In [25]:
import pandas as pd
import numpy as np
import pickle
import os
root_directory = 'D:/github/w_vattenstatus/ekostat_calculator'#"../" #os.getcwd()
workspace_directory = root_directory + '/workspaces'
resource_directory = root_directory + '/resources'
#alias = 'lena'
user_id = 'test_user' #kanske ska vara off_line user?
workspace_alias = 'lena_indicator' # kustzonsmodellen_3daydata
# workspace_alias = 'kustzonsmodellen_3daydata'
# ## Initiate EventHandler
print(root_directory)
paths = {'user_id': user_id,
'workspace_directory': root_directory + '/workspaces',
'resource_directory': root_directory + '/resources',
'log_directory': 'D:/github' + '/log',
'test_data_directory': 'D:/github' + '/test_data',
'cache_directory': 'D:/github/w_vattenstatus/cache'}
D:/github/w_vattenstatus/ekostat_calculator
In [27]:
file_path = workspace_directory + '/e86ae1c5-d241-46a4-9236-59524b44e500/input_data/exports/all_data_raw.pkl'
print(file_path)
D:/github/w_vattenstatus/ekostat_calculator/workspaces/e86ae1c5-d241-46a4-9236-59524b44e500/input_data/exports/all_data_raw.pkl
In [28]:
with open(file_path, "rb") as fid:
df = pickle.load(fid)
In [29]:
df.head()
Out[29]:
AMON
BIOV_CONC_ALL
BQIm
CPHL_BTL
CPHL_INTEG
DEPH
DIN
DOXY_BTL
DOXY_CTD
LATIT_DD
...
TEMP_BTL
TEMP_CTD
VISS_EU_CD
WADEP
WATER_BODY_NAME
WATER_DISTRICT
WATER_TYPE_AREA
WLTYP
origin_dtype
origin_file_path
0
NaN
NaN
NaN
NaN
0.68
NaN
NaN
NaN
NaN
58.80317
...
NaN
NaN
584340-174401
41.5
Krabbfjärden
Norra Östersjöns vattendistrikt
14 - Östergötlands yttre kustvatten
2 - Havsområde innanför 1 NM
chlorophyll
chlorophyll_sharkweb_data_chlorophyll_wb_2007-...
1
NaN
NaN
NaN
NaN
0.6
NaN
NaN
NaN
NaN
58.25987
...
NaN
NaN
581700-113000
71
Gullmarn centralbassäng
Västerhavets vattendistrikt
02 - Västkustens fjordar
2 - Havsområde innanför 1 NM
chlorophyll
chlorophyll_sharkweb_data_chlorophyll_wb_2007-...
2
NaN
NaN
NaN
NaN
1.5
NaN
NaN
NaN
NaN
56.37085
...
NaN
NaN
561400-161201
20
S Kalmarsunds utsjövatten
Södra Östersjöns vattendistrikt
09 - Blekinge skärgård och Kalmarsund. Yttre k...
2 - Havsområde innanför 1 NM
chlorophyll
chlorophyll_sharkweb_data_chlorophyll_wb_2007-...
3
NaN
NaN
NaN
NaN
1.02
NaN
NaN
NaN
NaN
65.72950
...
NaN
NaN
654470-222700
13
Gussöfjärden
Bottenvikens vattendistrikt
22 - Norra Bottenviken. Inre kustvatten
2 - Havsområde innanför 1 NM
chlorophyll
chlorophyll_sharkweb_data_chlorophyll_wb_2007-...
4
NaN
NaN
NaN
NaN
1.02
NaN
NaN
NaN
NaN
65.72950
...
NaN
NaN
654470-222700
13
Gussöfjärden
Bottenvikens vattendistrikt
22 - Norra Bottenviken. Inre kustvatten
2 - Havsområde innanför 1 NM
chlorophyll
chlorophyll_sharkweb_data_chlorophyll_wb_2007-...
5 rows × 59 columns
In [40]:
df.CPHL_INTEG.head()
Out[40]:
0 0.68
1 0.6
2 1.5
3 1.02
4 1.02
Name: CPHL_INTEG, dtype: object
In [136]:
df['DOXY'] = df['DOXY'].astype(float)
In [142]:
mind = df.loc[df['DOXY'] <= 0.5, 'DEPH'].min()
if np.isnan(mind):
print('det är nan')
det är nan
In [16]:
p = paths['resource_directory']+'/default_workspaces/default_workspace/subsets/default_subset/step_2/settings/indicator_settings/'
In [70]:
indicators = ['indicator_din_winter.set',
'indicator_dip_winter.set',
'indicator_ntot_summer.set',
'indicator_ntot_winter.set',
'indicator_ptot_summer.set',
'indicator_ptot_winter.set']
setting_paths = {file: pd.read_csv(p+file, sep = '\t', encoding = 'cp1252') for file in os.listdir(p)}
In [90]:
d = {}
min_s = 2
for key in setting_paths:
if 'ref_ref_value_limit_eq' not in setting_paths[key].columns:
continue
d[key] = {}
for ix in setting_paths[key].index:
eq = setting_paths[key].loc[ix, 'ref_ref_value_limit_eq']
d[key][eq] = {}
max_s = setting_paths[key].loc[ix, 'ref_Salinity_max_int']
S = np.linspace(min_s,max_s, (max_s-min_s)*10+1)
for s in S:
d[key][eq].setdefault(str(s), eval(eq))
In [96]:
d['indicator_ptot_winter.set']['-0.107*s+1.04'].keys()
Out[96]:
dict_keys(['2.0', '2.1', '2.2', '2.3', '2.4', '2.5', '2.6', '2.7', '2.8', '2.9', '3.0', '3.1', '3.2', '3.3', '3.4', '3.5', '3.6', '3.7', '3.8', '3.9', '4.0', '4.1', '4.2', '4.3', '4.4', '4.5', '4.6', '4.7', '4.8', '4.9', '5.0', '5.1', '5.2', '5.3', '5.4', '5.5', '5.6', '5.7', '5.8', '5.9', '6.0'])
In [77]:
d['indicator_secchi.set']['1023.3*((15+( (34-15) * ((6-s) /6) ))**-1.696)'][5]
Out[77]:
7.4863849412192316
In [55]:
for file in os.listdir(p):
pd.read_csv(p+file, sep = '\t', encoding = 'cp1252')
In [78]:
d.keys()
Out[78]:
dict_keys(['indicator_biov.set', 'indicator_chl.set', 'indicator_din_winter.set', 'indicator_dip_winter.set', 'indicator_ntot_summer.set', 'indicator_ntot_winter.set', 'indicator_ptot_summer.set', 'indicator_ptot_winter.set', 'indicator_secchi.set'])
In [52]:
Out[52]:
'indicator_oxygen.set'
In [93]:
file_path = paths['resource_directory']+'/default_workspaces/default_workspace/subsets/default_subset/step_2/settings/indicator_settings/evaluated_refeq.pkl'
with open(file_path, "wb") as fid:
pickle.dump(d, fid)
In [101]:
for dec in [ 1.0 , 1.33333333, 1.66666667, 2.65 , 2.33333333,
2.66666667, 3.0 , 3.33333333, 3.66666667, 4.85 ,
4.33333333, 4.66666667, 5.0 ]:
print(str(dec)[0:3])
1.0
1.3
1.6
2.6
2.3
2.6
3.0
3.3
3.6
4.8
4.3
4.6
5.0
In [98]:
[np.linspace(1,5,13)]
Out[98]:
[array([ 1. , 1.33333333, 1.66666667, 2. , 2.33333333,
2.66666667, 3. , 3.33333333, 3.66666667, 4. ,
4.33333333, 4.66666667, 5. ])]
In [107]:
Out[107]:
array([ 6.6010603 , 3.24496 , 3.41383584, 2.21988369, 4.10806689,
4.43343673, 4.38326748, 5.57009553, 4.62956377, 2.74367025,
3.12286204, 5.52893687, 2.08994427, 4.28758053, 3.90591736,
3.20396791, 2.01626012, 2.80853732, 3.89133465, 3.63974187,
5.98929232, 5.13579287, 2.58902642, 2.79499868, 2.79088486,
4.0920386 , 6.36833578, 4.24958578, 4.54489204, 2.50708856,
5.77483327, 5.7798803 , 3.22471022, 5.82551547, 3.97466646,
6.16708314, 2.35349421, 6.60919869, 3.22394766, 5.62804223,
3.4108617 , 4.19050055, 4.82976794, 6.99369245, 2.81805842,
6.41730364, 6.35202057, 3.96031299, 3.17504189, 2.28642792,
5.64165174, 4.20360586, 3.18978084, 3.34043285, 6.31128279,
6.33941815, 3.71984367, 3.76234437, 2.45064058, 3.66153514,
2.64817181, 3.41873853, 4.17839438, 6.34695536, 4.66008212,
2.25223142, 5.53068733, 6.46516299, 2.40641837, 5.68709984,
3.70553171, 5.70959796, 6.29950544, 3.02539811, 5.2578197 ,
3.42970664, 4.51891999, 6.36599526, 5.14415588, 2.36166991,
6.45910865, 6.3535847 , 6.87499212, 5.06662971, 3.2860947 ,
3.85394064, 5.97242885, 4.15242211, 6.44566409, 5.46008555,
5.2096964 , 5.77535915, 5.36647585, 6.3677222 , 4.82677047,
6.96183236, 5.80617276, 3.61836768, 5.9585569 , 2.45443207,
4.5761112 , 4.48285007, 2.09822133, 2.18489849, 3.77453837,
5.35625945, 5.6321461 , 6.69700334, 6.45532106, 6.77617823,
6.88021196, 6.2169619 , 3.99762575, 3.00747163, 3.89225719,
5.66934578, 3.57854487, 5.94665458, 4.72678766, 2.62478592,
4.92804088, 2.89553663, 6.79764873, 6.07213323, 5.18654021,
5.76269813, 6.887896 , 6.93768845, 2.59644017, 6.45094422,
5.9648806 , 2.53556546, 6.48950503, 3.09153383, 6.7708668 ,
3.71063863, 5.3311976 , 4.16875732, 3.08503216, 3.66050458,
4.84205032, 5.54160895, 6.4740455 , 6.83211582, 2.51838987,
5.30671063, 3.51119121, 6.99383454, 6.78002364, 5.72286752,
4.63708464, 4.22489422, 6.00786539, 6.9229469 , 5.95982355,
2.42456686, 6.2406571 , 6.64500182, 4.42666297, 6.39578781,
5.97849555, 4.9375233 , 6.54136349, 5.05557131, 4.54057935,
2.24422601, 3.91347613, 4.89411538, 5.46139666, 6.54090655,
4.45604927, 4.64194392, 5.41017485, 2.78513159, 4.17711328,
6.40214575, 6.11012662, 3.18736717, 4.93819243, 3.82017568,
6.01123126, 2.9813821 , 5.44454487, 4.50781006, 3.84266977,
6.49932456, 6.85802411, 2.06069112, 3.7439218 , 3.54717379,
3.06455159, 5.7540167 , 3.1233239 , 6.38119082, 3.66922314,
4.38421895, 3.54018639, 6.0492097 , 6.78006498, 2.69710159,
5.01606079, 2.84018528, 3.20192121, 3.29217164, 2.72331782,
2.38172748, 5.02599888, 6.08729345, 2.68331596, 6.12764649,
5.38177388, 3.84795041, 4.60307856, 2.30148117, 5.40147976,
6.47835571, 5.30719597, 6.08972029, 5.2815531 , 4.07593114,
4.95874283, 5.47375333, 6.41458379, 2.68479132, 6.5142995 ,
5.29251744, 2.67981867, 6.49248486, 2.31839131, 4.64040294,
4.0898884 , 6.24303411, 6.50215969, 6.02613714, 5.41397932,
2.61582313, 3.46279092, 5.8991565 , 4.90534901, 6.92466457,
6.52777612, 3.26519221, 2.41678343, 2.36274684, 3.55931512,
4.88307122, 6.25161128, 4.40151633, 6.75101109, 3.49779287,
4.4474702 , 5.57930554, 4.17460041, 4.60884254, 4.91154303,
6.45082427, 2.55911497, 3.61922495, 3.2982324 , 5.83870869,
5.10074813, 3.78189087, 5.71416616, 2.31933742, 2.34549337,
2.13946563, 2.29600362, 5.6300123 , 4.1054376 , 6.04613306,
2.51446186, 3.75626966, 4.29040338, 6.1040771 , 6.81144492,
5.03849896, 6.91040026, 6.44631074, 5.10498807, 4.37201194,
3.02037423, 2.73334041, 3.29234755, 3.14064254, 6.60511684,
4.64340523, 6.82834399, 5.64546229, 3.71653082, 2.76096645,
3.46095977, 5.14142206, 3.8618567 , 3.94164855, 5.96739354,
3.07559819, 3.84507712, 6.89649528, 6.57040703, 5.95982814,
5.88871262, 2.62418352, 3.14876336, 5.10790271, 6.03964115,
5.80175079, 5.68282394, 5.22034721, 3.63202356, 6.51251972,
2.41760084, 3.90748459, 6.59832057, 3.0032561 , 3.84693984,
3.72250226, 2.21162316, 2.77378063, 3.50747996, 5.47549038,
3.99191608, 6.3669983 , 3.87612272, 2.12181812, 4.99813661,
3.82065213, 2.79206899, 5.01440588, 2.43581059, 5.60632851,
5.82116555, 5.26490228, 6.85498212, 2.20394208, 6.55087959,
3.27985762, 4.3039096 , 2.94285723, 3.8375168 , 2.40251824,
6.92049493, 3.37146438, 2.80795108, 4.47878843, 2.94090468,
2.16035933, 5.1053939 , 6.87169481, 6.484998 , 3.42547111,
4.36422954, 5.00895677, 2.62709025, 2.47151325, 3.61602757,
2.70687504, 2.55552865, 4.39114909, 5.33244478, 4.70398568,
4.89385047, 2.52563926, 2.50178359, 6.08194531, 5.72072472,
3.16915466, 2.81070077, 2.35284792, 2.18656913, 3.11260454,
6.80316658, 5.55414462, 5.73829151, 5.09562578, 2.6091713 ,
2.27485084, 4.94572193, 4.50632588, 6.42429995, 2.92809948,
2.72827887, 3.66296688, 6.21226292, 3.50008142, 2.302515 ,
3.68444873, 6.74593995, 5.4806082 , 2.12923154, 3.37615465,
5.30205425, 4.06016221, 4.09177262, 4.85925346, 3.66952599,
3.18812206, 4.62519238, 5.09366184, 6.01744229, 2.44993141,
5.81106715, 5.50512271, 5.29160916, 3.92678422, 4.73365606,
6.15512783, 5.18367279, 6.23536394, 5.5149554 , 5.46189156,
2.24312036, 5.35402291, 3.2090351 , 6.37455559, 4.46215751,
3.38097209, 3.47021908, 6.79583174, 2.57965951, 4.52443603,
4.77833946, 5.83590022, 2.14242122, 6.54307286, 6.10059542,
2.05960407, 3.02635051, 2.38001791, 6.39899199, 4.66354247,
5.34620462, 2.61869804, 5.6048249 , 5.76587729, 2.27126762,
2.60663582, 5.32803592, 2.08922628, 5.79656354, 5.68849358,
5.57269238, 5.03539969, 5.15006657, 5.08984094, 6.43854719,
4.40600665, 4.36050879, 4.8765918 , 2.57767395, 3.8012618 ,
5.98263885, 3.57498598, 3.17051657, 5.77721603, 5.51912493,
3.97202358, 3.06254803, 5.62581742, 4.86079441, 6.90097718,
5.4549318 , 5.72192522, 3.84520851, 6.93718608, 6.41567304,
6.46517056, 2.21856008, 4.0698391 , 3.09322857, 5.86713084,
3.22572897, 5.80118043, 4.12735608, 6.10365204, 3.90307575,
3.45782182, 3.30639741, 6.17944897, 2.79703867, 6.03019453,
2.26784857, 4.82913405, 5.67482837, 3.0761241 , 3.31468015,
2.96884187, 5.5258543 , 5.29226585, 2.61010116, 6.11302801,
2.9945091 , 4.20596894, 5.33282576, 5.65712022, 2.06710252,
5.89226856, 2.27152853, 6.38713923, 4.82807023, 2.5976598 ,
6.20830911, 3.69238735, 2.29659016, 4.21598221, 2.89718771,
4.17898983, 4.26572951, 3.28923335, 6.86094136, 2.56329475,
5.91716367, 5.9525202 , 6.96933617, 6.72628019, 4.77297773,
6.05090042, 2.1907268 , 5.81390802, 6.48630265, 5.62087593,
6.83935513, 2.98741204, 4.7517594 , 5.14665783, 5.60143373,
2.88785603, 3.70640008, 6.41924445, 4.1231234 , 4.2779945 ,
6.8425754 , 2.52346168, 3.46413746, 6.67561416, 6.50540003,
4.17617139, 5.47036747, 2.24077895, 3.611794 , 3.01495423,
4.61995554, 4.25297264, 5.44887263, 4.79391722, 5.38563732,
2.85908008, 5.2642741 , 4.019255 , 4.22968524, 6.3912242 ,
3.58588485, 3.54726916, 6.83456106, 2.05642345, 2.24649877,
5.28039743, 4.76374906, 2.88529733, 5.79972449, 3.94962174,
6.28836213, 6.77326015, 3.35342134, 2.1402671 , 3.63173721,
3.94892425, 5.24821845, 4.84962789, 3.70588199, 3.52105248,
2.7131836 , 4.89673299, 2.72783821, 4.06050472, 3.93801914,
4.47664292, 2.29187269, 6.06272989, 6.49681067, 2.80146844,
5.41276081, 5.50977548, 2.09192701, 2.08843838, 3.00644175,
4.89916285, 6.08707641, 3.99718512, 3.52005841, 2.59255413,
3.83656189, 4.08824997, 5.38164259, 4.63478789, 4.5078803 ,
2.08593656, 6.10769204, 3.71777039, 5.9348098 , 4.49625613,
5.57320828, 2.09979496, 6.04176526, 4.24382005, 5.48352101,
4.85905996, 5.3607152 , 5.30764445, 5.51925569, 3.99876735,
2.54841938, 4.62946503, 3.9905059 , 4.62052946, 4.81178643,
5.68883789, 4.24438642, 2.40776417, 3.95335791, 4.55153637,
6.26842621, 6.22918276, 5.31479533, 2.48136463, 2.50515794,
5.44537632, 4.52980485, 4.92872163, 3.60958198, 2.67662184,
4.03657994, 6.4073302 , 5.30010396, 6.70649364, 5.11603871,
4.66697549, 5.18369799, 4.65725034, 2.18116726, 4.52368599,
2.55978505, 6.45669251, 6.75826543, 3.38070447, 5.66296693,
4.71110914, 2.20594907, 4.53022125, 3.00024833, 2.63480973,
4.5771394 , 3.74341051, 3.19818603, 5.76471624, 2.59151546,
4.76060495, 2.50535212, 4.07589494, 5.49107716, 2.27521664,
6.95000286, 2.36956183, 3.47628794, 5.14927814, 4.97322921,
6.94614263, 6.47256597, 5.37479809, 5.02672277, 6.5175305 ,
2.30762292, 5.92618389, 6.49809812, 5.8714571 , 4.78865872,
4.18584783, 6.19302085, 3.09729177, 3.87769368, 4.15137331,
6.5149138 , 5.47949426, 5.33748716, 2.24257262, 3.30038201,
5.82178737, 2.70460343, 6.78310953, 2.41339558, 3.64028632,
2.54192869, 5.28403478, 3.44231218, 3.01898436, 5.23216487,
6.06042357, 2.68605062, 4.22446082, 4.35646675, 6.08332605,
3.69606435, 5.495924 , 3.26405 , 3.89200534, 5.63093026,
6.66452531, 4.82825194, 4.51020728, 6.49102082, 3.1623459 ,
6.61319523, 6.15659966, 6.69773966, 4.58253678, 2.72361418,
2.57441214, 2.5178882 , 6.42703505, 4.77716147, 5.17264717,
5.45513051, 5.98325276, 6.47141375, 5.6330004 , 6.45326482,
4.61049864, 5.08564762, 2.52567373, 4.14681304, 5.81675927,
2.33450457, 4.47947193, 4.59515819, 3.35228994, 5.24977635,
6.80722875, 3.07568198, 5.32457134, 6.84282027, 4.94880271,
2.26470248, 5.43708499, 4.12946831, 6.75815772, 6.18148259,
6.77784929, 6.40563829, 5.95601409, 5.47004346, 4.85939394,
5.18462477, 4.75412445, 2.79647078, 5.44904446, 6.92184261,
6.89779946, 6.16987867, 4.15309332, 5.07155944, 6.00462997,
5.29583614, 6.66621441, 6.75811187, 5.01399859, 3.60627957,
6.97096222, 6.58701733, 2.96555724, 5.00734894, 6.51387744,
3.11251847, 2.52491795, 4.17553526, 6.20503682, 6.3068079 ,
5.95163957, 3.75483952, 3.04628434, 4.41593584, 2.58231251,
2.53990398, 6.55837207, 3.30916326, 3.00794294, 3.23487093,
6.13761168, 6.84108829, 3.11989125, 2.00532133, 3.80733042,
4.38941066, 6.89539226, 6.97609342, 6.02215196, 5.57961064,
2.75753671, 5.41546559, 4.50412348, 2.28261197, 6.10923538,
2.52262979, 2.34595639, 5.84988753, 5.99809035, 4.08117605,
2.38173367, 3.24105891, 2.35646543, 6.18763759, 3.55202442,
2.35937754, 4.72167166, 3.21325201, 4.77658103, 5.27177838,
3.25868155, 3.15293279, 6.7679112 , 6.65008773, 6.59002722,
5.1938573 , 6.13305356, 3.89925556, 2.75146924, 2.26606669,
3.61840626, 5.0232155 , 2.76729455, 2.21799992, 5.37111702,
6.70216428, 5.34714444, 3.86495444, 5.1904591 , 5.2429226 ,
3.63375579, 2.88688379, 5.78118034, 6.73377762, 4.53609485,
6.72216661, 2.6236366 , 4.02761055, 6.39747093, 2.07009363,
2.28623343, 5.58086544, 4.13812788, 3.89786515, 2.81061764,
6.6064213 , 3.71485898, 5.42036267, 5.65780738, 6.93772988,
5.61580181, 2.79555868, 2.82452343, 3.12545691, 6.5350658 ,
6.80912922, 5.63637582, 2.26981947, 4.58994057, 3.70533692,
3.40903108, 4.79573695, 3.72380013, 3.51600392, 5.04499763,
6.81796801, 4.58596155, 5.64112039, 3.17582261, 4.31821694,
2.83464331, 6.96248503, 3.99132365, 2.62548625, 4.3049228 ,
3.21520434, 2.67112666, 6.38300549, 5.65611638, 5.72610013,
3.31449261, 3.74687402, 6.40268635, 4.38894772, 6.03536693,
3.57301247, 6.61936097, 6.57478375, 3.29133143, 3.37881767,
4.29190951, 2.78318652, 3.47154996, 3.92530825, 5.68694541,
5.19874319, 3.45830602, 6.77607394, 6.779493 , 6.07538109,
6.77353694, 3.21986679, 6.16015009, 3.46914696, 5.368496 ,
6.21149472, 3.95552016, 3.55342598, 2.58620214, 5.37992924,
5.03195323, 2.84888083, 2.06416058, 4.12142025, 4.7678995 ,
6.7598017 , 3.94875956, 4.97886557, 6.02331098, 2.947528 ,
6.3383257 , 5.35985634, 3.75102133, 2.45804365, 5.29021222,
5.44020553, 6.16907161, 3.07110775, 5.75345357, 4.62906318,
4.80644617, 2.8642308 , 2.98898953, 5.84736307, 3.86205869,
4.81223949, 6.93610551, 4.2344211 , 6.29928813, 6.55967358,
2.43917526, 3.54349285, 6.812135 , 5.29020844, 3.3029618 ,
3.91599249, 5.47242509, 4.17335352, 3.18873478, 2.08334657,
6.10669979, 2.5003463 , 2.80125459, 4.37952553, 6.97285166,
3.73488338, 6.72173887, 6.66009084, 2.77257682, 2.36424281,
3.16097409, 4.59292004, 4.17488435, 3.00906553, 5.63355049,
2.82469003, 3.25536509, 3.32138399, 4.01289673, 5.39444695,
6.33392333, 6.13136915, 4.27297296, 5.00186334, 6.95656487,
6.32289682, 2.54448855, 3.65483061, 6.72834894, 6.29611889,
3.7310734 , 3.67180711, 4.92910969, 5.90997437, 4.04098712,
2.95821929, 5.13390442, 3.76426873, 3.61518412, 5.25573663,
5.01030047, 6.52131229, 6.13450691, 2.81907906, 5.65547555])
In [122]:
dd = d['indicator_ptot_winter.set']['-0.107*s+1.04']
ref_list = []
for s in np.random.uniform(2,6,4000):
get_s = str(s)[0:3]
print(get_s)
ref_list.append(dd.get(get_s))
3.7
2.0
2.6
2.7
4.2
2.2
3.6
5.4
5.0
2.8
2.5
4.7
3.7
5.0
3.7
3.2
5.5
3.6
4.8
3.2
2.2
3.3
3.4
4.2
4.5
4.3
2.0
2.4
4.1
3.8
2.4
4.0
5.7
4.7
3.8
2.9
5.5
2.5
4.9
3.7
2.4
3.9
5.1
5.2
3.1
4.9
4.5
5.6
5.3
4.9
3.0
4.6
2.5
4.1
2.5
4.6
4.9
2.1
2.1
3.6
5.2
2.1
2.2
3.2
3.2
5.2
4.2
5.6
2.6
2.6
3.5
4.0
4.0
3.0
5.6
2.3
2.7
3.2
3.2
5.5
4.7
2.3
4.3
4.6
4.1
2.4
3.7
5.3
3.2
3.9
5.5
3.7
2.5
5.7
2.2
4.3
3.9
4.1
4.5
2.0
2.8
5.4
4.8
5.3
5.1
3.3
4.0
2.7
4.9
3.7
5.4
4.2
2.9
2.0
5.2
3.4
2.2
2.3
2.9
5.3
3.3
4.6
5.4
4.8
2.5
4.8
3.5
5.9
4.0
2.6
3.7
4.7
5.8
2.2
3.6
5.3
3.2
3.4
4.5
4.7
5.2
4.3
3.0
2.8
2.4
4.1
5.1
5.6
3.4
2.1
5.2
3.8
4.5
4.3
3.8
4.2
2.9
4.5
5.1
4.3
2.6
3.5
4.2
4.9
5.5
5.1
4.6
4.8
5.9
4.9
3.7
5.8
4.8
5.3
5.0
3.1
4.1
4.9
3.7
2.8
3.6
2.9
4.1
3.7
4.7
4.0
5.4
2.1
3.6
2.6
5.6
2.2
4.3
3.7
3.2
2.9
4.5
2.4
5.9
4.1
2.1
2.0
3.8
5.1
2.6
4.8
5.8
4.7
5.1
4.2
5.7
2.7
4.7
4.3
3.8
2.8
2.0
3.0
5.1
2.4
3.2
3.0
3.5
3.1
3.4
2.1
5.9
3.5
4.5
5.5
5.4
2.8
4.6
4.3
5.2
3.9
4.4
2.7
5.2
4.7
2.8
4.5
2.0
3.0
4.4
2.2
5.2
2.2
4.4
2.3
5.7
5.3
3.8
2.8
5.0
4.7
2.3
4.7
5.2
4.1
3.4
2.1
2.6
3.1
4.8
5.1
2.1
4.0
3.5
5.4
3.4
5.0
4.2
2.5
3.4
4.0
4.3
3.9
4.8
4.5
2.7
2.3
4.0
4.5
2.0
3.5
2.2
5.9
3.5
3.2
2.1
2.1
2.6
5.3
5.3
5.6
4.4
4.0
3.9
3.0
2.9
3.5
5.0
3.8
3.3
2.7
5.1
3.2
3.7
2.0
5.6
3.6
2.3
3.6
2.3
2.8
4.3
5.2
5.1
4.9
4.4
2.7
3.7
5.6
5.4
5.8
2.2
2.2
4.9
5.0
2.6
2.9
3.0
5.0
4.7
4.1
4.1
3.0
3.7
5.4
4.5
2.5
3.0
2.7
3.5
3.9
2.8
2.3
3.6
2.8
5.3
5.2
3.5
2.7
5.1
5.1
5.3
2.6
2.4
3.2
2.3
4.7
5.8
4.3
3.0
5.3
3.7
3.2
2.6
3.9
4.3
3.5
4.2
5.5
4.9
3.7
2.7
5.5
2.6
5.0
3.9
5.2
2.2
5.9
2.4
4.7
4.6
3.8
5.1
4.1
4.4
4.9
3.9
5.1
2.1
2.8
3.5
2.0
5.0
4.9
2.8
2.0
5.6
2.0
3.8
5.1
5.4
5.6
4.8
5.7
2.6
4.0
4.6
2.1
4.4
5.5
4.9
4.1
4.3
2.2
2.7
3.6
4.2
5.5
4.8
5.2
2.3
2.1
3.3
4.4
2.8
4.1
4.1
2.0
4.3
4.5
2.5
4.4
5.5
3.1
5.3
2.9
2.0
2.8
3.8
4.9
3.1
5.8
3.4
2.7
3.1
3.8
5.1
3.5
4.6
3.9
4.1
2.3
4.8
5.6
4.1
5.6
5.3
2.0
2.0
4.0
5.7
2.4
4.9
3.1
5.5
4.0
3.5
5.7
2.2
5.2
5.9
2.6
4.6
4.3
5.8
5.9
4.8
3.2
2.6
2.3
2.1
5.7
5.0
4.6
2.5
2.5
2.1
4.3
5.0
2.3
3.5
4.0
5.2
5.6
4.3
2.0
3.8
2.3
5.1
3.9
4.6
4.4
4.2
5.9
2.5
3.7
3.1
5.2
4.8
4.5
2.6
3.0
5.7
5.2
4.9
3.8
5.5
3.3
5.1
2.2
3.2
3.7
4.9
4.8
5.5
2.4
3.9
4.6
2.9
3.0
2.3
4.6
3.6
5.2
2.2
5.5
3.0
5.5
5.1
5.2
3.5
4.3
2.6
5.5
5.1
4.7
4.9
2.2
2.4
4.1
5.6
3.8
4.3
5.8
4.3
4.8
5.9
2.0
5.8
4.6
2.5
2.3
5.4
4.4
5.5
2.1
4.6
3.2
4.6
5.5
3.7
5.6
4.6
3.2
5.0
4.5
4.5
2.5
2.8
4.8
3.1
5.3
3.4
2.4
4.3
4.1
3.2
3.7
4.4
3.6
5.7
4.9
5.7
3.9
4.6
2.2
3.1
2.9
4.4
2.7
2.9
2.5
4.3
4.1
4.7
3.9
4.3
2.0
5.2
5.5
2.1
4.3
3.1
5.6
4.7
3.9
5.7
5.7
3.4
4.0
4.5
3.0
2.6
2.9
5.1
3.6
2.6
2.5
4.3
3.8
3.2
3.0
4.0
5.4
4.4
3.9
3.8
2.0
3.3
5.9
2.3
5.4
2.1
3.5
3.0
2.5
2.3
5.2
5.5
3.9
5.8
2.7
5.4
2.6
5.1
2.4
3.6
2.3
2.2
4.9
4.0
3.9
3.3
5.4
5.6
2.3
3.6
4.6
4.4
3.5
3.4
5.6
4.1
2.4
4.1
2.5
5.2
4.7
5.7
2.2
2.5
3.1
2.2
4.8
2.5
3.6
2.0
4.2
5.0
2.5
3.9
4.1
2.1
5.2
2.8
3.4
3.8
4.7
5.3
5.5
3.3
4.5
4.1
5.7
3.8
5.4
2.4
5.4
3.9
5.0
3.1
2.7
5.2
4.4
2.6
2.0
3.1
2.3
4.7
2.8
3.8
5.9
5.3
5.4
2.0
2.7
4.4
4.8
5.5
3.4
5.8
4.4
4.3
5.3
4.7
3.7
2.6
4.8
4.3
2.2
5.6
3.0
2.8
2.9
2.5
3.2
5.0
4.2
4.1
4.0
4.7
4.4
5.2
3.5
5.8
5.7
5.5
3.2
3.0
4.8
2.4
2.0
3.2
3.3
5.2
3.3
3.8
2.8
4.6
3.8
2.2
4.1
5.2
3.7
3.8
4.8
5.9
5.1
5.7
3.1
3.5
4.6
3.1
2.1
3.9
2.9
2.6
3.6
3.9
5.5
5.8
4.4
5.8
3.0
5.0
2.5
3.9
2.9
5.4
4.7
3.4
3.4
3.5
3.5
2.7
2.2
5.7
5.9
4.0
5.0
5.7
4.4
4.0
2.9
3.6
4.8
2.8
4.1
2.9
2.2
3.0
4.3
3.7
2.1
5.9
5.4
3.7
2.2
4.0
2.9
3.7
2.8
3.0
4.4
4.7
3.9
4.8
5.3
3.0
5.8
2.9
5.0
5.3
2.3
5.4
4.4
2.4
5.2
5.9
5.9
2.9
3.7
4.2
5.9
3.0
5.4
2.7
5.2
5.9
3.8
4.0
4.8
4.1
5.2
2.7
5.4
3.3
2.2
4.2
2.1
2.9
2.9
2.3
4.4
5.0
2.5
4.7
5.2
2.5
4.1
5.0
5.1
4.7
3.6
3.3
3.5
3.3
5.5
4.7
2.6
3.2
3.9
4.3
2.3
3.8
3.0
2.0
4.7
5.1
3.7
2.0
5.6
3.3
5.0
3.4
2.8
4.3
4.2
2.5
4.9
3.8
3.6
5.4
2.2
4.9
2.2
4.9
5.5
4.0
3.5
3.7
5.4
4.5
3.1
4.8
4.5
5.4
3.4
2.1
2.2
5.3
4.9
2.5
4.4
5.7
2.0
4.8
4.8
5.9
5.8
5.6
3.7
2.5
4.6
4.5
5.9
3.4
2.0
2.2
3.4
3.6
4.1
2.8
4.2
5.3
2.1
3.1
3.1
5.7
5.3
5.6
3.3
2.4
4.5
3.9
3.8
5.2
3.2
2.7
4.3
5.4
4.4
2.4
5.1
5.3
4.1
4.7
5.2
2.7
2.0
4.2
2.4
3.9
3.8
3.7
4.0
4.5
4.6
5.2
2.2
4.6
2.4
2.9
3.9
3.3
4.1
4.2
4.9
4.7
3.8
3.2
4.8
2.2
4.0
4.2
4.6
2.9
2.8
2.3
2.1
4.8
4.1
4.4
3.6
3.3
4.8
4.3
5.1
4.3
3.4
4.6
4.3
5.4
4.1
2.7
5.4
3.4
2.3
5.9
3.7
5.2
4.2
2.7
5.6
4.5
5.7
4.6
4.7
4.9
4.7
2.1
5.3
5.1
2.9
4.7
5.4
2.8
4.0
3.3
3.3
4.3
5.9
3.1
3.7
2.8
5.4
3.8
3.3
3.1
5.6
5.3
3.5
2.8
4.6
2.5
5.1
4.3
4.8
3.1
2.9
2.0
4.7
2.3
5.9
3.9
2.7
5.0
4.2
5.1
4.8
4.8
2.2
2.8
2.4
2.9
3.4
2.3
5.3
2.9
5.4
5.7
4.0
3.7
2.3
3.7
4.3
2.1
5.0
2.1
4.5
5.1
3.0
2.9
3.7
2.9
3.7
3.7
5.4
4.0
2.0
4.5
4.8
5.3
5.9
3.3
4.2
5.9
4.2
2.2
3.5
4.7
2.9
2.6
2.2
4.6
2.7
5.9
4.3
5.4
2.0
3.6
4.3
3.0
2.0
4.0
4.9
2.4
5.7
4.8
4.6
3.0
4.1
2.5
5.8
2.1
3.4
5.5
4.9
5.1
2.0
5.2
5.4
5.3
3.1
4.5
2.6
4.8
3.3
4.0
3.0
3.8
3.8
2.5
5.0
4.7
2.0
5.6
4.5
3.2
4.3
5.4
2.0
2.4
4.6
3.2
3.1
3.3
2.2
2.0
3.1
4.4
2.1
4.9
3.8
2.5
4.0
4.4
3.3
5.7
5.3
4.2
3.8
3.1
5.3
4.3
3.8
4.1
2.6
3.7
5.0
2.9
4.3
2.6
2.6
5.8
2.3
3.9
2.6
3.8
4.4
4.3
3.6
3.4
2.4
4.0
4.9
4.2
4.8
2.3
5.3
2.6
2.0
3.3
5.2
2.0
5.2
2.8
3.8
2.0
5.3
5.1
2.5
3.9
2.5
2.8
5.4
2.8
2.0
2.3
5.2
3.9
2.3
3.4
4.0
3.3
2.6
4.5
2.5
5.3
5.1
3.2
2.5
5.8
3.0
4.1
4.2
5.9
5.0
4.1
5.4
4.6
4.3
4.3
2.4
2.7
3.9
4.3
5.1
5.7
4.7
5.3
3.2
3.7
4.4
4.5
4.0
3.5
4.4
2.9
3.8
3.4
5.3
3.5
5.0
3.0
5.3
2.7
3.2
5.9
5.2
4.5
2.7
3.6
5.6
2.1
2.0
4.9
4.9
4.3
4.5
4.0
5.0
3.5
5.1
3.9
4.2
5.6
2.0
5.0
2.4
2.6
5.2
3.1
5.5
4.7
2.2
2.4
5.5
2.5
3.2
3.4
2.2
3.8
4.4
3.9
4.1
3.5
3.7
4.2
5.6
3.1
4.5
4.8
2.3
5.5
5.9
4.7
4.3
2.2
2.8
2.5
5.5
3.3
4.3
5.7
4.7
5.8
3.4
2.6
2.2
3.3
5.5
2.8
4.6
4.7
4.0
2.6
2.4
5.9
3.0
5.9
4.1
3.9
4.4
4.6
5.8
2.0
3.3
4.4
4.5
3.2
3.6
3.1
2.9
2.0
3.7
3.5
4.9
2.5
3.2
4.9
3.4
2.3
2.8
5.4
2.6
4.5
2.5
2.3
5.4
4.9
5.8
2.5
3.3
4.1
3.1
3.3
3.3
4.2
2.1
4.8
3.9
5.4
4.4
3.4
3.0
5.4
3.8
4.0
3.2
3.0
3.9
2.1
4.9
5.4
3.4
3.9
5.9
4.4
3.1
5.7
2.1
5.7
2.3
3.5
4.0
2.2
5.2
2.7
5.6
3.1
2.7
4.9
2.5
4.5
5.5
2.3
3.3
3.8
4.3
2.1
2.8
5.2
3.0
4.8
2.4
5.6
4.9
5.7
5.5
3.7
5.8
5.1
2.8
4.5
2.6
4.4
4.9
4.6
5.9
5.8
3.4
4.7
3.2
4.2
3.8
4.6
4.0
5.5
5.6
2.5
4.7
4.6
4.6
4.5
2.2
4.5
2.0
3.5
4.8
2.7
3.4
2.7
2.8
2.3
4.1
5.7
4.0
5.1
2.2
4.9
3.3
5.0
2.6
2.5
3.5
2.6
5.2
5.5
2.4
2.7
5.7
3.5
2.7
2.0
4.5
3.3
2.7
5.8
5.4
2.7
2.4
5.1
5.7
4.2
3.8
4.3
2.4
4.0
3.5
4.2
5.4
2.2
4.0
2.2
3.4
4.3
3.1
3.1
3.1
3.4
3.5
5.2
3.0
4.5
2.9
5.9
4.6
2.3
5.5
4.1
3.7
2.1
4.6
4.1
4.2
4.9
4.7
5.3
2.4
4.7
4.3
4.1
5.5
2.3
5.6
4.6
2.0
2.6
4.1
5.8
5.5
4.8
2.6
2.6
4.2
3.5
3.9
5.3
3.8
3.0
4.1
5.9
5.6
5.3
3.1
5.8
3.6
2.9
5.1
3.9
4.4
3.0
5.1
4.5
3.9
3.4
2.9
5.5
5.8
3.5
5.9
4.6
3.1
3.2
5.5
3.7
4.5
4.7
3.3
4.5
5.2
4.9
4.5
3.9
4.8
5.2
5.2
3.4
4.0
4.0
5.2
2.2
2.9
5.0
2.5
3.5
2.1
2.6
5.9
2.8
3.1
4.1
3.4
2.2
5.4
4.2
2.6
3.7
2.1
4.7
2.0
2.8
4.5
3.3
5.4
5.6
5.5
5.6
3.9
5.2
2.4
2.9
2.1
2.2
4.4
4.5
3.2
5.2
2.4
5.3
4.1
5.8
5.8
4.5
3.0
3.2
3.8
2.6
3.3
5.1
2.3
3.3
5.0
4.2
4.9
2.8
2.1
2.6
5.5
4.2
5.8
2.8
3.3
3.5
3.5
2.9
2.7
2.3
3.6
4.3
5.5
4.5
2.7
5.5
2.6
4.4
5.2
4.9
4.4
5.3
3.8
3.6
2.9
5.0
5.6
2.0
5.6
5.6
5.9
2.5
2.5
2.7
2.3
5.6
2.2
5.0
2.4
2.1
3.8
3.8
3.7
5.0
2.4
3.4
2.7
5.7
3.4
3.1
3.8
5.7
5.9
5.4
4.6
4.9
3.6
2.7
4.0
4.9
2.0
3.1
2.0
4.9
4.0
2.8
2.2
3.2
2.6
4.9
4.2
4.9
2.1
3.2
5.7
5.5
4.0
5.5
4.1
5.9
4.3
4.2
4.6
5.0
5.1
3.5
4.5
4.5
5.4
3.4
4.0
5.2
2.4
3.6
5.7
2.8
3.4
5.0
5.4
4.3
5.2
3.9
5.6
3.3
2.9
4.3
3.2
3.5
2.4
3.2
2.2
5.6
2.0
3.1
3.7
4.4
5.9
3.1
2.5
2.8
4.3
2.4
5.9
5.5
2.7
4.3
2.2
3.6
2.6
3.0
5.3
5.0
4.4
5.7
2.2
3.3
2.2
2.9
3.8
4.7
5.0
4.3
5.6
4.5
4.1
4.4
2.7
2.4
3.7
2.1
2.4
3.0
5.7
4.0
2.7
2.8
4.1
5.8
3.2
2.1
3.5
3.1
2.0
4.6
4.7
2.5
3.3
5.2
4.1
5.7
4.2
5.9
2.4
2.3
5.8
2.6
2.6
5.7
2.8
5.1
4.2
3.7
5.8
3.8
3.3
4.4
2.2
5.8
4.1
5.8
5.9
4.6
3.8
2.3
2.2
3.3
3.2
5.8
5.7
5.5
5.1
5.3
3.7
5.3
3.1
2.0
5.7
2.1
2.0
3.6
2.9
5.9
5.4
5.2
3.2
3.4
5.4
5.0
5.1
3.8
2.4
5.2
5.7
4.3
2.7
5.3
3.9
5.7
3.7
4.2
2.1
2.3
2.5
5.4
5.0
3.9
3.0
3.3
2.1
4.7
4.3
2.2
4.2
5.9
2.3
2.1
2.8
4.3
3.2
4.7
3.4
2.4
4.1
5.0
2.3
3.3
3.4
4.6
4.3
5.0
4.2
5.7
3.1
3.4
5.7
3.2
4.5
5.4
5.9
3.5
3.5
5.1
4.2
2.6
5.6
2.2
3.3
3.4
4.8
3.9
5.8
4.8
4.2
2.9
3.3
3.0
2.0
2.5
3.6
3.0
2.3
5.8
4.2
2.2
2.1
3.1
3.8
5.3
3.8
3.1
4.6
4.2
5.6
5.0
5.9
4.7
4.6
3.0
5.3
4.5
3.4
4.1
5.5
4.2
2.9
4.1
3.9
3.7
2.0
5.2
3.3
4.8
4.2
3.6
5.2
2.8
4.9
5.5
3.5
5.5
4.5
5.2
5.7
4.0
3.0
4.3
5.0
5.8
5.0
2.4
5.0
3.5
4.7
5.5
4.9
2.8
2.2
4.2
2.1
5.0
3.8
5.3
4.5
2.3
2.3
2.1
4.0
2.3
2.9
3.7
2.2
2.1
2.6
4.3
5.3
4.8
2.8
2.6
3.2
3.6
5.8
3.0
3.1
3.8
2.7
2.1
2.9
2.4
2.9
4.7
3.9
5.0
3.4
5.6
4.9
3.9
5.4
5.7
5.8
3.7
3.6
5.2
3.8
4.6
2.0
5.6
2.7
5.7
4.5
4.7
4.8
4.3
4.6
5.5
2.8
5.9
5.9
5.6
4.6
5.5
3.3
2.6
5.0
5.9
5.7
2.2
5.6
3.7
2.2
3.4
3.1
4.2
4.3
5.2
5.0
2.9
2.9
4.4
2.8
4.3
3.0
2.3
3.2
4.6
3.8
3.0
5.8
3.9
4.7
4.9
2.0
5.9
2.9
2.2
5.6
4.1
4.9
3.3
2.8
4.3
4.7
4.0
5.1
2.4
2.6
2.5
3.1
5.3
3.0
2.8
3.4
4.8
4.9
2.4
3.7
2.4
3.2
2.3
4.7
5.7
4.1
3.4
4.0
2.6
2.6
2.8
4.5
3.5
3.1
5.6
2.7
4.7
3.3
2.0
2.4
3.2
5.4
3.9
4.1
2.3
4.6
4.1
2.4
2.5
3.3
4.7
5.9
2.4
4.5
3.3
5.4
3.2
5.8
2.0
3.5
4.7
2.0
3.1
4.0
4.7
4.4
5.6
3.5
4.9
3.4
4.5
5.0
4.7
4.8
4.2
5.2
2.7
4.8
4.0
5.8
3.4
5.8
4.7
3.4
3.8
4.2
5.4
4.8
3.2
4.6
4.2
5.6
3.9
5.0
5.0
2.8
5.5
3.7
2.9
2.1
3.8
3.6
4.6
2.0
4.5
3.5
5.6
3.6
4.8
3.3
4.9
2.1
5.6
5.2
3.8
2.9
5.2
5.3
5.1
2.6
3.9
4.9
4.2
3.1
2.2
5.7
5.7
3.3
3.4
3.8
2.6
5.2
4.3
4.4
4.0
5.9
2.3
2.9
2.8
3.6
4.4
2.5
4.0
5.5
5.7
3.6
4.2
3.5
2.0
4.1
5.1
5.4
3.9
5.4
2.8
2.4
4.3
5.8
5.7
4.8
2.5
4.9
4.5
5.2
2.2
5.5
4.7
4.4
4.4
2.7
5.0
3.6
3.0
2.4
3.1
5.7
3.7
5.0
4.3
5.2
3.9
3.8
4.3
2.9
5.9
3.5
5.0
5.7
4.1
4.5
5.6
4.1
4.9
2.5
3.6
5.4
4.0
5.8
4.0
5.3
5.3
2.0
4.2
4.0
4.1
2.5
3.4
5.1
2.8
3.7
4.8
3.9
3.1
4.7
3.2
2.9
2.1
5.5
2.5
4.7
4.8
4.3
2.2
3.9
5.1
5.4
3.6
3.9
5.2
5.2
4.9
2.1
4.6
4.0
5.1
5.6
5.8
5.2
2.5
5.5
4.4
4.0
2.0
3.3
2.8
4.2
5.9
4.4
4.8
2.5
3.1
5.1
4.0
4.6
2.7
2.4
4.4
5.5
5.6
5.3
3.4
2.5
3.6
4.9
5.8
5.7
4.1
4.0
3.3
2.2
5.3
5.1
4.8
5.8
2.4
2.6
5.3
3.7
2.1
5.2
3.9
4.6
3.5
4.2
5.9
4.0
5.6
3.3
3.7
4.7
4.6
2.6
5.5
3.6
2.1
2.8
4.6
5.0
2.2
3.9
2.8
5.3
5.1
5.8
2.4
5.9
2.7
4.3
5.7
4.5
5.2
4.2
2.5
2.9
2.3
3.6
5.4
2.9
5.7
4.4
3.7
2.8
4.6
3.0
3.9
5.7
3.5
3.6
3.6
2.9
3.4
2.6
2.9
4.1
4.9
5.1
3.6
2.4
4.8
4.6
3.6
4.9
4.5
4.0
3.9
4.7
4.5
2.9
4.1
3.7
3.3
2.3
5.3
4.9
5.1
4.0
5.1
5.4
3.4
3.7
5.6
5.4
2.0
3.2
5.0
4.1
3.6
4.3
5.9
5.9
2.8
5.7
4.0
2.7
2.9
5.9
3.7
5.6
4.4
3.0
4.1
5.5
3.9
2.9
5.1
4.5
3.5
5.5
4.9
4.0
4.6
2.6
2.0
4.2
5.2
2.2
3.6
5.3
2.3
5.1
3.5
2.4
5.1
2.8
2.4
5.3
3.4
2.7
2.7
5.0
5.0
2.6
3.1
5.0
3.8
4.7
2.1
4.6
2.7
3.3
2.9
2.4
2.9
4.5
2.1
2.9
2.6
4.1
2.0
4.1
4.9
4.7
3.0
3.0
5.7
5.9
2.4
2.4
3.6
3.0
3.9
5.1
5.0
4.3
2.6
2.0
4.3
2.4
3.7
3.1
4.9
3.4
3.9
4.0
5.6
5.5
4.3
2.4
5.3
2.3
2.6
2.3
4.4
4.2
4.4
2.1
4.3
4.8
5.7
2.8
4.4
2.6
2.8
5.2
4.0
2.7
5.0
5.7
3.4
4.8
3.9
4.6
2.4
3.0
2.8
2.3
5.4
5.4
5.3
2.5
2.4
3.1
2.0
3.3
2.8
3.6
4.0
4.4
5.8
3.0
5.0
4.2
3.1
5.5
3.4
3.5
5.3
4.0
5.6
5.4
4.4
2.9
4.2
5.4
2.6
2.1
2.4
4.6
4.4
3.5
3.1
5.6
3.9
3.0
2.9
2.1
2.0
5.7
4.0
2.1
5.3
2.5
5.8
2.1
3.9
2.3
2.2
4.9
4.8
2.4
2.6
3.4
4.0
5.0
3.2
5.0
2.2
2.9
5.6
4.1
3.1
3.7
4.2
3.3
4.2
2.8
5.7
2.3
3.8
3.3
4.0
2.5
5.2
5.8
5.0
4.6
4.3
4.5
2.4
5.7
4.9
3.9
2.3
2.2
4.2
5.6
2.5
2.2
3.3
2.9
3.6
5.8
4.0
4.4
2.8
3.5
2.4
3.5
5.5
4.8
5.2
5.3
2.1
2.4
5.0
3.6
4.3
2.7
5.5
3.1
2.1
5.4
2.2
4.1
4.4
2.2
5.6
4.6
5.8
4.1
3.7
4.9
4.6
5.0
4.3
4.4
3.6
4.9
5.0
2.4
4.5
2.7
2.1
4.6
4.5
2.0
2.0
5.4
4.1
2.9
2.7
3.9
4.0
2.9
2.1
3.2
5.6
2.6
3.6
2.8
2.3
5.9
3.6
3.9
3.3
4.5
4.5
5.3
3.8
2.8
4.9
5.5
2.5
3.5
5.0
2.4
5.3
4.8
2.7
2.3
2.8
3.3
2.5
3.8
3.5
4.0
5.6
5.6
5.7
5.0
2.4
2.8
4.7
3.8
4.9
3.0
2.9
5.2
5.2
4.9
2.4
3.9
5.1
5.1
3.4
3.2
2.1
2.8
5.0
4.0
2.0
5.6
5.8
3.6
4.3
2.9
5.2
4.9
5.0
2.3
4.3
4.9
5.9
2.8
4.9
3.8
3.7
4.5
5.5
2.2
4.9
4.2
4.1
3.6
5.0
5.7
5.0
3.8
3.2
2.2
5.4
5.7
4.5
4.0
4.8
5.4
4.4
4.9
2.0
4.6
3.3
4.8
4.1
4.5
3.6
5.7
4.2
2.3
2.4
5.2
5.3
4.0
2.2
2.7
4.1
2.3
4.2
4.6
2.2
4.6
5.5
3.5
5.5
4.7
3.2
4.3
2.5
2.4
2.7
2.2
4.6
2.9
2.4
2.3
5.0
3.9
4.8
4.6
3.1
3.2
2.2
2.1
4.1
3.9
4.5
2.5
3.6
2.6
2.2
4.2
3.6
2.4
4.9
5.1
4.7
3.4
5.9
4.7
3.7
4.4
2.0
2.3
3.7
4.3
2.9
5.9
3.9
5.7
4.3
2.1
2.6
2.1
5.3
4.2
4.2
5.4
2.7
2.0
5.9
2.5
2.6
3.7
3.5
5.7
4.3
4.9
3.6
5.9
5.6
5.8
3.5
5.4
5.9
2.0
2.3
3.8
2.3
3.1
3.1
3.5
5.9
4.6
3.6
4.5
3.3
4.6
2.1
5.5
5.8
5.5
2.7
3.1
3.3
3.3
2.5
2.8
5.8
4.9
4.1
3.0
2.1
5.9
3.5
2.9
4.6
3.3
3.7
5.8
2.6
5.5
2.7
5.5
2.8
5.5
2.2
5.5
4.5
5.4
4.7
4.8
2.9
4.4
5.0
4.8
4.8
5.7
4.5
5.9
4.3
2.4
4.0
5.7
4.6
3.2
2.1
5.4
4.7
2.9
3.9
3.4
4.2
4.3
5.5
3.4
3.0
4.4
3.1
3.6
4.8
2.1
5.8
3.6
3.1
3.7
5.9
3.0
4.6
5.9
3.4
2.5
3.9
5.8
5.2
3.2
5.0
3.1
3.4
5.7
4.7
4.0
3.4
4.0
3.4
2.7
2.1
5.5
5.2
2.9
4.7
5.1
2.8
4.5
5.9
5.9
5.8
3.0
3.6
4.3
2.1
5.7
3.4
2.2
2.1
3.7
4.5
5.8
2.1
4.4
4.8
2.5
5.0
5.9
3.4
2.6
4.7
4.7
3.2
4.0
5.4
5.1
3.6
2.9
3.5
2.9
2.2
3.1
2.3
2.1
4.1
5.2
5.5
3.5
2.3
3.6
3.6
4.3
5.5
3.2
5.2
3.6
2.1
4.1
4.0
4.3
5.2
4.2
2.2
4.4
5.3
3.7
3.1
3.7
4.4
3.1
5.1
2.3
3.4
2.9
3.6
3.7
2.1
4.6
4.9
4.3
3.0
2.8
4.2
5.3
3.7
2.7
4.2
4.0
2.5
3.8
3.8
2.6
5.3
2.9
2.0
3.6
5.0
5.1
4.2
3.4
4.6
2.0
4.7
4.3
4.6
2.4
2.5
4.6
4.8
2.8
2.5
4.5
5.4
5.3
4.9
3.6
4.3
2.9
5.5
2.7
4.6
3.1
2.6
3.8
5.5
5.6
4.1
3.2
2.5
5.5
2.4
5.7
5.9
5.7
4.9
2.9
2.8
3.9
5.2
2.4
2.0
3.1
4.7
5.9
3.1
2.4
5.3
5.0
2.9
3.5
5.9
5.1
5.9
3.6
2.9
4.8
2.1
2.8
4.1
5.5
5.1
5.0
2.3
2.1
4.7
2.3
2.0
3.5
3.1
5.5
5.3
3.7
5.1
5.4
3.1
2.6
5.4
4.7
4.9
2.9
4.1
4.2
2.9
2.4
5.8
4.0
3.7
5.5
4.0
5.8
2.7
4.8
3.0
2.2
3.1
5.9
3.4
3.5
3.4
4.2
4.4
4.1
3.6
3.5
2.6
2.6
2.7
3.6
5.8
5.6
3.7
4.6
2.2
3.0
5.3
4.0
4.6
3.6
5.3
2.7
3.6
5.9
2.0
4.6
2.4
5.4
3.1
2.7
4.3
3.9
2.6
5.6
3.2
3.6
5.5
2.7
5.8
3.1
4.4
3.9
2.0
4.7
2.1
5.2
3.8
3.2
2.5
4.7
3.5
2.5
4.4
4.6
2.3
4.3
3.5
3.2
3.6
4.8
2.2
3.8
4.8
4.1
5.9
4.5
2.8
2.6
4.9
3.5
2.4
4.7
3.1
2.4
5.9
5.8
4.8
5.9
2.7
4.9
5.3
5.4
5.6
4.7
5.2
3.0
4.3
5.1
3.8
5.8
5.0
5.6
3.5
3.2
3.3
3.1
3.2
3.4
5.6
4.3
3.4
4.0
2.3
2.9
5.3
4.5
3.9
2.2
3.3
5.5
2.1
3.8
4.8
5.0
2.3
4.5
2.1
3.7
3.5
2.3
2.3
4.6
2.6
2.9
3.3
2.8
4.7
3.9
4.5
3.6
3.4
5.1
3.8
2.5
5.4
5.4
5.1
3.6
3.3
2.0
4.4
4.8
3.1
4.6
3.8
4.4
4.1
4.1
3.1
5.0
2.3
4.3
5.1
2.6
4.6
5.7
2.7
3.2
2.7
3.4
2.0
4.2
2.4
3.6
4.2
2.5
5.2
4.5
4.0
2.8
2.5
4.5
3.7
2.7
5.9
2.4
3.0
4.5
2.3
2.8
4.2
4.3
2.5
4.9
4.2
4.0
2.9
4.4
5.1
4.3
3.6
5.3
3.1
5.7
5.7
4.3
2.7
5.9
2.9
4.9
4.5
5.8
2.3
5.8
4.6
4.8
5.2
5.4
2.5
2.8
4.7
3.2
3.7
2.1
3.7
4.5
2.5
5.9
4.7
3.5
4.0
4.8
5.1
3.7
4.5
5.9
2.1
5.2
4.6
3.1
2.2
5.6
5.0
3.0
5.4
5.6
2.0
5.2
2.2
3.5
3.2
5.5
3.4
3.2
4.8
2.1
2.1
4.3
5.1
4.1
4.1
2.8
3.5
5.6
2.2
2.0
4.7
2.9
4.5
4.7
4.9
4.7
5.1
4.5
2.8
2.1
3.6
5.2
4.0
3.7
3.7
2.3
3.3
3.7
5.5
4.7
3.5
4.4
2.8
5.9
2.7
2.0
4.1
2.9
2.3
2.7
2.2
2.9
3.7
5.0
5.7
4.8
5.4
2.7
4.1
4.5
2.2
3.0
2.1
5.1
3.3
5.1
3.1
4.1
5.5
4.9
5.9
3.1
2.7
4.7
4.7
4.0
4.7
4.7
3.9
5.5
3.5
2.7
4.6
5.1
3.1
4.3
3.8
5.8
2.7
4.9
2.5
2.3
5.0
2.2
3.6
2.6
4.6
5.8
3.4
2.7
2.4
4.2
4.4
2.6
5.3
2.8
3.2
4.9
4.7
2.0
3.8
4.5
4.2
4.1
4.5
2.8
4.7
4.6
5.8
5.7
3.7
5.3
3.1
3.4
5.3
3.0
2.4
2.1
4.1
2.3
4.1
2.8
2.9
5.6
4.2
5.4
3.4
4.6
5.0
3.6
5.1
4.2
2.6
3.8
4.6
3.7
4.2
4.2
5.5
3.6
2.5
5.3
5.6
5.6
3.9
5.3
2.6
2.7
4.2
4.7
2.0
3.3
5.1
2.1
4.5
5.8
4.8
2.5
3.9
4.6
2.0
5.7
4.7
5.3
3.5
3.7
3.1
5.9
3.1
4.1
4.1
5.1
5.9
3.8
3.9
3.9
2.8
2.9
5.1
4.4
2.0
3.1
4.7
3.7
5.1
4.3
4.2
2.1
4.2
2.8
3.5
2.3
3.5
3.2
4.3
5.1
4.3
2.3
4.2
5.2
2.8
2.0
5.5
4.8
2.3
4.3
2.4
3.4
3.2
5.6
3.0
3.5
4.6
2.6
3.0
5.2
3.1
3.8
4.9
3.1
3.7
3.9
5.0
3.0
5.7
2.9
2.3
5.7
2.6
4.1
4.8
4.1
3.7
2.4
5.7
3.4
4.2
5.0
4.6
4.2
4.6
5.1
2.0
4.5
3.6
3.7
2.7
4.3
2.9
3.6
2.9
4.1
3.4
5.3
4.9
2.8
3.8
4.6
5.7
5.3
4.8
3.8
2.6
2.2
5.6
2.6
4.6
5.0
2.3
5.7
5.7
4.1
3.3
2.1
3.2
2.6
4.3
2.7
2.5
5.0
3.4
2.9
4.5
4.4
3.5
4.9
5.1
4.8
4.5
4.6
4.0
2.9
3.3
5.6
3.1
4.0
4.7
4.2
4.1
4.9
5.8
3.7
5.4
5.6
3.9
2.4
3.0
2.3
3.6
2.1
2.2
4.4
3.5
5.7
3.1
3.4
4.1
4.8
3.9
5.7
4.0
5.2
2.9
3.1
4.3
2.6
2.7
2.8
3.3
4.3
5.0
3.9
4.8
2.1
2.4
3.2
3.4
5.3
4.6
3.4
5.8
5.5
4.8
2.8
2.1
5.8
3.5
5.8
5.1
2.9
2.3
5.9
3.1
4.6
2.2
5.0
5.3
5.2
3.2
2.2
3.6
3.5
5.1
2.7
4.0
4.7
4.0
4.0
5.7
3.1
2.2
2.9
5.5
2.8
2.7
4.9
4.7
5.9
5.8
5.3
3.0
5.0
3.0
3.5
5.7
5.3
2.6
2.3
5.0
2.0
4.0
3.1
5.4
3.0
2.6
5.5
2.1
2.3
3.1
4.0
2.1
4.8
4.1
3.1
3.5
2.2
5.1
5.5
5.8
5.2
5.9
5.8
2.1
4.2
4.4
2.3
5.1
2.7
4.1
4.7
2.6
5.1
5.2
5.2
5.1
2.8
5.9
3.5
5.3
2.9
4.2
3.1
5.7
4.1
5.8
2.3
4.5
2.8
2.8
5.8
4.0
4.0
4.3
3.3
3.1
5.7
4.2
4.9
2.5
5.0
4.6
2.9
2.3
3.3
2.8
2.9
2.2
2.5
5.6
2.1
4.3
5.5
3.9
2.5
2.9
5.3
4.2
5.3
2.5
4.0
2.4
3.2
4.0
5.4
2.9
5.0
5.5
2.1
4.9
3.6
5.6
4.0
4.4
4.7
4.1
4.9
3.2
5.5
5.6
5.0
2.5
2.0
5.5
5.9
5.0
2.8
5.9
5.7
5.3
3.3
4.6
4.2
5.1
2.3
3.2
5.6
5.5
2.3
5.6
3.6
4.5
3.6
In [148]:
float('1*k+3')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-148-052d373676d3> in <module>()
----> 1 float('1*k+3')
ValueError: could not convert string to float: '1*k+3'
In [ ]:
Content source: ekostat/ekostat_calculator
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