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License: OTHER
Kernel: SageMath 7.3

Sequential update of worksheet files

This worksheet exports other worksheets to .sage files in order to save time when executing every particular worksheet. It can also be used to reproduce all computational results with one click, just click on Cell -> Run All. This converts all other worksheets to .sage files and them loads them into the current worksheet, which executes all the code in each worksheet. This can take tens of minutes! 

def fun_export_load_ipynb(list_wsnames, folder, load1 = True):
    '''
    Exports worksheet 'name' to .sage file in specified folder and then imports it 
    into current worksheet, executing all code and displaying its output. 
    If you only want to export but not load the files, set option load1 = False.
    '''
    for name in list_wsnames:
        fun_export_ipynb(name, folder)
        print 'created file ' + folder + name
        if load1:
            load(folder + name + '.sage')
            print 'imported file ' + folder + name
import json   # for reading notebook metadata
def fun_export_ipynb(worksheet, folder):
    '''
    Exports worksheet.ipynb to a .py or .sage depending on kernel.
    Example:
    fun_export_ipynb('Worksheet_setup', 'temp/')
    Need to import json before using this function. Unless option output=True is given,
    each line of text has a semicolon added in the .py file, preventing any output.
    '''
    str1 = 'jupyter nbconvert  --to=python \'' + worksheet+'.ipynb\''
    print str1
    print 'Exporting specified worksheet to .py file...'
    
    try:
        retcode = os.system(str1)
        if retcode < 0:
            print >>sys.stderr, "nbconvert was terminated by signal", -retcode
        else:
            print >>sys.stderr, "nbconvert returned", retcode
    except OSError as e:
        print >>sys.stderr, "Execution failed:", e
        print >>sys.stderr, "Trying ipython nbconvert instead..."
        str1 = 'ipython nbconvert  --to script \'' + worksheet+'.ipynb\''    # for new version of python
        try:      
            retcode = os.system(str1)
            if retcode < 0:
                print >>sys.stderr, "nbconvert was terminated by signal", -retcode
            else:
                print >>sys.stderr, "nbconvert returned", retcode
        except OSError as e:
            print >>sys.stderr, "Execution failed:", e              

    str1 = worksheet + '.py'
    
    print 'Checking if specified ipynb file was run with sage kernel...'
    with open(worksheet+'.ipynb') as data_file:    
        data = json.load(data_file)
    
    if data['metadata']['kernelspec']['name'][0:4] == 'sage':
        print 'Renaming .py file to .sage if notebook kernel was sage (to avoid exponent error)'
        str2 = folder + worksheet + '.sage'
        os.rename(str1, str2)

def fun_export_load_ipynb(list_wsnames, folder, load1 = True):
    '''
    Exports worksheet 'name' to .sage file in specified folder and then imports it 
    into current worksheet, executing all code and displaying its output. 
    If you only want to export but not load the files, set option load1 = False.
    '''
    for name in list_wsnames:
        fun_export_ipynb(name, folder)
        print 'created file ' + folder + name
        if load1:
            load(folder + name + '.sage')
            print 'imported file ' + folder + name

fun_export_load_ipynb(['Worksheet_setup'], 'temp/', load1 = True)
jupyter nbconvert --to=python 'Worksheet_setup.ipynb' Exporting specified worksheet to .py file...
nbconvert returned 0
Checking if specified ipynb file was run with sage kernel... Renaming .py file to .sage if notebook kernel was sage (to avoid exponent error) created file temp/Worksheet_setup imported file temp/Worksheet_setup
/home/sschyman/Programs/sage-upgrade/local/lib/python2.7/site-packages/traitlets/traitlets.py:809: DeprecationWarning: A parent of InlineBackend._config_changed has adopted the new (traitlets 4.1) @observe(change) API clsname, change_or_name), DeprecationWarning)

Executing all worksheets in the correct order to reproduce figures saved in subfolder figures

Below, list_wsnames contains a list of worksheet names that need to be executed in the specified order to recreate all figures in the directory /figures. This will take a few minutes. If you only want to export the worksheets as .sage files to the temp folder, just change load1 = True to load1 = False.

list_wsnames = ['leaf_enbalance_eqs', 'E_PM_eqs', 'stomatal_cond_eqs', 'leaf_chamber_eqs', 'leaf_chamber_data', 'Tables_of_variables' ]
folder = 'temp/'
fun_export_load_ipynb(list_wsnames, folder, load)
jupyter nbconvert --to=python 'leaf_enbalance_eqs.ipynb' Exporting specified worksheet to .py file...
nbconvert returned 0
Checking if specified ipynb file was run with sage kernel... Renaming .py file to .sage if notebook kernel was sage (to avoid exponent error) created file temp/leaf_enbalance_eqs
44100.0000000000kilogram*meter^2/(mole*second^2)
meter/second == meter/second
mole/meter^3 == mole/meter^3
mole/(meter^2*second) == mole/(meter^2*second)
mole/(meter^2*second) == mole/(meter^2*second)
mole/(meter^2*second) == mole/(meter^2*second)
mole/meter^3 == mole/meter^3
kilogram/(kelvin*second^3) == kilogram/(kelvin*second^3)
1 == 1
1 == 1
kilogram/meter^3 == kilogram/meter^3
kilogram/(meter*second^2) == kilogram/(meter*second^2)
kilogram/(meter*second^2) == kilogram/(meter*second^2)
(1.13400000000000e-7)*T_l^4 - 900.306522304087 C_wa 1.29441408346663 C_wl 1.91570361006325 D_va 0.0000248765000000000 E_l 185.424519010311 E_lmol 0.00420463761928142 H_l 325.157459266011 L_l 0.0300000000000000 Le 0.888469037042992 M_N2 0.0280000000000000 M_O2 0.0320000000000000 M_w 0.0180000000000000 Nu 26.1863624980041 P_a 101325 P_wa 3212.56734153661 P_wl 4868.42309771766 Pr 0.710000000000000 R_ll 89.4180217236781 R_mol 8.31447200000000 R_s 600 Re 1927.40122068744 Re_c 3000 T_a 298.500000000000 T_l 305.650648423 T_w 298.500000000000 a_s 1.00000000000000 a_sh 2 alpha_a 0.0000221020000000000 c_pa 1010 epsilon_l 1 g 9.81000000000000 g_bw 0.0209367439791524 g_sw 0.0100000000000000 g_tw 0.00676759777734245 h_c 22.7362219510171 k_a 0.0260474000000000 lambda_E 2.45000000000000e6 nu_a 0.0000155650000000000 rho_a 1.16339248053449 sigm 5.67000000000000e-8 v_w 1
jupyter nbconvert --to=python 'leaf_enbalance_eqs.ipynb' Exporting specified worksheet to .py file...
nbconvert returned 0
Checking if specified ipynb file was run with sage kernel... Renaming .py file to .sage if notebook kernel was sage (to avoid exponent error) imported file temp/leaf_enbalance_eqs jupyter nbconvert --to=python 'E_PM_eqs.ipynb' Exporting specified worksheet to .py file...
nbconvert returned 0
Checking if specified ipynb file was run with sage kernel... Renaming .py file to .sage if notebook kernel was sage (to avoid exponent error) created file temp/E_PM_eqs
[ [P_wl == (Delta_eTa*P_wa*f_u + P_was*f_u*gamma_v - Delta_eTa*R_ll + Delta_eTa*R_s)/(Delta_eTa*f_u + f_u*gamma_v), E_w == -((P_wa - P_was)*f_u*gamma_v + Delta_eTa*R_ll - Delta_eTa*R_s)/(Delta_eTa + gamma_v), beta_B == -((P_wa - P_was)*f_u - R_ll + R_s)*gamma_v/((P_wa - P_was)*f_u*gamma_v + Delta_eTa*R_ll - Delta_eTa*R_s)] ]
kelvin == kelvin
kilogram/second^3 == kilogram/second^3
kilogram/second^3 == kilogram/second^3
kilogram/(meter*second^2) == kilogram/(meter*second^2)
kilogram/second^3 == kilogram/second^3
kilogram/second^3 == kilogram/second^3
kilogram/second^3 == kilogram/second^3
kilogram/second^3 == kilogram/second^3
kilogram/second^3 == kilogram/second^3
kilogram/(meter*second^2) == kilogram/(meter*second^2)
kelvin == kelvin
meter/second == meter/second
$ g $ $ v_{w} $ $ {P_{wa}} $ $ {a_{sh}} $ $ M_{O_{2}} $ $ T_{a} $ $ {R_{mol}} $ $ T_{w} $ $ R_{s} $ $ {c_{pa}} $ $ M_{N_{2}} $ $ \epsilon_{l} $ $ {\sigma} $ $ a_{s} $ $ P_{a} $ $ {N_{Pr}} $ $ {N_{Re_c}} $ $ M_{w} $ $ \lambda_{E} $ $ \epsilon $ $ {g_{sw}} $ $ L_{l} $ $ g $ $ v_{w} $ $ {P_{wa}} $ $ {a_{sh}} $ $ M_{O_{2}} $ $ T_{a} $ $ {R_{mol}} $ $ T_{w} $ $ R_{s} $ $ {c_{pa}} $ $ M_{N_{2}} $ $ \epsilon_{l} $ $ {\sigma} $ $ a_{s} $ $ P_{a} $ $ {N_{Pr}} $ $ {N_{Re_c}} $ $ M_{w} $ $ \lambda_{E} $ $ \epsilon $ $ {g_{sw}} $ $ L_{l} $ T_l = 305.650648423 E_l = 185.424519010311 H_l = 325.157459266011 R_ll = 89.4180217236781 Direct estimates: E_l == 201.520517691209 H_l == 398.479482308791 T_l == 307.263098002106 P_wl == 4888.37878666472 Using estimated T_l: 5332.58270455183 E_l == 254.937149826832 H_l == 398.479482308791 0 == 110.468903558398 Using estimated T_l only to calculate R_ll: E_l == 164.417599968095 H_l == 325.113496473507 307.263098002106 == 305.649681621994 Using 1 iteration to get T_l: T_l(R_ll=0): 307.263098002106 R_ll(T_l) = 110.468903558398 T_l = 305.649681621994 E_l == 199.088662380171 H_l == 325.113496473508 185.424519010311 == 199.120796183645 185.424519010311 == 185.424519010311 T_l = 305.650648423 E_l = 185.424519010311 H_l = 325.157459266011 R_ll = 89.4180217236781 Direct estimates: E_l == 201.520517691209 H_l == 398.479482308791 T_l == 307.263098002106 Using estimated T_l: E_l == 236.648612148471 H_l == 398.479482308791 0 == 110.468903558398 Using estimated T_l only to calculate R_ll: E_l == 156.390826464557 H_l == 333.140269977045 307.263098002106 == 305.826201131718 Using 1 iteration to get T_l: T_l(R_ll=0): 307.479451379050 R_ll(T_l) = 113.318783813752 T_l = 305.783550529736 E_l == 189.502633426895 H_l == 331.200842871040
kilogram/second^3 == kilogram/second^3
kilogram/second^3 == kilogram/second^3
kilogram/second^3 == kilogram/second^3 E_{l} = -\frac{{\left({P_{wa}} - {P_{was}}\right)} c_{E} c_{H} + {\left({\Delta_{eTa}} {R_{ll}} - {\Delta_{eTa}} R_{s}\right)} c_{E}}{{\Delta_{eTa}} c_{E} + c_{H}}
kilogram/second^3 == kilogram/second^3 H_{l} = \frac{{\left({\left({P_{wa}} - {P_{was}}\right)} c_{E} - {R_{ll}} + R_{s}\right)} c_{H}}{{\Delta_{eTa}} c_{E} + c_{H}}
kelvin == kelvin T_{l} = \frac{{\left({\Delta_{eTa}} T_{a} + {P_{wa}} - {P_{was}}\right)} c_{E} + T_{a} c_{H} - {R_{ll}} + R_{s}}{{\Delta_{eTa}} c_{E} + c_{H}} 0 == 0 0 == 0 0 == 0 [ [E_l == -(Delta_eTa*(R_ll - R_s)*c_E + (P_wa*c_E - P_was*c_E)*c_H)/(Delta_eTa*c_E + c_H), H_l == (P_wa*c_E - P_was*c_E - R_ll + R_s)*c_H/(Delta_eTa*c_E + c_H), P_wl == ((P_wa*c_E - R_ll + R_s)*Delta_eTa + P_was*c_H)/(Delta_eTa*c_E + c_H), T_l == (Delta_eTa*T_a*c_E + P_wa*c_E - P_was*c_E + T_a*c_H - R_ll + R_s)/(Delta_eTa*c_E + c_H)] ]
kilogram/second^3 == kilogram/second^3 E_{l} = -\frac{{\Delta_{eTa}} {\left({R_{ll}} - R_{s}\right)} c_{E} + {\left({P_{wa}} c_{E} - {P_{was}} c_{E}\right)} c_{H}}{{\Delta_{eTa}} c_{E} + c_{H}}
kilogram/second^3 == kilogram/second^3 H_{l} = \frac{{\left({P_{wa}} c_{E} - {P_{was}} c_{E} - {R_{ll}} + R_{s}\right)} c_{H}}{{\Delta_{eTa}} c_{E} + c_{H}}
kilogram/(meter*second^2) == kilogram/(meter*second^2) {P_{wl}} = \frac{{\left({P_{wa}} c_{E} - {R_{ll}} + R_{s}\right)} {\Delta_{eTa}} + {P_{was}} c_{H}}{{\Delta_{eTa}} c_{E} + c_{H}}
kelvin == kelvin T_{l} = \frac{{\Delta_{eTa}} T_{a} c_{E} + {P_{wa}} c_{E} - {P_{was}} c_{E} + T_{a} c_{H} - {R_{ll}} + R_{s}}{{\Delta_{eTa}} c_{E} + c_{H}} 0 == 0 0 == 0 0 == 0 [ [E_l == -(Delta_eTa*(R_ll - R_s)*c_E + (P_wa*c_E - P_was*c_E)*c_H)/(Delta_eTa*c_E + c_H), H_l == (P_wa*c_E - P_was*c_E - R_ll + R_s)*c_H/(Delta_eTa*c_E + c_H), P_wl == ((P_wa*c_E - R_ll + R_s)*Delta_eTa + P_was*c_H)/(Delta_eTa*c_E + c_H), T_l == (Delta_eTa*T_a*c_E + P_wa*c_E - P_was*c_E + T_a*c_H - R_ll + R_s)/(Delta_eTa*c_E + c_H)] ]
numerical solution: T_l = 308.321395271 E_l = 180.542235053941 H_l = 150.521099595469 R_ll = 68.9366653505872 g_bw = 0.0131620455576424 g_tw = 0.00291849206962754 Direct estimates: E_l = 198.222104889662 H_l = 201.777895110338 T_l == 310.133484539870 T_l == 310.133484539870 T_l == 309.093414355984 400 == 400.000000000000 Using T_l from eq_Tl_Delta: T_l = 310.133484539870 P_wl = 6256.38161942359 E_l = 216.096641092879 H_l = 201.777895110338 R_ll = 93.2413750164809 400 == 511.115911219698 Using T_l from eq_Tl_Delta only to calculate R_ll: T_l = 310.133484539870 R_ll = 93.2413750164809 E_l = 169.892070077829 H_l = 136.866554905691 400 == 400.000000000000 Using T_l from eq_Tl_Delta2: T_l = 309.093414355984 P_wl = 5906.50495424572 E_l = 198.222104889661 H_l = 172.358447812369 R_ll = 79.2391719599513 400 == 449.819724661981 Using T_l from eq_Tl_Delta2 only to calculate R_ll: T_l = 309.093414355984 R_ll = 79.2391719599513 E_l = 174.146435695707 H_l = 146.614392344342 400 == 400.000000000000 T_l = 305.650648423 E_l = 185.424519010311 H_l = 325.157459266011 R_ll = 89.4180217236781 Direct estimates: E_l == 201.520517691209 H_l == 398.479482308791 T_l == 307.263098002106 Using estimated T_l: E_l == 254.937149826832 H_l == 398.479482308791 0 == 110.468903558398 Using estimated T_l only to calculate R_ll: E_l == 164.417599968095 H_l == 325.113496473507 307.263098002106 == 305.649681621994 Using 1 iteration to get T_l: T_l(R_ll=0): 307.263098002106 R_ll(T_l) = 110.468903558398 T_l = 305.649681621994 E_l == 199.088662380171 H_l == 325.113496473508
kilogram/second^3 == kilogram/second^3
kilogram/second^3 == kilogram/second^3
kilogram/second^3 == kilogram/second^3
1.08204645272521
Image in a Jupyter notebook
T_l = 308.321395271 E_l = 180.542235053941 H_l = 150.521099595469 R_ll = 68.9366653505872 g_bw = 0.0131620455576424 g_tw = 0.00291849206962754 Direct estimates: E_l = 198.222104889662 H_l = 201.777895110338 T_l == 310.133484539870 T_l == 310.133484539870 T_l == 309.093414355984 400 == 400.000000000000 Penman-stomata: E_l = 198.222104889662 H_l = 201.777895110338 T_l = 310.133484539870 400 == 400.000000000000 PM-equation: E_l = 241.448619283973 H_l = 158.551380716027 400 == 400.000000000000 MU-equation: E_l = 156.668183937778 H_l = 243.331816062222 400 == 400.000000000000 Corrected MU-equation: E_l = 195.741933442269 H_l = 204.258066557731 400 == 400.000000000000 4*T_l^3*a_sh*epsilon_l*sigm [ Rll1 == -3*T1^4*a_sh*epsilon_l*sigm - T_w^4*a_sh*epsilon_l*sigm ]
Image in a Jupyter notebook
kilogram/second^3 == kilogram/second^3
kilogram/second^3 == kilogram/second^3
kilogram/second^3 == kilogram/second^3
kilogram/second^3 == kilogram/second^3
kelvin == kelvin
T_l = 308.321395271 E_l = 180.542235053941 H_l = 150.521099595469 R_ll = 68.9366653505872 g_bw = 0.0131620455576424 g_tw = 0.00291849206962754 Direct estimates: E_l = 177.353891811830 H_l = 153.963492920038 T_l = 308.443094724766 R_ll = 68.6826152681320 308.443094724766 == 308.443094724766 308.443094724766 == 308.443094724766 308.443094724766 == 307.807922524752 400 == 400.000000000000 Using T_l from eq_Tl_Delta_Rlllin.rhs() only to calculate R_ll: T_l = 308.443094724766 R_ll = 70.5556017512511 E_l = 176.784812127415 H_l = 152.659586121334 400 == 400.000000000000 Penman-stomata: E_l = 198.222104889662 H_l = 201.777895110338 T_l = 310.133484539870 400 == 400.000000000000 PM-equation: E_l = 241.448619283973 H_l = 158.551380716027 400 == 400.000000000000 MU-equation: E_l = 156.668183937778 H_l = 243.331816062222 400 == 400.000000000000 Corrected MU-equation: E_l = 195.741933442269 H_l = 204.258066557731 400 == 400.000000000000 imported file temp/E_PM_eqs jupyter nbconvert --to=python 'stomatal_cond_eqs.ipynb' Exporting specified worksheet to .py file...
nbconvert returned 0
Checking if specified ipynb file was run with sage kernel... Renaming .py file to .sage if notebook kernel was sage (to avoid exponent error) created file temp/stomatal_cond_eqs
mole/(meter*second) == mole/(meter*second)
mole/(meter^2*second) == mole/(meter^2*second)
meter^2 == meter^2
meter^2*second/mole == meter^2*second/mole
meter^2*second/mole == meter^2*second/mole
imported file temp/stomatal_cond_eqs jupyter nbconvert --to=python 'leaf_chamber_eqs.ipynb' Exporting specified worksheet to .py file...
nbconvert returned 0
Checking if specified ipynb file was run with sage kernel... Renaming .py file to .sage if notebook kernel was sage (to avoid exponent error) created file temp/leaf_chamber_eqs C_wa 0.647207041733317 C_wl 1.12479267904924 D_va 0.0000248765000000000 E_l 142.258640360008 E_lmol 0.00322581950929723 H_l -112.722448184652 L_l 0.0300000000000000 Le 0.888469037042992 M_N2 0.0280000000000000 M_O2 0.0320000000000000 M_w 0.0180000000000000 Nu 26.1863624980041 P_a 101325 P_wa 1606.28367076831 P_wl 2768.40610422238 Pr 0.710000000000000 R_ll -29.5361921753575 R_mol 8.31447200000000 R_s 0.000000000000000 Re 1927.40122068744 Re_c 3000 T_a 298.500000000000 T_l 296.021082253 T_w 298.500000000000 a_s 1.00000000000000 a_sh 2 alpha_a 0.0000221020000000000 c_pa 1010 epsilon_l 1 g 9.81000000000000 g_bw 0.0208112438130012 g_sw 0.0100000000000000 g_tw 0.00675443157676732 h_c 22.7362219510171 k_a 0.0260474000000000 lambda_E 2.45000000000000e6 nu_a 0.0000155650000000000 rho_a 1.17040820486688 sigm 5.67000000000000e-8 v_w 1 Volume = 0.310000000000000 l min flow rate for flushing = 1.55000000000000e-11 m3/s min flow rate for flushing = 0.930000000000000 l/min flow rate for 1 m/s direct wind = 0.00150000000000000 m3/s flow rate for 1 m/s direct wind = 90.0000000000000 l/m flow rate for 5 m/s direct wind = 0.00750000000000000 m3/s flow rate for 5 m/s direct wind = 450.000000000000 l/m Volume = 0.400000000000000 l min flow rate for flushing = 2.00000000000000e-11 m3/s min flow rate for flushing = 1.20000000000000 l/min flow rate for 1 m/s direct wind = 0.00150000000000000 m3/s flow rate for 1 m/s direct wind = 90.0000000000000 l/m
[ [R_d == S_s, R_lu == -S_a + S_s, R_ld == S_b, R_u == 0] ] B_l == 0.0106559443973775 B_l == 0.00119137080184970
meter^3 == meter^3
mole/second == mole/second
mole/second == mole/second
mole/second == mole/second
mole/second == mole/second
meter^3/second == meter^3/second
kilogram/second^3 == kilogram/second^3 [ T_out == (F_in_mola*M_air*T_in*c_pa + F_in_molw*M_w*T_in*c_pv + H_l*L_A + Q_in)/(E_lmol*L_A*M_w*c_pv + F_in_mola*M_air*c_pa + F_in_molw*M_w*c_pv) ]
T_out == (F_in_v*M_w*P_w_in*T_in*c_pv + H_l*L_A*R_mol*T_in + Q_in*R_mol*T_in + (F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*T_in*c_pa)/(E_lmol*L_A*M_w*R_mol*T_in*c_pv + F_in_v*M_w*P_w_in*c_pv + (F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*c_pa)
[ F_out_v == (E_lmol*L_A*R_mol*T_in*T_out + F_in_v*P_a*T_out)/(P_a*T_in) ]
[ T_in == (F_in_v*M_w*P_w_in*T_out*c_pv + (F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*T_out*c_pa)/(F_in_v*M_w*P_w_in*c_pv - (E_lmol*M_w*R_mol*T_out*c_pv - H_l*R_mol)*L_A + Q_in*R_mol + (F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*c_pa) ]
[ Q_in == ((E_lmol*M_w*R_mol*T_in*T_out*c_pv - H_l*R_mol*T_in)*L_A - ((F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*T_in - (F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*T_out)*c_pa - (F_in_v*M_w*P_w_in*T_in - F_in_v*M_w*P_w_in*T_out)*c_pv)/(R_mol*T_in) ] [ H_l == (E_lmol*L_A*M_w*R_mol*T_in*T_out*c_pv - Q_in*R_mol*T_in - ((F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*T_in - (F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*T_out)*c_pa - (F_in_v*M_w*P_w_in*T_in - F_in_v*M_w*P_w_in*T_out)*c_pv)/(L_A*R_mol*T_in) ]
[ H_l == -((F_in_mola*M_air*T_in - F_in_mola*M_air*T_out)*c_pa + (F_in_molw*M_w*T_in - F_out_molw*M_w*T_out)*c_pv + Q_in)/L_A ]
[19.1756620432331, 35.0631107274011, 62.0367042507648, 106.473009443655, 177.667645607840, 288.831780737493, 458.305321164456, 710.999051614674, 1080.07052944457, 1608.82992645281, 2352.86266113812, 3382.34604242497, 4784.52773726516, 6666.32514525647, 9156.99713166349]
mole/second == mole/second
mole/second == mole/second
meter^3/second == meter^3/second
mole/second == mole/second
meter^3/second == meter^3/second 1227.86016957787 0.000166666666666667 Volumentric flow at 0 oC: 0.000168711114309656 m3/s Volumentric flow at 25 oC: 0.000184152365848157 m3/s 25oC/0oC: 1.09152480322167 Volumentric flow at 25 oC without added vapour: 0.000181920800536946 m3/s
kilogram/(meter*second^2) == kilogram/(meter*second^2)
mole/(meter^2*second) == mole/(meter^2*second)
1227.86016957787 0.000166666666666667 Volumentric flow at 0 oC: 0.000168711114309656 m3/s Volumentric flow at 25 oC: 0.000184152365848157 m3/s 25oC/0oC: 1.09152480322167 Volumentric flow at 25 oC without added vapour: 0.000181920800536946 m3/s
0.821854415126695 jupyter nbconvert --to=python 'leaf_chamber_eqs.ipynb' Exporting specified worksheet to .py file...
nbconvert returned 0
Checking if specified ipynb file was run with sage kernel... Renaming .py file to .sage if notebook kernel was sage (to avoid exponent error) imported file temp/leaf_chamber_eqs jupyter nbconvert --to=python 'leaf_chamber_data.ipynb' Exporting specified worksheet to .py file...
nbconvert returned 0
Checking if specified ipynb file was run with sage kernel... Renaming .py file to .sage if notebook kernel was sage (to avoid exponent error) created file temp/leaf_chamber_data E_l 180.542235053941 H_l 150.521099595469 T_l 308.321395271
kilogram/second^3 == kilogram/second^3
kilogram/second^3 == kilogram/second^3 h_c(Ball): 21.8153792127944 g_vmol(Ball): 0.110506932339455 g_vmol(SS): g_twmol == 0.117381005752392 T_l(Ball): 309.125596874492 T_l(SS): 308.321395271 T_a: 303
lc_time T_in1 T_in2 T_wall_in T_wall_out T_outside T_outside_PT T_chamber T_chamber1 T_leaf1 T_leaf2 T_leaf_in Wind T_dew Air_inflow Air_outflow nr_time T_leaf_IR V_leaf_IR Rn_above_leaf Rn_below_leaf Rn_beside_leaf RnV_above_leaf RnV_below_leaf RnV_beside_leaf sens_timefirst sens_timelast water_flow_avg water_flow_std water_flow_n pow_timefirst pow_timelast fan_power_avg fan_power_std fan_power_n Comment...................................................
TS K K K K K Deg C K K K K K m/s degC SLPM SLPM TS Deg C mV W/m^2 W/m^2 W/m^2 mV mV mV ul/min ul/min ul/min TS TS W W
2014-03-25 12:39:34 23.98 23.97 22.56 22.52 22.50 25.93 22.58 21.99 19.92 21.18 16.53 0.9999 -19.32 9.052 4.451 2014-03-25 12:39:35 19.36 -0.1045 16.74 -6.927 -26.00 -0.02004 0.007208 0.03366 2014-03-25 12:39:21 2014-03-25 12:39:28 -8.233 0.1577 100 2014-03-25 12:38:37 2014-03-25 12:39:35 0.06427 0.00001266 54 Black leaf, silver perforated alu (35.4-1), varying Tdew
2014-03-25 13:01:40 23.97 23.97 22.57 22.58 22.54 26.05 22.58 22.00 20.00 21.22 16.68 1.020 -14.97 8.490 4.180 2014-03-25 13:01:41 19.36 -0.1045 16.25 -6.859 -25.52 -0.01945 0.007137 0.03303 2014-03-25 13:01:22 2014-03-25 13:01:29 -7.859 0.3323 100 2014-03-25 13:00:39 2014-03-25 13:01:38 0.06428 0.00002267 55 varying Tdew
2014-03-25 14:07:31 23.91 23.91 22.58 22.59 22.59 26.10 22.57 22.04 20.10 21.26 16.88 1.021 -10.08 8.556 4.293 2014-03-25 14:07:31 19.53 -0.09854 15.18 -6.506 -24.44 -0.01817 0.006771 0.03163 2014-03-25 14:07:11 2014-03-25 14:07:18 -7.807 0.1403 100 2014-03-25 14:06:32 2014-03-25 14:07:31 0.06429 0.00001291 54 varying Tdew
2014-03-25 16:30:31 24.25 24.24 22.76 22.68 22.67 26.14 22.77 22.26 20.43 21.52 17.38 1.028 -5.017 7.524 3.821 2014-03-25 16:30:34 19.81 -0.08857 14.46 -6.187 -22.65 -0.01730 0.006439 0.02931 2014-03-25 16:30:18 2014-03-25 16:30:25 -7.440 0.07817 100 2014-03-25 16:29:33 2014-03-25 16:30:32 0.06417 0.00001307 54 varying Tdew
2014-03-25 16:48:46 24.28 24.23 22.73 22.73 22.72 26.02 22.75 22.25 20.57 21.61 17.81 1.019 0.08945 6.557 3.387 2014-03-25 16:48:48 19.92 -0.08459 13.34 -5.753 -20.79 -0.01596 0.005988 0.02690 2014-03-25 16:48:26 2014-03-25 16:48:33 -6.759 0.2183 100 2014-03-25 16:47:46 2014-03-25 16:48:45 0.06423 0.00002734 55 varying Tdew
2014-03-25 17:08:19 24.24 24.21 22.75 22.67 22.68 26.02 22.74 22.34 20.86 21.74 18.41 1.026 5.120 5.555 2.939 2014-03-25 17:08:22 20.11 -0.07734 11.71 -5.082 -18.50 -0.01401 0.005290 0.02393 2014-03-25 17:08:08 2014-03-25 17:08:15 -5.926 0.1492 100 2014-03-25 17:07:21 2014-03-25 17:08:20 0.06411 0.00001511 55 varying Tdew
2014-03-25 17:33:07 24.16 24.14 22.79 22.74 22.69 26.06 22.80 22.46 21.25 21.97 19.27 1.016 10.21 4.554 2.509 2014-03-25 17:33:08 20.48 -0.06401 9.708 -4.205 -15.42 -0.01162 0.004379 0.01994 2014-03-25 17:32:52 2014-03-25 17:32:59 -5.070 0.1435 100 2014-03-25 17:32:06 2014-03-25 17:33:05 0.06434 0.00002586 55 varying Tdew
2014-03-25 18:20:04 23.97 23.93 22.78 22.77 22.75 26.06 22.79 22.53 21.71 22.21 20.36 1.040 15.16 3.047 1.719 2014-03-25 18:20:06 20.90 -0.04869 6.990 -2.973 -10.52 -0.008364 0.003099 0.01360 2014-03-25 18:19:53 2014-03-25 18:20:00 -3.535 0.2791 100 2014-03-25 18:19:05 2014-03-25 18:20:04 0.06408 0.00002733 55 varying Tdew
R_s = (0.0, 0.0) joule/(R_s*meter^2*second) P_wa = (218.598991722, 1694.78883295) pascal/P_wa T_a = (295.72, 295.95) kelvin/T_a v_w = (0.9999, 1.04) meter/(second*v_w) g_sw = (0.035, 0.035) meter/(g_sw*second)
Image in a Jupyter notebookImage in a Jupyter notebook
lc_time T_in1 T_in2 T_wall_in T_wall_out T_outside T_outside_PT T_chamber T_chamber1 T_leaf1 T_leaf2 T_leaf_in Wind T_dew Air_inflow Air_outflow nr_time T_leaf_IR V_leaf_IR Rn_above_leaf Rn_below_leaf Rn_beside_leaf RnV_above_leaf RnV_below_leaf RnV_beside_leaf sens_timefirst sens_timelast water_flow_avg water_flow_std water_flow_n pow_timefirst pow_timelast fan_power_avg fan_power_std fan_power_n Comment...................................................
TS K K K K K Deg C K K K K K m/s degC SLPM SLPM TS Deg C mV W/m^2 W/m^2 W/m^2 mV mV mV ul/min ul/min ul/min TS TS W W
2014-03-27 11:22:28 15.30 15.41 21.98 21.97 21.88 25.24 22.00 21.67 20.29 21.07 18.41 0.8333 10.21 6.827 3.491 2014-03-27 11:22:29 20.30 -0.07058 9.835 -3.912 -13.66 -0.01177 0.004074 0.01766 2014-03-27 11:22:19 2014-03-27 11:22:26 -4.825 0.1412 100 2014-03-27 11:21:28 2014-03-27 11:22:27 1.195 0.0003885 54 Black leaf, silver perforated alu (35.4-1), varying wind speed
2014-03-27 13:01:58 15.54 15.65 22.22 22.26 22.22 25.53 22.22 21.95 20.72 21.46 18.72 1.313 10.21 6.427 3.324 2014-03-27 13:02:01 20.41 -0.06664 10.02 -3.833 -16.85 -0.01199 0.003993 0.02180 2014-03-27 13:01:48 2014-03-27 13:01:55 -5.543 0.1600 100 2014-03-27 13:00:59 2014-03-27 13:01:58 1.181 0.0004875 54 varying wind speed
2014-03-27 14:04:25 15.68 15.80 22.41 22.40 22.37 25.69 22.43 22.17 21.05 21.76 18.95 1.796 10.21 6.251 3.274 2014-03-27 14:04:23 20.36 -0.06849 10.02 -3.781 -17.83 -0.01198 0.003939 0.02307 2014-03-27 14:04:10 2014-03-27 14:04:17 -6.124 0.1336 100 2014-03-27 14:03:24 2014-03-27 14:04:23 1.185 0.0003885 54 varying wind speed
2014-03-27 15:55:10 15.74 15.85 22.40 22.42 22.36 25.61 22.42 22.18 21.22 21.85 19.06 2.299 10.21 6.140 3.202 2014-03-27 15:55:11 19.58 -0.09654 9.917 -3.843 -17.14 -0.01187 0.004003 0.02218 2014-03-27 15:55:01 2014-03-27 15:55:08 -6.583 0.1710 100 2014-03-27 15:54:09 2014-03-27 15:55:08 1.169 0.0004755 55 varying wind speed
2014-03-27 17:01:01 15.90 16.02 22.43 22.48 22.40 25.81 22.45 22.22 21.42 21.96 19.24 2.995 10.21 5.851 3.136 2014-03-27 17:01:03 20.03 -0.08036 8.408 -3.625 -16.34 -0.01006 0.003776 0.02114 2014-03-27 17:00:38 2014-03-27 17:00:48 -7.255 0.1174 100 2014-03-27 17:00:04 2014-03-27 17:01:03 1.144 0.0000 54 varying wind speed
2014-03-27 19:00:22 15.88 15.99 22.40 22.38 22.21 25.57 22.46 22.24 21.53 22.03 19.38 3.831 10.20 5.750 3.057 2014-03-27 19:00:26 19.20 -0.1106 8.123 -3.345 -14.00 -0.009719 0.003485 0.01811 2014-03-27 19:00:07 2014-03-27 19:00:17 -8.006 0.1148 100 2014-03-27 18:59:24 2014-03-27 19:00:23 1.155 0.0004130 55 varying wind speed
2014-03-28 10:08:04 15.46 15.57 21.81 21.91 21.85 25.20 21.85 21.67 20.98 21.44 18.93 3.804 10.20 6.147 3.245 2014-03-28 10:08:10 19.58 -0.09655 8.239 -2.960 -12.37 -0.009857 0.003085 0.01600 2014-03-28 10:07:52 2014-03-28 10:08:02 -7.736 0.1666 100 2014-03-28 10:07:07 2014-03-28 10:08:06 1.159 0.0001872 55 New day
2014-03-28 11:29:49 15.71 15.83 22.23 22.21 22.15 25.57 22.24 22.08 21.38 21.84 19.29 4.335 10.20 5.858 3.204 2014-03-28 11:29:51 19.47 -0.1005 8.058 -3.227 -12.36 -0.009641 0.003363 0.01599 2014-03-28 11:29:27 2014-03-28 11:29:37 -7.984 0.08737 100 2014-03-28 11:28:49 2014-03-28 11:29:48 1.174 0.0004692 55 New day
2014-03-28 13:47:28 15.80 15.90 22.35 22.28 22.21 25.57 22.31 22.16 21.54 21.99 19.46 5.102 10.20 5.858 3.131 2014-03-28 13:47:29 19.50 -0.09967 6.985 -3.328 -12.71 -0.008357 0.003468 0.01643 2014-03-28 13:47:18 2014-03-28 13:47:27 -8.505 0.1932 100 2014-03-28 13:46:29 2014-03-28 13:47:28 1.186 0.0004914 54 Reduced head difference (transpiration seemed low)
2014-03-28 15:04:55 15.76 15.86 22.38 22.34 22.20 25.53 22.37 22.21 21.55 22.01 19.44 4.611 10.20 5.855 3.163 2014-03-28 15:04:56 19.39 -0.1037 7.228 -3.377 -12.89 -0.008648 0.003519 0.01667 2014-03-28 15:04:45 2014-03-28 15:04:55 -8.219 0.1147 100 2014-03-28 15:03:56 2014-03-28 15:04:55 1.182 0.0003857 55 Reduced head difference (transpiration seemed low)
2014-03-28 15:38:46 15.77 15.86 22.30 22.37 22.30 25.57 22.33 22.08 21.44 21.94 19.34 4.168 10.21 5.948 3.144 2014-03-28 15:38:49 19.42 -0.1023 7.755 -3.358 -12.98 -0.009278 0.003499 0.01679 2014-03-28 15:38:37 2014-03-28 15:38:47 -7.979 0.1037 100 2014-03-28 15:37:49 2014-03-28 15:38:47 1.168 0.001412 54 Varying Tdew
2014-03-31 12:42:52 16.15 16.28 23.06 23.06 22.93 26.30 23.10 22.89 22.13 22.64 19.90 3.325 10.20 5.259 2.863 2014-03-31 12:42:54 19.57 -0.09719 8.972 -3.413 -14.55 -0.01074 0.003556 0.01882 2014-03-31 13:42:34 2014-03-31 13:42:41 -7.484 0.09733 100 2014-03-31 13:41:52 2014-03-31 13:42:51 1.147 0.0001906 53 New daz
2014-03-31 14:49:10 16.20 16.33 23.25 23.34 23.21 26.63 23.28 23.04 22.17 22.75 19.91 2.637 10.18 5.346 2.915 2014-03-31 14:49:14 19.81 -0.08855 9.860 -3.661 -16.83 -0.01180 0.003814 0.02177 2014-03-31 15:48:56 2014-03-31 15:49:03 -7.032 0.1764 100 2014-03-31 15:48:12 2014-03-31 15:49:11 1.147 0.0000 53 Varying wind
2014-03-31 16:28:13 16.03 16.16 23.37 23.41 23.29 26.63 23.38 23.08 21.63 22.51 19.59 1.042 10.19 5.857 3.016 2014-03-31 16:28:15 19.31 -0.1065 10.95 -4.276 -14.55 -0.01310 0.004454 0.01881 2014-03-31 17:28:06 2014-03-31 17:28:13 -5.120 0.1585 100 2014-03-31 17:27:14 2014-03-31 17:28:13 1.147 0.0000 53 Varying wind
0.0420000000000000 R_s = (0.0, 0.0) joule/(R_s*meter^2*second) P_wa = (1187.38813505, 1278.34796382) pascal/P_wa T_a = (295.0, 296.53) kelvin/T_a v_w = (0.8333, 5.102) meter/(second*v_w) g_sw = (0.042, 0.042) meter/(g_sw*second)
Image in a Jupyter notebookImage in a Jupyter notebook
18 ['Date', 'Time', 'Inflow rate', 'Tdew humidifier', 'Incoming2 Temp_C(5)', 'Incoming3 Temp_C(6)', 'wall inside Temp_C(3) ', 'wall outside Temp_C(4)', 'chamber air Temp_C(1) ', 'Tl1', 'Tl2', 'TlIR', 'Tlin', 'Fan power', 'FlowMeter out', 'Wind speed', 'Sensirion', 'Comment'] ['', '', 'l/min', 'oC', 'oC', 'oC', 'oC', 'oC', 'oC', 'oC', 'oC', 'oC', 'oC', 'W', 'l/min', 'm/s', 'ul/min', ''] R_s = (0.0, 0.0) joule/(R_s*meter^2*second) P_wa = (168.502255462, 1143.55502295) pascal/P_wa T_a = (296.05, 296.71) kelvin/T_a v_w = (0.7, 0.7) meter/(second*v_w) g_sw = (0.0065, 0.0065) meter/(g_sw*second)
Image in a Jupyter notebookImage in a Jupyter notebookImage in a Jupyter notebook
R_s = (0.0, 700.0) joule/(R_s*meter^2*second) P_wa = (1300.96492908905, 1300.96492908905) pascal/P_wa T_a = (295, 295) kelvin/T_a v_w = (1.0, 1.0) meter/(second*v_w) g_sw = (0.045, 0.045) meter/(g_sw*second)
Image in a Jupyter notebook
R_s = (350.0, 350.0) joule/(R_s*meter^2*second) P_wa = (567.937364287074, 567.937364287074) pascal/P_wa T_a = (282.0, 298.0) kelvin/T_a v_w = (1.0, 1.0) meter/(second*v_w) g_sw = (0.045, 0.045) meter/(g_sw*second)
Image in a Jupyter notebook
imported file temp/leaf_chamber_data jupyter nbconvert --to=python 'Tables_of_variables.ipynb' Exporting specified worksheet to .py file...
nbconvert returned 0
Checking if specified ipynb file was run with sage kernel... Renaming .py file to .sage if notebook kernel was sage (to avoid exponent error) created file temp/Tables_of_variables Tables_of_variables.ipynb Worksheet_update.ipynb Worksheet_setup.ipynb stomatal_cond_eqs.ipynb leaf_chamber_eqs.ipynb leaf_chamber_data.ipynb leaf_enbalance_eqs.ipynb E_PM_eqs.ipynb imported file temp/Tables_of_variables