mlt_145)
    * assim_half_life_mlt_145 (ppoll_index_143)
    * ppoll_index_143 (pers_pollution_142/ppoll_in_1970_143)
    * ppoll_in_1970_143 (1.36e8)
    * assim_half_life_1970_146 (1.5)
    * land_fert_degr_rt_122 (ppoll_index_143)
    * inherent_land_fert_124 (600)
    * land_fert_regen_time_125 (p_fr_inp_for_land_maint_126)
    * p_fr_inp_for_land_maint_126 (perc_food_ratio_128)
    * food_ratio_127 (food_pc__88/subsist_food_pc_127)
    * subsist_food_pc_127 (230)
    * food_pc__88 (food_87/population_1)
    * food_87 (land_yield_103*arable_land_85*land_fr_harvested_87*(1-processing_loss_87))
    * land_fr_harvested_87 (.7)
    * processing_loss_87 (.1)
    * agr_inp_per_hect_101 (agr_inp_99*(1-p_fr_inp_for_land_maint_126)/arable_land_85)
    * marg_land_yield_mlt_cap_111 (agr_inp_per_hect_101)
    * land_yield_mlt_cap_102 (agr_inp_per_hect_101)
    * urb_ind_land_dev_time_119 (10)
    * avg_life_land_112 (avg_life_land_norm_112*s_land_life_mlt_yield_113)
    * avg_life_land_norm_112 (1000)
    * s_land_life_mlt_yield_113 (if (time > t_land_life_time) then .95^(time-t_land_life_time)*p_land_life_mlt_yield_1_114 + (1-.95^(time-t_land_life_time))*p_land_life_mlt_yield_2_115 else p_land_life_mlt_yield_1_114)
    * land_yield_103 (s_land_yield_fact_104*land_fertility_121*land_yield_mlt_cap_102*s_yield_mlt_air_poll_105)
    * s_land_yield_fact_104 (if (time > t_policy_year_150) then p_land_yield_fact_2_104 else p_land_yield_fact_1_104)
    * s_yield_mlt_air_poll_105 (if (time > t_air_poll_time) then p_yield_mlt_air_poll_2_107 else p_yield_mlt_air_poll_1_106)
    * urb_ind_land_req_118 (urb_ind_land_pc_117*population_1)
    * dev_cost_per_hect_97 (pot_arable_land_86/pot_arable_land_tot_84)
    * pot_arable_land_tot_84 (3.2e9)
    * marg_prod_land_dev_109 (land_yield_103/(dev_cost_per_hect_97*social_discount_109))
    * social_discount_109 (.07)
    * land_fr_cult_84 (arable_land_85/pot_arable_land_tot_84)
    * fr_inp_al_land_dev_108 (marg_prod_land_dev_109/marg_prod_agr_inp_110)
    * marg_prod_agr_inp_110 (s_avg_life_agr_inp_100*land_yield_103*marg_land_yield_mlt_cap_111/land_yield_mlt_cap_102)
    * current_agr_inp_98 (tot_agric_invest_92*(1-fr_inp_al_land_dev_108))
    * tot_agric_invest_92 (industrial_output_50*s_fioa_agr_93)
    * s_avg_life_agr_inp_100 (if (time > t_policy_year_150) then p_avg_life_agr_inp_2_100 else p_avg_life_agr_inp_1_100)
    * urb_ind_land_pc_117 (ind_out_pc_49)
    * s_indic_food_pc_89 (if (time > t_policy_year_150) then p_indic_food_pc_2_91 else p_indic_food_pc_1_90)
    * p_fr_io_al_agr_1_94 (food_pc__88/s_indic_food_pc_89)
    * food_short_perc_del_128 (2)
    * p_fr_io_al_agr_2_95 (food_pc__88/s_indic_food_pc_89)
    * s_fioa_agr_93 (if (time > t_policy_year_150) then p_fr_io_al_agr_2_95 else p_fr_io_al_agr_1_94)
    * p_indic_food_pc_1_90 (ind_out_pc_49)
    * p_indic_food_pc_2_91 (ind_out_pc_49)
    * p_yield_mlt_air_poll_1_106 (industrial_output_50/ind_out_in_1970_107)
    * p_yield_mlt_air_poll_2_107 (industrial_output_50/ind_out_in_1970_107)
    * p_land_life_mlt_yield_1_114 (land_yield_103/inherent_land_fert_124)
    * p_land_life_mlt_yield_2_115 (land_yield_103/inherent_land_fert_124)
    * p_fr_cap_al_obt_res_1_135 (nr_res_fr_remain_133)
    * p_fr_cap_al_obt_res_2_136 (nr_res_fr_remain_133)
    * p_indic_serv_pc_1_61 (ind_out_pc_49)
    * p_indic_serv_pc_2_62 (ind_out_pc_49)
    * p_fr_io_al_serv_1_64 (serv_out_pc_71/s_indic_serv_pc_60)
    * p_fr_io_al_serv_2_65 (serv_out_pc_71/s_indic_serv_pc_60)
    * s_fioa_cons_const_58 (if (time > t_policy_year_150) then p_fioa_cons_const_2_58 else p_fioa_cons_const_1_58)
    * p_fioa_cons_var_59 (ind_out_pc_49/ind_out_pc_des_59)
    * t_ind_equil_time_57 (4000)
    * p_ind_cap_out_ratio_1_51 (3)
    * p_ind_cap_out_ratio_2_51 (ind_cap_out_ratio_2_ICOR2T*yield_tech_mult_icor_COYM*ppoll_tech_mult_icor_COPM)
    * t_policy_year_150 (4000)
    * p_avg_life_ind_cap_1_54 (14)
    * p_avg_life_ind_cap_2_54 (14)
    * p_fioa_cons_const_1_58 (.43)
    * p_fioa_cons_const_2_58 (.43)
    * p_avg_life_serv_cap_2_69 (20)
    * p_avg_life_serv_cap_1_69 (20)
    * p_serv_cap_out_ratio_1_72 (1)
    * p_serv_cap_out_ratio_2_72 (1)
    * p_avg_life_agr_inp_1_100 (2)
    * p_avg_life_agr_inp_2_100 (2)
    * p_land_yield_fact_1_104 (1)
    * p_land_yield_fact_2_104 (SMTH3(yield_tech_LYTD,tech_dev_del_TDD))
    * p_nr_res_use_fact_1_131 (1)
    * p_nr_res_use_fact_2_131 (SMTH3(res_tech_NRTD,tech_dev_del_TDD))
    * p_ppoll_gen_fact_1_138 (1)
    * p_ppoll_gen_fact_2_138 (SMTH3(ppoll_tech_PTD,tech_dev_del_TDD))
    * tech_dev_del_TDD (20)
    * p_res_tech_chg_mlt_NRCM (1-nr_res_use_rate_130/des_res_use_rt_DNRUR)
    * des_res_use_rt_DNRUR (4.8e9)
    * ind_cap_out_ratio_2_ICOR2T (s_nr_res_use_fact_131)
    * ind_out_in_1970_107 (7.9e11)
    * p_ppoll_tech_chg_mlt_POLGFM (1-ppoll_index_143/des_ppoll_index_DPOLX)
    * des_ppoll_index_DPOLX (1.2)
    * ppoll_tech_mult_icor_COPM (s_ppoll_gen_fact_138)
    * p_yield_tech_chg_mlt_LYCM (des_food_ratio_DFR-food_ratio_127)
    * des_food_ratio_DFR (2)
    * yield_tech_mult_icor_COYM (s_land_yield_fact_104)
    * t_land_life_time (4000)
    * t_air_poll_time (4000)
    * t_fcaor_time (4000)
    * res_intens (nr_res_use_rate_130/industrial_output_50)
    * poll_intens_ind (ppoll_gen_ind_139*s_ppoll_gen_fact_138/industrial_output_50)
    * cons_ind_out (industrial_output_50*s_fioa_cons_57)
    * cons_ind_out_pc (cons_ind_out/population_1)
    * Histpop (TIME)
    * HWI_Human_Welfare_Index ((Life_Expectancy_Index+Education_Index+GDP_Index)/3)
    * Life_Expectancy_Index (life_expectancy_19)
    * GDP_Index ((LOG10(GDP_per_capita_2)-LOG10(24))/(LOG10(9508)-LOG10(24)))
    * GDP_per_capita_2 (ind_out_pc_49)
    * Education_Index (GDP_per_capita_2)
    * Arable_Land_in_Gha (arable_land_85/1e9)
    * HEF_Human_Ecological_Footprint ((Arable_Land_in_Gha+Urban_Land_in_Gha+Absorption_Land_in_Gha)/1.91)
    * Gha_per_unit_of_pollution (4/1e9)
    * Absorption_Land_in_Gha (ppoll_gen_rt_137*Gha_per_unit_of_pollution)
    * Urban_Land_in_Gha (urban_ind_land_120/1e9)
 %% Cell type:code id: tags:
 ``` python
-w3_72_s = import_w3_72_list('w3-72_state.txt')
+w3_72_s = import_w3_72_list(os.path.join(w3_72_vars_lists_dir, 'w3-72_state.txt'))
 disp('Nombre de paramètres dans w3 72 : ' + str(len(w3_72_s)))
 state_vars_dict = item_list_to_names_values(xmldoc.getElementsByTagName('stock'))
 # Affiche les lookup tables de W3-03 et colore en bleu les nouveaux par rapport à W3-72
 state_names, s_not_found, s_has_dep = disp_item_list(state_vars_dict, 'State', w3_72_s)
 # Liste des paramètres présents dans W3 72 mais absents de W3 03
 disp('## Not in new state variables')
 if len(s_not_found):
    disp('\n'.join([('* ' + n + '\n') for n in s_not_found]))
 else:
    disp('Tout a été trouvé !')
 ```
 %% Output
    Nombre de paramètres dans w3 72 : 29
    ## State (18)
    ('pop_0_to_14_2', '65E7')* pop_15_to_44_6 (70E7)
    * pop_45_to_64_10 (19E7)
    * pop_65_plus_14 (6E7)
    * service_capital_67 (1.44E11)
    * industrial_capital_52 (2.1E11)
    * labor_util_fr_del_82 (1)
    * nr_resources_129 (1e12)
    * pers_pollution_142 (2.5e7)
    * land_fertility_121 (600)
    * agr_inp_99 (5e9)
    * arable_land_85 (.9e9)
    * pot_arable_land_86 (2.3e9)
    * urban_ind_land_120 (8.2e6)
    * perc_food_ratio_128 (1)
    * <span style="color: blue">res_tech_NRTD</span> (1)
    * <span style="color: blue">ppoll_tech_PTD</span> (1)
    * <span style="color: blue">yield_tech_LYTD</span> (1)
    ## Not in new state variables
    * income_expect_avg_time_43, avg_ind_out_pc_43
    * social_adj_del_40, del_ind_out_pc_40
    * eff_hlth_serv_pc_22, hlth_serv_impact_del_22
    * fert_cont_facil_pc_46
+    *
+    *
+    *
+    *
+    *
+    *
+    *
+    *
+    *
    * lifet_perc_del_37, perc_life_expectancy_37
 %% Cell type:code id: tags:
 ``` python
-w3_72_l = import_w3_72_list('w3-72_tables.txt')
+w3_72_l = import_w3_72_list(os.path.join(w3_72_vars_lists_dir, 'w3-72_table.txt'))
 disp('Nombre de paramètres dans w3 72 : ' + str(len(w3_72_l)))
 lookup_vars_dict = item_list_to_names_values([e for e in aux_items if len(e.getElementsByTagName('gf'))])
 # Affiche les lookup tables de W3-03 et colore en bleu les nouveaux par rapport à W3-72
 lookup_names, l_not_found, l_has_dep = disp_item_list(lookup_vars_dict, 'Lookup', w3_72_l)
 # Liste des paramètres présents dans W3 72 mais absents de W3 03
 disp('# Not in new parameters')
 if len(l_not_found):
    [disp('* ' + n + '\n') for n in l_not_found]
 else:
    disp('Tout a été trouvé !')
 ```
 %% Output
    Nombre de paramètres dans w3 72 : 37
    ## Lookup (56)
    ('mortal_65_plus_16', 'life_expectancy_19')* mortal_45_to_64_12 (life_expectancy_19)
    * mortal_15_to_44_8 (life_expectancy_19)
    * mortal_0_to_14_4 (life_expectancy_19)
    * lifet_mlt_ppoll_29 (ppoll_index_143)
    * lifet_mlt_food_20 (food_pc__88/subsist_food_pc_127)
    * hlth_serv_al_pc_21 (serv_out_pc_71)
    * crowd_mult_ind_27 (ind_out_pc_49)
    * fr_pop_urban_26 (population_1)
    * fam_resp_to_soc_norm_41 (fam_income_expect_42)
    * soc_fam_size_norm_39 (del_ind_out_pc_40)
    * compl_mlt_perc_lifet_36 (perc_life_expectancy_37)
    * fecundity_mult_34 (life_expectancy_19)
    * fr_serv_al_fert_cont_48 (need_for_fert_cont_44)
    * jobs_per_ind_cap_unit_75 (ind_out_pc_49)
    * capacity_util_fr_83 (labor_util_fr_del_82)
    * jobs_per_serv_cap_unit_77 (serv_out_pc_71)
    * jobs_per_hect_79 (agr_inp_per_hect_101)
    * <span style="color: red">lifet_mlt_hlth_serv_1_24</span> (eff_hlth_serv_pc_22)
    * <span style="color: red">lifet_mlt_hlth_serv_2_25</span> (eff_hlth_serv_pc_22)
    * fert_cont_eff_table_45 (fert_cont_facil_pc_46)
    * pc_res_use_mlt_132 (ind_out_pc_49)
    * assim_half_life_mlt_145 (ppoll_index_143)
    * land_fert_degr_rt_122 (ppoll_index_143)
    * land_fert_regen_time_125 (p_fr_inp_for_land_maint_126)
    * p_fr_inp_for_land_maint_126 (perc_food_ratio_128)
    * marg_land_yield_mlt_cap_111 (agr_inp_per_hect_101)
    * land_yield_mlt_cap_102 (agr_inp_per_hect_101)
    * dev_cost_per_hect_97 (pot_arable_land_86/pot_arable_land_tot_84)
    * fr_inp_al_land_dev_108 (marg_prod_land_dev_109/marg_prod_agr_inp_110)
    * urb_ind_land_pc_117 (ind_out_pc_49)
    * <span style="color: red">p_fr_io_al_agr_1_94</span> (food_pc__88/s_indic_food_pc_89)
    * <span style="color: red">p_fr_io_al_agr_2_95</span> (food_pc__88/s_indic_food_pc_89)
    * <span style="color: red">p_indic_food_pc_1_90</span> (ind_out_pc_49)
    * <span style="color: red">p_indic_food_pc_2_91</span> (ind_out_pc_49)
    * <span style="color: red">p_yield_mlt_air_poll_1_106</span> (industrial_output_50/ind_out_in_1970_107)
    * <span style="color: red">p_yield_mlt_air_poll_2_107</span> (industrial_output_50/ind_out_in_1970_107)
    * <span style="color: red">p_land_life_mlt_yield_1_114</span> (land_yield_103/inherent_land_fert_124)
    * <span style="color: red">p_land_life_mlt_yield_2_115</span> (land_yield_103/inherent_land_fert_124)
    * <span style="color: red">p_fr_cap_al_obt_res_1_135</span> (nr_res_fr_remain_133)
    * <span style="color: red">p_fr_cap_al_obt_res_2_136</span> (nr_res_fr_remain_133)
    * <span style="color: red">p_indic_serv_pc_1_61</span> (ind_out_pc_49)
    * <span style="color: red">p_indic_serv_pc_2_62</span> (ind_out_pc_49)
    * <span style="color: red">p_fr_io_al_serv_1_64</span> (serv_out_pc_71/s_indic_serv_pc_60)
    * <span style="color: red">p_fr_io_al_serv_2_65</span> (serv_out_pc_71/s_indic_serv_pc_60)
    * <span style="color: blue">p_fioa_cons_var_59</span> (ind_out_pc_49/ind_out_pc_des_59)
    * <span style="color: blue">p_res_tech_chg_mlt_NRCM</span> (1-nr_res_use_rate_130/des_res_use_rt_DNRUR)
    * <span style="color: blue">ind_cap_out_ratio_2_ICOR2T</span> (s_nr_res_use_fact_131)
    * <span style="color: blue">p_ppoll_tech_chg_mlt_POLGFM</span> (1-ppoll_index_143/des_ppoll_index_DPOLX)
    * <span style="color: blue">ppoll_tech_mult_icor_COPM</span> (s_ppoll_gen_fact_138)
    * <span style="color: blue">p_yield_tech_chg_mlt_LYCM</span> (des_food_ratio_DFR-food_ratio_127)
    * <span style="color: blue">yield_tech_mult_icor_COYM</span> (s_land_yield_fact_104)
    * <span style="color: blue">Histpop</span> (TIME)
    * <span style="color: blue">Life_Expectancy_Index</span> (life_expectancy_19)
    * <span style="color: blue">GDP_per_capita_2</span> (ind_out_pc_49)
    * <span style="color: blue">Education_Index</span> (GDP_per_capita_2)
    # Not in new parameters
    Tout a été trouvé !
 %% Cell type:code id: tags:
 ``` python
-w3_72_p = import_w3_72_list('w3-72_parameters.txt')
+w3_72_p = import_w3_72_list(os.path.join(w3_72_vars_lists_dir, 'w3-72_parameter.txt'))
 def isfloat(value):
    '''
    Renvoi vrai si une chaîne de caractère peut être identifiée comme un flottant
    Utile pour identifier les constantes dans le fichier de W3-03
    '''
    try:
        float(value)
        return True
    except ValueError:
        return False
 parameters_items = [
    e for e in aux_items
    if len(e.getElementsByTagName('eqn'))
    and isfloat(e.getElementsByTagName('eqn')[0].childNodes[0].nodeValue)
 ]
 disp('Nombre de paramètres dans w3 72 : ' + str(len(w3_72_p)))
 parameters_vars = item_list_to_names_values(parameters_items)
 # Affiche les parameters tables de W3-03 et colore en bleu les nouveaux par rapport à W3-72
 parameters_names, p_not_found, p_has_dep = disp_item_list(parameters_vars, 'Parameters', w3_72_p)
 # Liste des paramètres présents dans W3 72 mais absents de W3 03
 disp('## Not in new parameters')
 if len(l_not_found):
    [disp('* ' + n + '\n') for n in p_not_found]
 else:
    disp('Tout a été trouvé !')
 ```
 %% Output
    Nombre de paramètres dans w3 72 : 37
    ## Parameters (55)
    ('labor_force_partic_80', '.75')* reproductive_lifetime_30 (30)
    * life_expect_norm_19 (28)
    * ind_out_pc_des_59 (400)
    * hlth_serv_impact_del_22 (20)
    * income_expect_avg_time_43 (3)
    * social_adj_del_40 (20)
    * lifet_perc_del_37 (20)
    * max_tot_fert_norm_33 (12)
    * des_compl_fam_size_norm_38 (3.8)
    * labor_util_fr_del_time_82 (2)
    * t_pop_equil_time_30 (4000)
    * <span style="color: blue">t_fert_cont_eff_time_45</span> (4000)
    * t_zero_pop_grow_time_38 (4000)
    * ind_mtl_emiss_fact_139 (.1)
    * ind_mtl_toxic_index_139 (10)
    * frac_res_pers_mtl_139 (.02)
    * fr_agr_inp_pers_mtl_140 (.001)
    * agr_mtl_toxic_index_140 (1)
    * ppoll_trans_del_141 (20)
    * ppoll_in_1970_143 (1.36e8)
    * assim_half_life_1970_146 (1.5)
    * inherent_land_fert_124 (600)
    * subsist_food_pc_127 (230)
    * land_fr_harvested_87 (.7)
    * processing_loss_87 (.1)
    * urb_ind_land_dev_time_119 (10)
    * avg_life_land_norm_112 (1000)
    * pot_arable_land_tot_84 (3.2e9)
    * social_discount_109 (.07)
    * food_short_perc_del_128 (2)
    * t_ind_equil_time_57 (4000)
    * p_ind_cap_out_ratio_1_51 (3)
    * <span style="color: blue">t_policy_year_150</span> (4000)
    * <span style="color: blue">p_avg_life_ind_cap_1_54</span> (14)
    * <span style="color: blue">p_avg_life_ind_cap_2_54</span> (14)
    * <span style="color: blue">p_fioa_cons_const_1_58</span> (.43)
    * <span style="color: blue">p_fioa_cons_const_2_58</span> (.43)
    * p_avg_life_serv_cap_2_69 (20)
    * p_avg_life_serv_cap_1_69 (20)
    * p_serv_cap_out_ratio_1_72 (1)
    * p_serv_cap_out_ratio_2_72 (1)
    * p_avg_life_agr_inp_1_100 (2)
    * p_avg_life_agr_inp_2_100 (2)
    * p_land_yield_fact_1_104 (1)
    * p_nr_res_use_fact_1_131 (1)
    * p_ppoll_gen_fact_1_138 (1)
    * <span style="color: blue">tech_dev_del_TDD</span> (20)
    * <span style="color: blue">des_res_use_rt_DNRUR</span> (4.8e9)
    * ind_out_in_1970_107 (7.9e11)
    * <span style="color: blue">des_ppoll_index_DPOLX</span> (1.2)
    * <span style="color: blue">des_food_ratio_DFR</span> (2)
    * <span style="color: blue">t_land_life_time</span> (4000)
    * <span style="color: blue">t_air_poll_time</span> (4000)
    * <span style="color: blue">t_fcaor_time</span> (4000)
    ## Not in new parameters
    Tout a été trouvé !

--- a/models/Model Analysis with Vensim v4.pdf
+++ b/models/Model Analysis with Vensim v4.pdf