コード例 #1
0
def d3d(tmpdir,dic):
    #initialize a model
    rpath = str(tmpdir)+'/'
    dic.update({'rpath':rpath}) # use tmpdir for running the model
    b = pyPoseidon.model(**dic)

    try:
        b.execute()
        out = data.data(**dic)
        a = pyPoseidon.read_model(rpath+'d3d_model.json') # read model
        a.execute()
        out = data.data(**dic)
        return True
    except:
        return False
コード例 #2
0
ファイル: d3d.py プロジェクト: vvoukouvalas/pyPoseidon
    def get_data(self, **kwargs):

        dic = self.__dict__

        dic.update(kwargs)

        self.data = data(**dic)
コード例 #3
0
ファイル: test_d3d_cast.py プロジェクト: moghimis/pyPoseidon
def d3d(tmpdir,dic):
    #initialize a model
    rpath = str(tmpdir)
    dic.update({'rpath':rpath + '/20181001.00/'}) # use tmpdir for running the model
    b = pyPoseidon.model(**dic)

    b.execute()
    # Cast
    #read the info from the first run
    with open(rpath+'/20181001.00/d3d_model.json', 'rb') as f:
        info = pd.read_json(f,lines=True).T
        info[info.isnull().values] = None
        info = info.to_dict()[0]

    info.update({'path':rpath}) # The path of the project

    #creating a time sequence of the runs
    start_date = pd.to_datetime('2018-10-1 0:0:0')
    end_date = pd.to_datetime('2018-10-1 12:0:0')
    date_list = pd.date_range(start_date,end_date, freq='12H')
    #append to dic
    info.update({'start_date':start_date,'end_date':end_date, 'dates' : date_list})

    #creating a sequence of folder to store the runs. In this case we name them after the date attribute.
    #NOTE that the first folder is the fisrt run already perfomed!!
    folders = [datetime.datetime.strftime(x, '%Y%m%d.%H') for x in date_list]
    info.update({'folders':folders})

    #set meteo files
    meteo = []
    for date in date_list:
        end_date= pd.to_datetime(date) + pd.to_timedelta(info['time_frame'])
        end_date = end_date.strftime(format='%Y-%m-%d %H:%M:%S')
        dr = pd.date_range(date, end_date, freq='12H')
        dur = [(DATA_DIR / ('uvp_' + datetime.datetime.strftime(x, '%Y%m%d%H') + '.grib')).as_posix() for x in dr]
        meteo.append(dur)
    info.update({'meteo_source':meteo})
    print(meteo)

    info.update({'time_frame' : len(folders)*[info['time_frame']]})


    h = cast.cast(**info) # initialize
    h.run()
    # combine output
    folders = [info['path']+'/'+f for f in info['folders']]
    res = data.data(folders=folders,solver='d3d')


    # check single run
    case2.update({'rpath':rpath + '/combined/'})
    a = pyPoseidon.model(**case2)
    a.execute()
    out = data.data(**case2)

    test = True
    for var in out.Dataset.data_vars:
        if not out.Dataset[var].equals(res.Dataset[var]):
            if np.abs(out.Dataset[var].values-res.Dataset[var].values).max() > 1.e-6 : test = False


    return test
コード例 #4
0
def d3d(tmpdir):
    #initialize a model
    rpath = str(tmpdir) + '/d3d/'
    case.update({'rpath':
                 rpath + '/20181001.00/'})  # use tmpdir for running the model
    b = pyPoseidon.model(**case)

    b.execute()

    #creating a time sequence of the runs
    start_date = pd.to_datetime('2018-10-1 0:0:0')
    end_date = pd.to_datetime('2018-10-2 0:0:0')
    date_list = pd.date_range(start_date, end_date, freq='12H')

    #creating a sequence of folder to store the runs. In this case we name them after the date attribute.
    #NOTE that the first folder is the fisrt run already perfomed!!
    rpaths = [
        rpath + datetime.datetime.strftime(x, '%Y%m%d.%H') + '/'
        for x in date_list
    ]

    #set meteo files
    meteo = []
    for date in date_list:
        end_date = pd.to_datetime(date) + pd.to_timedelta('12H')
        end_date = end_date.strftime(format='%Y-%m-%d %H:%M:%S')
        dr = pd.date_range(date, end_date, freq='12H')
        names = [
            'uvp_' + datetime.datetime.strftime(x, '%Y%m%d%H') + '.grib'
            for x in dr
        ]
        dur = [(DATA_DIR / name).as_posix() for name in names]
        meteo.append(dur)

    #set cast
    for l in range(len(rpaths) - 1):
        h = cast.cast(solver='d3d',
                      model=b,
                      ppath=rpaths[l],
                      cpath=rpaths[l + 1],
                      meteo=meteo[l + 1],
                      date=date_list[l + 1])
        h.set(execute=True)  # execute

    # Run check case - Total duration
    check.update({'rpath':
                  rpath + 'check/'})  # use tmpdir for running the model
    c = pyPoseidon.model(**check)
    c.execute()

    # COMPARE
    output = data.data(folders=rpaths, solver='d3d')
    total = data.data(folders=[rpath + 'check/'], solver='d3d')

    test = True
    rb = []
    for var in total.Dataset.data_vars:
        if not total.Dataset[var].equals(output.Dataset[var]):
            rb.append(var)
            if np.abs(total.Dataset[var].values -
                      output.Dataset[var].values).max() > 1.e-6:
                test = False

    print(rb)
    return test
コード例 #5
0
def schism(tmpdir):
    #initialize a model
    rpath = str(tmpdir)+'/schism/'
    case.update({'rpath':rpath+'20181001.00/'}) # use tmpdir for running the model

    b = pyPoseidon.model(**case)

    b.execute()

    #creating a time sequence of the runs
    start_date = pd.to_datetime('2018-10-1 0:0:0')
    end_date = pd.to_datetime('2018-10-2 0:0:0')
    date_list = pd.date_range(start_date,end_date, freq='12H')

    #creating a sequence of folder to store the runs. In this case we name them after the date attribute.
    #NOTE that the first folder is the fisrt run already perfomed!!
    rpaths = [rpath + datetime.datetime.strftime(x, '%Y%m%d.%H') +'/' for x in date_list]

    #creating a sequence of folder from which we read the meteo.
    meteo = []
    for date in date_list:
        prev_date= pd.to_datetime(date) - pd.to_timedelta('12H')
        prev_date = prev_date.strftime(format='%Y-%m-%d %H:%M:%S')
        dr = pd.date_range(prev_date, date, freq='12H')
        names = ['uvp_'+ datetime.datetime.strftime(x, '%Y%m%d%H') + '.grib' for x in dr]
        dur = [ (DATA_DIR / name).as_posix() for name in names ] 
        meteo.append(dur)

    #set cast
    for l in range(len(rpaths)-1):
        h = cast.cast(solver='schism',model=b,ppath=rpaths[l],cpath=rpaths[l+1],meteo=meteo[l+1], date=date_list[l+1])
        h.set(execute=True) # execute

    # Run check case - Total duration
    check.update({'rpath':rpath+'check/'}) # use tmpdir for running the model

    # Combine meteo appropriately

    m1 = pm.meteo(meteo_source=METEO_FILES_2[0],meteo_engine='cfgrib')
    m2 = pm.meteo(meteo_source=METEO_FILES_2[1],meteo_engine='cfgrib')
    m3 = pm.meteo(meteo_source=METEO_FILES_2[2],meteo_engine='cfgrib')
    m4 = pm.meteo(meteo_source=METEO_FILES_2[3],meteo_engine='cfgrib')

    # extract correct chunk

    w1 = m1.Dataset.isel(time=slice(0,13))
    w2 = m2.Dataset.isel(time=slice(1,13)) # note that we keep the 12 hour from the previous file
    w3 = m3.Dataset.isel(time=slice(1,13))
    w4 = m4.Dataset.isel(time=slice(1,13))

    #combine
    meteo = xr.combine_by_coords([w1,w2,w3,w4],combine_attrs='override')
    #saving
    check.update({'meteo_source' : meteo})
    
    c = pyPoseidon.model(**check)

    c.execute()

    # COMPARE
    output = data.data(folders=rpaths,solver='schism')

    total = data.data(folders=[rpath+'check/'],solver='schism')

    r = output.Dataset.isel(time=slice(0,36))
    

    rb = []
    for var in total.Dataset.data_vars:
        if not total.Dataset[var].equals(r[var]):
            rb.append(var)

    print(rb)


#    flag = True TODO
#    for var in rb:
#        flag = False
#        mdif = np.abs(total.results.Dataset[var].values - output.results.Dataset[var].values).max()
#        if mdif < 1.e-14 :
#            flag = True
#    print(mdif)

    if (rb == ['zcor']) or rb==[]:
        return True
    else:
        return False
コード例 #6
0
def schism(tmpdir):
    #initialize a model
    rpath = str(tmpdir) + '/schism/'
    case.update({'rpath':
                 rpath + '20181001.00/'})  # use tmpdir for running the model

    b = pyPoseidon.model(**case)

    b.execute()

    # run the cast
    with open(rpath + '20181001.00/schism_model.json', 'rb') as f:
        info = pd.read_json(f, lines=True).T
        info[info.isnull().values] = None
        info = info.to_dict()[0]

    info.update({'path': rpath})

    #creating a time sequence of the runs
    start_date = pd.to_datetime('2018-10-1 0:0:0')
    end_date = pd.to_datetime('2018-10-2 0:0:0')
    date_list = pd.date_range(start_date, end_date, freq='12H')

    #append to dic
    info.update({
        'start_date': start_date,
        'end_date': end_date,
        'dates': date_list
    })

    #creating a sequence of folder to store the runs. In this case we name them after the date attribute.
    #NOTE that the first folder is the fisrt run already perfomed!!
    folders = [datetime.datetime.strftime(x, '%Y%m%d.%H') for x in date_list]
    info.update({'folders': folders})

    #creating a sequence of folder from which we read the meteo.
    meteo = []
    for date in date_list:
        end_date = pd.to_datetime(date) + pd.to_timedelta(info['time_frame'])
        end_date = end_date.strftime(format='%Y-%m-%d %H:%M:%S')
        dr = pd.date_range(date, end_date, freq='12H')
        names = [
            'uvp_' + datetime.datetime.strftime(x, '%Y%m%d%H') + '.grib'
            for x in dr
        ]
        dur = [(DATA_DIR / name).as_posix() for name in names]
        meteo.append(dur)

    info.update({'meteo_source': meteo})

    info['time_frame'] = len(folders) * [info['time_frame']]

    #set cast
    h = cast.cast(**info)  # initialize

    h.run()

    # Run check case - Total duration
    check.update({'rpath':
                  rpath + 'check/'})  # use tmpdir for running the model

    c = pyPoseidon.model(**check)

    c.execute()

    # COMPARE
    folders = [info['path'] + f for f in info['folders']]
    output = data.data(folders=folders, solver='schism')

    total = data.data(folders=[rpath + 'check/'], solver='schism')

    rb = []
    for var in total.Dataset.data_vars:
        if not total.Dataset[var].equals(output.Dataset[var]):
            rb.append(var)

    print(rb)

    #    flag = True TODO
    #    for var in rb:
    #        flag = False
    #        mdif = np.abs(total.results.Dataset[var].values - output.results.Dataset[var].values).max()
    #        if mdif < 1.e-14 :
    #            flag = True
    #    print(mdif)

    if (rb == ['zcor']) or rb == []:
        return True
    else:
        return False
コード例 #7
0
def schism(tmpdir):
    #initialize a model
    rpath = str(tmpdir) + '/schism/'
    case.update({'rpath':
                 rpath + '20181001.00/'})  # use tmpdir for running the model

    #creating a time sequence of the runs
    start_date = pd.to_datetime('2018-10-1 0:0:0')
    end_date = pd.to_datetime('2018-10-2 0:0:0')
    date_list = pd.date_range(start_date, end_date, freq='12H')

    m0 = pm.meteo(meteo_source=METEO_FILES_1, engine='cfgrib')

    case.update({'meteo_source': m0.Dataset})

    b = pyPoseidon.model(**case)

    b.execute()

    # run the cast
    with open(rpath + '20181001.00/schism_model.json', 'rb') as f:
        info = pd.read_json(f, lines=True).T
        info[info.isnull().values] = None
        info = info.to_dict()[0]

    info.update({'path': rpath})

    #append to dic
    info.update({
        'start_date': start_date,
        'end_date': end_date,
        'dates': date_list
    })

    #creating a sequence of folder to store the runs. In this case we name them after the date attribute.
    #NOTE that the first folder is the fisrt run already perfomed!!
    folders = [datetime.datetime.strftime(x, '%Y%m%d.%H') for x in date_list]
    info.update({'folders': folders})

    #creating a sequence of folder from which we read the meteo.
    meteo = [m0.Dataset]
    for date in date_list[1:]:
        end_date = pd.to_datetime(date) + pd.to_timedelta(info['time_frame'])
        end_date = end_date.strftime(format='%Y-%m-%d %H:%M:%S')
        dr = [date - pd.to_timedelta('12H'), date]
        names = [
            'uvp_' + datetime.datetime.strftime(x, '%Y%m%d%H') + '.grib'
            for x in dr
        ]
        dur = [(DATA_DIR / name).as_posix() for name in names]
        m1 = pm.meteo(meteo_source=dur[0], engine='cfgrib')
        m2 = pm.meteo(meteo_source=dur[1], engine='cfgrib')
        w1 = m1.Dataset.isel(time=slice(12, 13))
        w2 = m2.Dataset.isel(time=slice(
            1, None))  # note that we keep the 12 hour from the previous file
        mf = xr.combine_by_coords([w1, w2])
        meteo.append(mf)

    info.update({'meteo_source': meteo})

    info['time_frame'] = len(folders) * [info['time_frame']]

    #set cast
    h = cast.cast(**info)  # initialize

    h.run()

    # Run check case - Total duration
    check.update({'rpath':
                  rpath + 'check/'})  # use tmpdir for running the model

    # Combine meteo appropriately

    m1 = pm.meteo(meteo_source=METEO_FILES_2[0], engine='cfgrib')
    m2 = pm.meteo(meteo_source=METEO_FILES_2[1], engine='cfgrib')
    m3 = pm.meteo(meteo_source=METEO_FILES_2[2], engine='cfgrib')
    m4 = pm.meteo(meteo_source=METEO_FILES_2[3], engine='cfgrib')

    # extract correct chunk

    w1 = m1.Dataset.isel(time=slice(0, 13))
    w2 = m2.Dataset.isel(time=slice(
        1, 13))  # note that we keep the 12 hour from the previous file
    w3 = m3.Dataset.isel(time=slice(1, 13))
    w4 = m4.Dataset.isel(time=slice(1, 13))

    #combine
    meteo = xr.combine_by_coords([w1, w2, w3, w4])
    #saving
    check.update({'meteo_source': meteo})

    c = pyPoseidon.model(**check)

    c.execute()

    # COMPARE
    folders = [info['path'] + f for f in info['folders']]
    output = data.data(folders=folders, solver='schism')

    total = data.data(folders=[rpath + 'check/'], solver='schism')

    r = output.Dataset.isel(time=slice(0, 36))

    rb = []
    for var in total.Dataset.data_vars:
        if not total.Dataset[var].equals(r[var]):
            rb.append(var)

    print(rb)

    #    flag = True TODO
    #    for var in rb:
    #        flag = False
    #        mdif = np.abs(total.results.Dataset[var].values - output.results.Dataset[var].values).max()
    #        if mdif < 1.e-14 :
    #            flag = True
    #    print(mdif)

    if (rb == ['zcor']) or rb == []:
        return True
    else:
        return False