API is tested locally. You can get metadata, or travels, or pointcalls data.

Using postgres, portic_v3 with the user api_user, password = 'portic'. READ only user.

porticapi/README.md

@@ -40,7 +40,21 @@ Backend is running and serve the data on http://localhost:5004 (change the PORT
 
 ### 1.1.2. Running with Apache
 
-todo 
+```bash
+
+
+plumegeo@cchum-kvm-plume:~/navigo/Viz/porticapi$ git fetch origin master
+plumegeo@cchum-kvm-plume:~/navigo/Viz/porticapi$ git reset --hard origin/master
+plumegeo@cchum-kvm-plume:~/navigo/Viz/porticapi$ cd ..
+plumegeo@cchum-kvm-plume:~/navigo/Viz$ cp porticapi/porticapi/apidata.py porticapi/porticapi/__init__.py
+
+sudo chown :www-data /home/plumegeo/navigo/Viz/porticapi/ -R
+sudo chmod 755 /home/plumegeo/navigo/Viz/porticapi/ -R
+for fic in $(find /home/plumegeo/navigo/Viz/porticapi/porticapi -type f -name "*.py"); do sudo dos2unix $fic; done
+
+sudo  service apache2 reload
+```
+
 
 ### 1.1.3. Debug things
 
@@ -70,19 +84,10 @@ Si le parametre n'est pas précisé, le serveur opte pour la valeur de paramètr
 
 URL : **http://data.portic.fr/api/**
 
-```py
-def formatCSV2_deprecated(data):
-    dest = io.StringIO()
-    dest.write('[toto, 1, tutu]')
-    output = make_response(dest.getvalue())
-    #output = excel.make_response_from_array(data, 'csv')
-    output.headers["Content-Disposition"] = "attachment; filename=export.csv"
-    output.headers["Content-type"] = "text/csv"
-    return output
-```
+
 
 ### 1.2.2. /fieldnames?
---- TOUT en json
+--- TOUT en json ou csv
 
 - API = pointcalls | travels | **any**
 
@@ -114,6 +119,9 @@ Exemple
 
 #### 1.2.4.2. /travels?link_to_port="UHGS_id du port"&both-to=true
 
+TODO not done yet
+
+
 Donnerait les trajets passant par le port en question, et toutes les escales. 
 sans doublons par défaut
 avec doublons si both-to=true
@@ -123,6 +131,8 @@ avec doublons si both-to=true
 
 #### 1.2.4.3. /travels?link_to_port="UHGS_id du port"&degree=0&both-to=true
 
+TODO not done yet
+
 Donnerait les trajets entrants ou partants du port en question, 
 sans doublons, seulement les escales directement précédentes ou suivantes 
 sans doublons par défaut

porticapi/apidata.py

@@ -19,11 +19,13 @@ import csv
 import json
 import io
 import os
+import psycopg2 as pg
 
 #import flask_ext 
 #import flask_excel as excel
 #import pyexcel as pe
 
+
 APP_ROOT = os.path.dirname(os.path.abspath(__file__))   # refers to application_top
 APP_STATIC = os.path.join(APP_ROOT, 'static')
 APP_DATA = os.path.join(APP_STATIC, 'data')
@@ -34,34 +36,29 @@ CORS(app)
 #app.config.from_object('config')
 #port = app.config['PORT']      
 port = '80'
+postgresport = '8004'
 
-def isInside(lat, lon, radius, x, y):    
-    """
-    Will be computed by postgres at the end of the afternoon
-    """
-    #radius is in degree in formula   
-    if ((x - lat) * (x - lat) + 
-        (y - lon) * (y - lon) <= radius * radius): 
-        return True
-    else: 
-        return False
 
-def calcul_isInside(lat, lon, radius):
+def retrieveDataFromPostgres(query) : 
     """
-    Will be computed by postgres at the end of the afternoon
+    Internal method to select data using SQL query
+    return a dataframe
     """
+    import pandas.io.sql as psql
     import pandas as pd
-    df = pd.read_csv('static/data/travels_API_11mai2020.csv', sep = ';')
-    #radius in km is converted in radius in degree
-    calculated = df.apply(lambda row:isInside(lat,lon,radius/111.19492664455873,row.departure_latitude,row.departure_longitude), axis=1)
-    return df.loc[calculated]
-
+    #connection = pg.connect("host='134.158.33.179' port='5433' dbname='portic_v3' user='api_user' password='portic'")
+    connection = pg.connect("""host='localhost' port='%s' dbname='portic_v3' user='api_user' password='portic'"""% postgresport)
 
+    df = pd.read_sql_query(query,con=connection)
+    connection.close()
+    return df
+    #print(df)
 
 def formatCSV(mydataframe):
+    """
+    Internal method to output dataframe in a CSV file
+    """
     #print(mydataframe)
-    #sheet = pe.Sheet(data)
-    #sheet.save_to_memory("csv", dest)
 
     #Options de compression possibles to_csv 
     #https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_csv.html
@@ -75,16 +72,24 @@ def formatCSV(mydataframe):
     output.headers["Content-type"] = "text/csv"
     return output
 
-def formatJSON(data):
-    json_str = json.dumps(data.to_json(orient='records'))
+def formatJSON(dataframe):
+    """
+    Internal method to output dataframe as JSON
+    """
+    json_str = json.dumps(dataframe.to_json(orient='records'))
     return json.loads(json_str)
 
-def formatOutput(dfcsv):
+def formatOutput(dfcsv, api='travels'):
     """
-    Apply various formatting on output by processing request parameters
+    Internal method
+    Apply various formatting on dataframe for output by processing request parameters
     - format : csv | **json**
     - zipped : true | **false**
     - shortenfields : true | **false**
+    NB : 
+    - shortenfields : the shortname is based on the ordinal position of the attribute in the table 
+    thus it can change with living database
+    - zipped is not yet implemented
     """
 
     import pandas as pd
@@ -94,12 +99,13 @@ def formatOutput(dfcsv):
     #print('shortenfields ? '+shortenfields)
     if (shortenfields != None and shortenfields=='true') :
         #API;colname;short_colname
-        filename = os.path.join(APP_DATA, 'shorten_names.csv')
-        mapnames = pd.read_csv(filename, sep = ';') 
+        #filename = os.path.join(APP_DATA, 'shorten_names.csv')
+        #mapnames = pd.read_csv(filename, sep = ';') 
+        mapnames = readFieldnames(api)
         #Filter according API
-        mapnames = mapnames[mapnames['API']=='pointcalls']
+        #mapnames = mapnames[mapnames['API']=='pointcalls']
         #print(mapnames['colname'])
-        mapper=mapnames.loc[:, ['colname', 'short_colname'] ].set_index('colname')['short_colname'].to_dict()
+        mapper=mapnames.loc[:, ['name', 'shortname'] ].set_index('name')['shortname'].to_dict()
         #print(mapper) 
         dfcsv = dfcsv.rename(columns=mapper)
 
@@ -110,24 +116,64 @@ def formatOutput(dfcsv):
     else: 
         return formatJSON(dfcsv)
 
-@app.route('/api/fieldnames/')   
-def getFieldnames():
+def readFieldnames(api) :
     """
-    récupère des métadonnées sur l'API, avec la liste des attributs, avec leur nom court et long, leur type et leur signification. 
-    get metadata about API with short and long name, type and definition
+    Internal method
+    We read the information schema to be sure to be conform to real living database
+
+    Name of tables differ from API names.
+    We generate the shortname using the order of the attribute in the table 
+    (3 characters, beginning either with t if travels, either with p if pointcalls)
     """
     import pandas as pd
+
+    table_name = "pointcall','built_travels"
+    if api is not None  :
+        if api == 'travels' : 
+            table_name = 'built_travels'
+        if api == 'pointcalls' : 
+            table_name = 'pointcall'
+    
+    #API;name;shortname;type;description
+    query = """SELECT case when c.table_name= 'built_travels' then 'travels' else 'pointcalls' end as API, 
+        c.column_name as name, 
+        case when c.table_name= 'built_travels' then 't' else 'p' end||navigo.pystrip(to_char(c.ordinal_position::int, '09')) as shortname,
+        c.data_type as type, pgd.description as description
+        FROM information_schema.columns c 
+        left outer join pg_catalog.pg_description pgd on (pgd.objsubid=c.ordinal_position  )
+        left outer join pg_catalog.pg_statio_all_tables st on (pgd.objoid=st.relid and  c.table_schema=st.schemaname and c.table_name=st.relname)
+        where c.table_name in ('%s')  and c.table_schema = 'navigoviz' and pgd.objoid = st.relid;"""% (table_name)
+
+    #print(query)
+    metadata = retrieveDataFromPostgres(query)
+    return metadata
+    """
     filename = os.path.join(APP_DATA, 'api_portic.csv')
     dfcsv = pd.read_csv(filename, sep = ';')
     
-    # Filter to keep desired API
-    api = request.args.get("api")
-    #print(getduplicates)
     if api is not None  :
         #Filter to retain this API
         dfcsv = dfcsv[dfcsv['API']==api]
+    return  dfcsv   
+    """
+         
+@app.route('/api/fieldnames/')   
+def getFieldnames():
+    """
+    récupère des métadonnées sur l'API, avec la liste des attributs, avec leur nom court et long, leur type et leur signification. 
+    get metadata about API with short and long name, type and definition
+
+    http://127.0.0.1:80/api/fieldnames/?format=json
+    http://127.0.0.1/api/fieldnames/?format=json&shortenfields=true
+    http://127.0.0.1/api/fieldnames/?format=json&shortenfields=true&api=pointcalls
+    http://127.0.0.1/api/fieldnames/?format=csv&shortenfields=true&api=pointcalls
+
+    """
+    # Filter to keep desired API
+    api = request.args.get("api")
+    df = readFieldnames(api)
 
-    return formatOutput(dfcsv)
+    return formatOutput(df, api)
 
 @app.route('/api/pointcalls/')   
 def getPointcalls():
@@ -136,22 +182,29 @@ def getPointcalls():
     Will be extracted from postgres, schema navigoviz, table pointcall (see navigocorpus/ETL)
         - params : **all** | tableau des noms longs des attributs de l'API à renvoyer
 
-    http://127.0.0.1:5004/api/pointcalls/?params=pointcall,pointcall_uhgs_id&shortenfields=true
-    http://127.0.0.1:5004/api/pointcalls/?format=csv
+    http://127.0.0.1:80/api/pointcalls/?params=pointcall,pointcall_uhgs_id&shortenfields=true
+    http://127.0.0.1:80/api/pointcalls/?format=csv
+
+    http://127.0.0.1:80/api/pointcalls/?params=pointcall,pointcall_uhgs_id&shortenfields=false
+    http://127.0.0.1:80/api/pointcalls/?format=json&params=id,pointcall,ship_name,destination,destination_uhgs_id&shortenfields=true
 
     """
     import pandas as pd
-    filename = os.path.join(APP_DATA, 'pointcalls_API_11mai2020.csv')
-    dfcsv = pd.read_csv(filename, sep = ';')
+    #filename = os.path.join(APP_DATA, 'pointcalls_API_11mai2020.csv')
+    #dfcsv = pd.read_csv(filename, sep = ';')
+
     #https://stackoverflow.com/questions/24251219/pandas-read-csv-low-memory-and-dtype-options
     #pd.read_csv('static/data/pointcalls_API_11mai2020.csv', dtype={"all_cargos": object, "pkid": int})
     #print(dfcsv.columns)
-    
+
+    query = 'select * from navigoviz.pointcall'
+
     ## Filter the result according requested params
     params = request.args.get("params")
     #params=pointcall,pointcall_uhgs_id
     if (params is not None and len(params)>0) :
         #print('selecting some columns')
+        fields = readFieldnames('pointcalls')
         keepparams = str(params).split(',')
         #keepparams = ['pkid', 'pointcall', 'pointcall_uhgs_id']
         #https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike
@@ -159,12 +212,17 @@ def getPointcalls():
         #labels = dfcsv.index.intersection(keepparams)
         labels = []
         for k in keepparams:
-            if k in dfcsv.columns : 
+            #print(k)
+            if k in fields['name'].tolist() : 
                 labels.append(k)
         #print(labels)
-        dfcsv =  dfcsv.loc[:, labels]
+        attributes = ",".join(labels)
+        query = 'select '+attributes+' from navigoviz.pointcall'
+        #dfcsv =  dfcsv.loc[:, labels]
+    
+    dfcsv = retrieveDataFromPostgres(query)
 
-    return formatOutput(dfcsv)
+    return formatOutput(dfcsv, 'pointcalls')
 
 
 
@@ -175,34 +233,48 @@ def getTravels():
     Will be extracted from postgres, schema navigoviz, table built_travels (see navigocorpus/ETL), 
     but with a filter by default : only source_entry = from and both-from, to avoid duplicates
         - params : **all** | tableau des noms longs des attributs de l'API à renvoyer
+        params=pointcall,pointcall_uhgs_id for instance
         - both_to : true | **false**
 
     http://127.0.0.1:5004/api/travels/?format=csv&both_to=false
+    http://127.0.0.1:80/api/travels/?format=csv&both_to=true&shortenfields=true
 
+    http://127.0.0.1:80/api/travels/?format=json&params=id,departure,destination,destination_uhgs_id
+    http://127.0.0.1:80/api/travels/?format=json&params=id,departure,destination,destination_uhgs_id&shortenfields=true
     """
     import pandas as pd
-    filename = os.path.join(APP_DATA, 'travels_API_11mai2020.csv')
-    dfcsv = pd.read_csv(filename, sep = ';')
+    #filename = os.path.join(APP_DATA, 'travels_API_11mai2020.csv')
+    #dfcsv = pd.read_csv(filename, sep = ';')
     
+    query = 'select * from navigoviz.built_travels'
+
+
     ## Filter the result according requested params
     params = request.args.get("params")
-    #print(params)
-    if (params != None) :
-        #params=pointcall,pointcall_uhgs_id
-        keepparams = params.split(',') 
-        #keepparams = ['pkid', 'pointcall', 'pointcall_uhgs_id']
-        dfcsv = dfcsv.loc[:, keepparams]
+    if (params is not None and len(params)>0) :
+        #print('selecting some columns')
+        fields = readFieldnames('travels')
+        keepparams = str(params).split(',')
+        #https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike
+        labels = []
+        for k in keepparams:
+            if k in fields['name'].tolist() : 
+                labels.append(k)
+        attributes = ",".join(labels)
+        query = 'select '+attributes+' from navigoviz.built_travels'
 
 
     # Filter to remove duplicates (by default) - if both_to is given, then do not filter and travels will contain duplicates
     getduplicates = request.args.get("both_to")
-    #print(getduplicates)
     if getduplicates is None or getduplicates != 'true' :
         #Filter travels
         #print('filtering duplicates out of travels')
-        dfcsv = dfcsv[dfcsv['source_entry']!='both-to']
-    
-    return formatOutput(dfcsv)
+        #dfcsv = dfcsv[dfcsv['source_entry']!='both-to']
+        query = query + " where source_entry <> 'both-to'"
+        
+    dfcsv = retrieveDataFromPostgres(query)
+
+    return formatOutput(dfcsv, 'travels')
 
 
 
@@ -213,54 +285,122 @@ def getDeparturesDetails():
     Return the travels, at the departure of the points located in a 100 km radius neighbourhood from the lat/lon given in parameter
     Will be extracted from postgres, schema navigoviz, table built_travels (see navigocorpus/ETL), 
     but with a filter by default : only source_entry = from and both-from, to avoid duplicates
-    """
-    lat = float(request.args.get("lat"))
-    lon = float(request.args.get("lon"))
-    radius = int(request.args.get("radius"))
-    df1 = calcul_isInside(lat, lon, radius)
-
-    dfcsv = df1[['departure','departure_uhgs_id','departure_latitude','departure_longitude']].drop_duplicates()
-
-    return formatOutput(dfcsv)
 
+    http://localhost:80/api/details/departures?lat=46&lon=-1&radius=100
+    http://localhost/api/details/departures/?lat=45.2333&lon=-1.5&radius=100
+    http://localhost/api/details/departures/?lat=45.2333&lon=toto&radius=100
+    """
+    
+    lat = None
+    lon = None
+    radius = None
+    try:
+        lat = float(request.args.get("lat"))
+        lon = float(request.args.get("lon"))
+        radius = int(request.args.get("radius"))
+    except ValueError:
+        #We go on, silenciously, by selecting all data
+        pass
+
+    if lat != None and lon != None and radius!= None : 
+        query = """select distinct departure, departure_uhgs_id, departure_latitude, departure_longitude from navigoviz.built_travels 
+        where source_entry<> 'both-to' and
+        st_distance(departure_point, 
+        st_setsrid(st_transform(st_setsrid(st_makepoint(%f, %f), 4326), 3857), 3857)) < %f""" % (lon,lat,radius*1000)
+    else :
+        query = """select distinct departure, departure_uhgs_id, departure_latitude, departure_longitude 
+        from navigoviz.built_travels
+        where source_entry<> 'both-to'"""
+    #df1 = calcul_isInside(lat, lon, radius)
+    #dfcsv = df1[['departure','departure_uhgs_id','departure_latitude','departure_longitude']].drop_duplicates()
+    
+    #print(query)
+    dfcsv = retrieveDataFromPostgres(query)
 
-    #return json.loads(json.dumps(df1[['departure','departure_uhgs_id','departure_latitude','departure_longitude']].drop_duplicates().to_json(orient='records')))
+    return formatOutput(dfcsv, 'travels')
 
 @app.route('/api/agg/departures/', methods = ['GET']) 
 def getDeparturesAgg():
     """
-    Return the count of departures, for the points located in a 100 km radius neighbourhood from the lat/lon given in parameter
+    Return the count of departures, for the points located in a radius km neighbourhood from the lat/lon given in parameter
     Will be extracted from postgres, schema navigoviz, table built_travels (see navigocorpus/ETL), 
     but with a filter by default : only source_entry = from and both-from, to avoid duplicates
-    """
-    lat = float(request.args.get("lat"))
-    lon = float(request.args.get("lon"))
-    radius = int(request.args.get("radius"))
-    df2 = calcul_isInside(lat, lon, radius).departure.value_counts().reset_index()
-    df2.columns = ['departure', 'count']
 
+    http://localhost/api/agg/departures/?lat=45.2333&lon=-1&radius=100
+
+    """
+    lat = None
+    lon = None
+    radius = None
+    try:
+        lat = float(request.args.get("lat"))
+        lon = float(request.args.get("lon"))
+        radius = int(request.args.get("radius"))
+    except ValueError:
+        #We go on, silenciously, by selecting all data
+        pass
+    #df2 = calcul_isInside(lat, lon, radius).departure.value_counts().reset_index()
+    #df2.columns = ['departure', 'count']
+
+    if lat != None and lon != None and radius!= None : 
+        query = """select  departure, count(*) as count from navigoviz.built_travels  
+        where  source_entry<> 'both-to' and
+        st_distance(departure_point, st_setsrid(st_transform(st_setsrid(st_makepoint(%f, %f), 4326), 3857), 3857)) < %f 
+        group by departure""" % (lon,lat,radius*1000)
+    else :
+        query = """select departure, count(*) 
+        from navigoviz.built_travels 
+        where source_entry<> 'both-to' 
+        group by departure"""
+    #print(query)
+    df2 = retrieveDataFromPostgres(query)
     
-    return formatOutput(df2)
+    return formatOutput(df2, 'travels')
 
     #return json.loads(json.dumps(df2.to_json(orient='records')))   
 
 @app.route('/api/agg/destinations/', methods = ['GET'])   
 def getDestinationsAgg():
     """
-    Return the count of destinations, for the points located in a 100 km radius neighbourhood from the lat/lon given in parameter
+    Return the count of destination for each different admiralties, 
+    for the points located in a radius km  neighbourhood from the lat/lon given in parameter
     Will be extracted from postgres, schema navigoviz, table built_travels (see navigocorpus/ETL), 
     but with a filter by default : only source_entry = from and both-from, to avoid duplicates
+
+    http://localhost/api/agg/destinations/?lat=45.2333&lon=-1&radius=100
+
+
     """
-    lat = float(request.args.get("lat"))
-    lon = float(request.args.get("lon"))
-    radius = int(request.args.get("radius"))
-    df3 = calcul_isInside(lat, lon, radius).destination_amiraute.value_counts().reset_index()
-    df3.columns = ['label', 'value']
+    lat = None
+    lon = None
+    radius = None
+    try:
+        lat = float(request.args.get("lat"))
+        lon = float(request.args.get("lon"))
+        radius = int(request.args.get("radius"))
+    except ValueError:
+        #We go on, silenciously, by selecting all data
+        pass
+    
+    #df3 = calcul_isInside(lat, lon, radius).destination_amiraute.value_counts().reset_index()
+    #df3.columns = ['label', 'value']
+
+    if lat != None and lon != None and radius!= None : 
+        query = """select  destination_admiralty as label, count(*) as value from navigoviz.built_travels  
+        where source_entry<> 'both-to' 
+        and st_distance(departure_point, st_setsrid(st_transform(st_setsrid(st_makepoint(%f, %f), 4326), 3857), 3857)) < %f 
+        group by destination_admiralty""" % (lon,lat,radius*1000)
+    else :
+        query = """select destination_admiralty as label, count(*) as value 
+        from navigoviz.built_travels 
+        where source_entry<> 'both-to'  
+        group by destination_admiralty"""
+    #print(query)
+    df3 = retrieveDataFromPostgres(query)
     df3['id'] = df3['label']
 
-    return formatOutput(df3)
+    return formatOutput(df3, 'travels')
 
-    #return json.loads(json.dumps(df3.to_json(orient='records')))            
 
 
 

porticapi/porticapi.wsgi

@@ -4,4 +4,4 @@
 import sys
 import os, os.path
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '.')))
-from porticwebapi import app as application
+from porticapi import app as application

porticapi/static/data/api_portic.csv

@@ -2,13 +2,13 @@ API;name;shortname;Exemple;Type;Description
 pointcalls;pkid;p00;2;int;Identifiant du pointcall
 pointcalls;pointcall;p01;Saint Malo;text;Toponyme du port de départ
 pointcalls;pointcall_uhgs_id;p02;A0170819;text;Identifiant géogénéral du port de départ
-pointcalls;pointcall_latitude;p03;48.65;float;latitude
-pointcalls;pointcall_longitude;p04;-2.016667;float;longitude
+pointcalls;latitude;p03;48.65;float;latitude
+pointcalls;longitude;p04;-2.016667;float;longitude
 pointcalls;pointcall_admiralty;p05;Saint-Malo;text;amirauté d'appartenance du port
 pointcalls;pointcall_province;p06;Bretagne;text;province d'appartenance du port
 pointcalls;pointcall_states;p07;;json;"états d'appartenance structurés ainsi : un dictionnaire de couple de dates, état d'appartenance. Les 2 dates les plus extrêmes sont 1749 et 1815. Exemple : [{1749-1768 : République de Gênes}, {1768-1814 : France}]"
 pointcalls;pointcall_status;p08;siège d'amirauté;text;oblique | siège d'amirauté | rien
-pointcalls;pointcall_shiparea;p09;;text;Shipping area telle que calculée par J-P. Dedieu (mer ou ocean et région côtière du point)
+pointcalls;shiparea;p09;;text;Shipping area telle que calculée par J-P. Dedieu (mer ou ocean et région côtière du point)
 pointcalls;pointcall_point;p10;;text;WKT point en EPSG 3857
 pointcalls;pointcall_out_date;p11;;text;Date de sortie telle que écrite dans la source
 pointcalls;pointcall_action;p12;;text;In | In-Out | Out | Transit | Loading | Captured | …
@@ -16,13 +16,13 @@ pointcalls;outdate_fixed;p13;1787-11-10;date;Date transformée si renseignée de
 pointcalls;pointcall_in_date;p14;;text;Date d'arrivée telle que écrite dans la source
 pointcalls;indate_fixed;p15;;date;Date transformée si renseignée de l'observation à la destination : yyyy-MM-DD
 pointcalls;net_route_marker;p16;;text;A ou Z. Si Z, ne pas garder pour composer la route
-pointcalls;pointcall_fullrank;p17;;int;Rang de visite d'un port du navire, toutes sources confondues
+pointcalls;pointcall_rankfull;p17;;int;Rang de visite d'un port du navire, toutes sources confondues
 pointcalls;date_fixed;p18;;date;Date exacte de visite du port si connue (déduite de outdate et indate : coalesce(p.outdate_fixed, p.indate_fixed)
 pointcalls;navigo_status;p19;;text;"Passé, ou futur par rapport au point d’observation. Plus intention non realise “J’aurais voulu aller à Malte… mais j’ai été capturé avant » Voir http://navigocorpus.org/iDocuments/Documents/Brief%20manual%20of%20use%20for%20Navigocorpus%20database.pdf "
 pointcalls;pointcall_function;p20;;text;"Anciennement, O = point d’observation, T = Terminus (dernier point mentionné dans l’itinéraire futur déclaré), A  = premier point mentionné dans l’itinéraire passé. NB : ce champ n’est plus prévu pour la saisie. Le nouveau champ qui remplace est « data_block_leader_marker » et désormais O devient A ; T reste T.  A devient I ? AUCUN I vu dans la colonne. Que des O ou des A. Pas de T"
 pointcalls;ship_name;s00;Postillon;text;Nom du navire tel que lu dans la source
 pointcalls;ship_id;s01;0012023N;text;Codage pour identifier le navire uniquement à travers plusieurs déclarations dans les sources
-pointcalls;tonnage;s02;12;float;"Tonnage du navire, noté en réel (virgule pour décimales).  1 tonneau = 24 quintaux, donc les valeurs de Marseille exprimés généralement en quintaux doivent être converties en tonneaux en divisant par 24. Mail Silvia du 29 janvier 2020 : Oui mais En faisant attention que l'unité de mesure exprimée soit le quintal car on a également rapporté à Marseille les tonnages en tonneaux de navires identifiés dans d'autres ports."
+pointcalls;tonnage;s02;12;float;"Tonnage du navire, noté en réel (virgule pour décimales).  1 tonneau = 24 quintaux, donc les valeurs de Marseille exprimés généralement en quintaux doivent être converties en tonneaux en divisant par 24, mais en faisant attention que l'unité de mesure exprimée soit le quintal car on a également rapporté à Marseille les tonnages en tonneaux de navires identifiés dans d'autres ports."
 pointcalls;tonnage_unit;s03;;text;Unité du tonnage. 46 valeurs différentes.  A harmoniser
 pointcalls;flag;s04;French;text;Pavillon du navire, tel que lu dans la source --> afficher etat d'appartenance dans la viz (celui du homeport)
 pointcalls;class;s05;;text;Catégorie de navire (telle que lue dans la source) 
@@ -99,7 +99,7 @@ travels;destination_admiralty;d05;Quimper;text;amirauté d'appartenance du port
 travels;destination_province;d06;Bretagne;text;province d'appartenance du port
 travels;destination_states;d07;;json;"états d'appartenance structurés ainsi : un dictionnaire de couple de dates, état d'appartenance. Les 2 dates les plus extrêmes sont 1749 et 1815. Exemple : [{1749-1768 : République de Gênes}, {1768-1814 : France}]"
 travels;destination_status;d08;;text;oblique | siège d'amirauté
-travels;departure_shiparea;d09;;text;Shipping area telle que calculée par J-P. Dedieu (mer ou ocean et région côtière du point)
+travels;destination_shiparea;d09;;text;Shipping area telle que calculée par J-P. Dedieu (mer ou ocean et région côtière du point)
 travels;destination_point;d10;;text;WKT point en EPSG 3857
 travels;destination_action;d11;;text;In | In-Out | Out | Transit | Loading | Captured | … Si renseignée (Marseille)
 travels;destination_in_date;d12;;text;Date d'arrivée telle que écrite dans la source
@@ -116,7 +116,7 @@ travels;homeport;t10;Binic;text;Port d'attache du navire
 travels;homeport_uhgs_id;t11;A0127863;text;Identifiant géogénéral du port d'attache
 travels;homeport_latitude;t12;48.6;float;latitude
 travels;homeport_longitude;t13;-2.833333;float;longitude
-travels;homeport_amiraute;t14;Saint-Brieuc;text;amirauté d'appartenance du port
+travels;homeport_admiralty;t14;Saint-Brieuc;text;amirauté d'appartenance du port
 travels;homeport_province;t15;Bretagne;text;province d'appartenance du port
 travels;homeport_states;t16;;json;"états d'appartenance structurés ainsi : un dictionnaire de couple de dates, état d'appartenance. Les 2 dates les plus extrêmes sont 1749 et 1815. Exemple : [{1749-1768 : République de Gênes}, {1768-1814 : France}]"
 travels;homeport_status;t17;;text;oblique | siège d'amirauté