update docs, changed apfu calculation

README.md

@@ -3,7 +3,7 @@
 Code authors: Weiran Li (University of Cambridge, U.K.) & Yishen Zhang (KU Leuven, Belgium) 
 
 ## Introduction  
-pyAp is a python package for calculating magmatic volatile, trace element concentrations, and oxygen fugacity using mineral apatite. 
+pyAp is a python package for calculating magmatic volatile, trace element concentrations, and oxygen fugacity using mineral apatite.  More modules will be added in the future.
 
 It includes multiple apatite-based models developed by W. Li and co-authors, including: 
 
@@ -12,15 +12,12 @@ It includes multiple apatite-based models developed by W. Li and co-authors, inc
   - Inputs:  Apatite F, Cl and H<sub>2</sub>O (if available) concentrations; Temperature and melt Cl and/or F concentrations (for calculating melt water) 
 
   - Outputs: Apatite stoichiometry (atom per fomula unit); Exchange coefficients for OH-Cl and/or OH-F; H<sub>2</sub>O concentrations in the melt
-
 * ApREE2O3: a lattice strain-thermodynamic model for calculating melt trace element (including REE) concentrations and oxygen fugacity (*fO<sub>2</sub>*) (following Li et al, in review)
 
   - Inputs:  Trace element concentrations in apatite and melt (be it melt inclusions/groundmass/whole rock)
 
   - Outputs: Partition coefficients of unmeasured trace elements; oxygen fugacity of the melt (if Eu was measured)
 
-* More modules will be added in the future (ApTimer - Li et al. 2020 EPSL; ApZone - Li et al. in prep). 
-
 
 ## Documentation
 Full documentation, further information about the package, and a tutorial for getting started are provided at [pyap.readthedocs.io](https://pyapati.readthedocs.io/en/latest/)
@@ -29,12 +26,12 @@ Full documentation, further information about the package, and a tutorial for ge
 pyAp can be installed by running ```python setup.py install``` in the package depository using command lines
 
 ## Citing pyAp
-If this package enables or aids your research please cite the relevant publications for:
+If you use this package please cite the relevant publications for:
 
 **ApThermo**:
 Li, W. & Costa, F. (2020) A thermodynamic model for F-Cl-OH partitioning between apatite and melt including non-ideal mixing and applications to constraining melt volatile budgets, Geochimica et Cosmochimica Acta 269, 203–222. https://doi.org/10.1016/j.gca.2019.10.035 
 
 **ApREE2O3**:
 Li, W., Costa, F, Oppenheimer, C. & Nagashima (in review) K. Measurements and a general model of volatile and trace element partitioning between apatite and alkaline melts. 
 
-You are encouraged to cite the specific version of the package you used. The first zenodo release (v1.0) is: DOI
+You are encouraged to cite the specific version of the package you used. The first zenodo release (v0.1) is: DOI

docs/build/doctrees/nbsphinx/tutorial_calcAST.ipynb

@@ -27,7 +27,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Inputs are SiO$_{2}$ concentration in the melt, and P$_{2}O_{5}$ concentration in the melt "
+    "Inputs are SiO$_{2}$ concentration in the melt, and P$_{2}$O$_{5}$ concentration in the melt"
    ]
   },
   {
@@ -47,7 +47,7 @@
      "output_type": "stream",
      "text": [
       ">> Warning: Concentrations are expressed as mass fractions. \n",
-      "Multiply them by 100 to convert into percentage!\n"
+      " Multiply them by 100 to convert into percentage!\n"
      ]
     },
     {

docs/build/html/_sources/index.rst.txt

@@ -20,7 +20,7 @@ Welcome to pyAp's documentation!
 
 Installation
 #########
-To install pyAp, please run ``python setup.py install`` in the package depository using command lines
+To install pyAp, please run ``python setup.py install`` in the package depository using command line commands
 
 
 

docs/build/html/_sources/tutorial_calcAST.ipynb.txt

@@ -27,7 +27,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Inputs are SiO$_{2}$ concentration in the melt, and P$_{2}O_{5}$ concentration in the melt "
+    "Inputs are SiO$_{2}$ concentration in the melt, and P$_{2}$O$_{5}$ concentration in the melt"
    ]
   },
   {
@@ -47,7 +47,7 @@
      "output_type": "stream",
      "text": [
       ">> Warning: Concentrations are expressed as mass fractions. \n",
-      "Multiply them by 100 to convert into percentage!\n"
+      " Multiply them by 100 to convert into percentage!\n"
      ]
     },
     {

docs/build/html/calc_water.html

@@ -628,30 +628,30 @@ <h2>Error integration from monte carlo simulation (MCS)<a class="headerlink" hre
   <tbody>
     <tr>
       <th>Ap1</th>
-      <td>4.323020</td>
-      <td>4.362345</td>
-      <td>0.764766</td>
-      <td>3.367485</td>
-      <td>3.624585</td>
-      <td>1.322785</td>
+      <td>4.334804</td>
+      <td>4.359289</td>
+      <td>0.751088</td>
+      <td>3.383908</td>
+      <td>3.608093</td>
+      <td>1.262393</td>
     </tr>
     <tr>
       <th>Ap2</th>
-      <td>1.472153</td>
-      <td>1.472895</td>
-      <td>0.236625</td>
-      <td>1.347868</td>
-      <td>1.414629</td>
-      <td>0.448814</td>
+      <td>1.459858</td>
+      <td>1.467147</td>
+      <td>0.234830</td>
+      <td>1.327742</td>
+      <td>1.403920</td>
+      <td>0.444818</td>
     </tr>
     <tr>
       <th>Ap3</th>
-      <td>0.672730</td>
-      <td>0.675322</td>
-      <td>0.168685</td>
-      <td>0.588964</td>
-      <td>0.621634</td>
-      <td>0.236727</td>
+      <td>0.674094</td>
+      <td>0.675498</td>
+      <td>0.170753</td>
+      <td>0.590710</td>
+      <td>0.622649</td>
+      <td>0.240046</td>
     </tr>
   </tbody>
 </table>

docs/build/html/index.html

@@ -116,7 +116,7 @@ <h1>Welcome to pyAp’s documentation!<a class="headerlink" href="#welcome-to-py
 </div>
 <div class="section" id="installation">
 <h2>Installation<a class="headerlink" href="#installation" title="Permalink to this headline"></a></h2>
-<p>To install pyAp, please run <code class="docutils literal notranslate"><span class="pre">python</span> <span class="pre">setup.py</span> <span class="pre">install</span></code> in the package depository using command lines</p>
+<p>To install pyAp, please run <code class="docutils literal notranslate"><span class="pre">python</span> <span class="pre">setup.py</span> <span class="pre">install</span></code> in the package depository using command line commands</p>
 </div>
 </div>
 <div class="section" id="indices-and-tables">

docs/build/html/plot_ternary_test_stoichiometry.html

@@ -663,9 +663,9 @@ <h2>Import data<a class="headerlink" href="#Import-data" title="Permalink to thi
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-0    25.897950
-1    25.748069
-2    25.887356
+0    25.896121
+1    25.744319
+2    25.885361
 Name: OXYGEN NUMBER, dtype: float64
 </pre></div></div>
 </div>
@@ -699,7 +699,12 @@ <h2>Ternary plot<a class="headerlink" href="#Ternary-plot" title="Permalink to t
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ApTernary</span><span class="o">.</span><span class="n">ternary</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># set up a figure for ternary plot</span>
+<span class="n">fig1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">fig1</span><span class="o">.</span><span class="n">set_size_inches</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="mi">6</span><span class="p">)</span>
+
+<span class="c1"># plot ternary (w/o data)</span>
+<span class="n">ApTernary</span><span class="o">.</span><span class="n">ternary</span><span class="p">(</span><span class="n">fig1</span><span class="p">)</span>
 
 <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">results_stoi</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
 
@@ -740,9 +745,11 @@ <h2>Ternary plot<a class="headerlink" href="#Ternary-plot" title="Permalink to t
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ApTernary</span><span class="o">.</span><span class="n">ternary</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig2</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">fig2</span><span class="o">.</span><span class="n">set_size_inches</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="mi">6</span><span class="p">)</span>
 
+<span class="c1"># plot ternary (w/o data)</span>
+<span class="n">ApTernary</span><span class="o">.</span><span class="n">ternary</span><span class="p">(</span><span class="n">fig2</span><span class="p">)</span>
 <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
 
     <span class="n">x_f</span> <span class="o">=</span> <span class="n">value</span><span class="p">[</span><span class="s1">&#39;XF&#39;</span><span class="p">]</span>

docs/build/html/tutorial_calcAST.html

@@ -358,7 +358,7 @@ <h1>3. Examples for apatite saturation temperature (AST) calculation<a class="he
 </pre></div>
 </div>
 </div>
-<p>Inputs are SiO<img class="math" src="_images/math/615d2d3e113dbd64bb9e2954633f05a2b51ef975.png" alt="_{2}"/> concentration in the melt, and P<img class="math" src="_images/math/66ddc82fce5dcbebbd3f642a2c4dd97c9fd3ee6f.png" alt="_{2}O_{5}"/> concentration in the melt</p>
+<p>Inputs are SiO<img class="math" src="_images/math/615d2d3e113dbd64bb9e2954633f05a2b51ef975.png" alt="_{2}"/> concentration in the melt, and P<img class="math" src="_images/math/615d2d3e113dbd64bb9e2954633f05a2b51ef975.png" alt="_{2}"/>O<img class="math" src="_images/math/22caea56a8025a12fe8729248f0d99735915557c.png" alt="_{5}"/> concentration in the melt</p>
 <p>When wrong input values are used. For example, one uses concetration (conc) in the incorrect mass fraction (0&lt;conc&lt;1) rather than the correct weight percent (1&lt;conc&lt;100):</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
@@ -374,7 +374,7 @@ <h1>3. Examples for apatite saturation temperature (AST) calculation<a class="he
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 &gt;&gt; Warning: Concentrations are expressed as mass fractions.
-Multiply them by 100 to convert into percentage!
+ Multiply them by 100 to convert into percentage!
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">

docs/build/html/tutorial_calcAST.ipynb

@@ -27,7 +27,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Inputs are SiO$_{2}$ concentration in the melt, and P$_{2}O_{5}$ concentration in the melt "
+    "Inputs are SiO$_{2}$ concentration in the melt, and P$_{2}$O$_{5}$ concentration in the melt"
    ]
   },
   {
@@ -47,7 +47,7 @@
      "output_type": "stream",
      "text": [
       ">> Warning: Concentrations are expressed as mass fractions. \n",
-      "Multiply them by 100 to convert into percentage!\n"
+      " Multiply them by 100 to convert into percentage!\n"
      ]
     },
     {

docs/source/index.rst

@@ -20,7 +20,7 @@ Welcome to pyAp's documentation!
 
 Installation
 #########
-To install pyAp, please run ``python setup.py install`` in the package depository using command lines
+To install pyAp, please run ``python setup.py install`` in the package depository using command line commands
 
 
 

docs/source/tutorial_calcAST.ipynb

@@ -27,7 +27,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Inputs are SiO$_{2}$ concentration in the melt, and P$_{2}O_{5}$ concentration in the melt "
+    "Inputs are SiO$_{2}$ concentration in the melt, and P$_{2}$O$_{5}$ concentration in the melt"
    ]
   },
   {
@@ -47,7 +47,7 @@
      "output_type": "stream",
      "text": [
       ">> Warning: Concentrations are expressed as mass fractions. \n",
-      "Multiply them by 100 to convert into percentage!\n"
+      " Multiply them by 100 to convert into percentage!\n"
      ]
     },
     {

example/calc_apfu_Ketcham.py

@@ -10,8 +10,6 @@
 
 ## calculate OH mole fraction using the method of Ketcham 2015, Am.Min.
 from pyAp.ApStoic_Ketcham import stoi_ketcham
-
-
 # - import module finish - #
 ############################################################################
 
@@ -24,6 +22,6 @@
 
 # print results 
 print(results)
-
+print(results['XOH'])
 # save results to csv file
 results.to_csv('outputs_apfu_ketcham.csv')

example/calc_apfu_error_mc.py

@@ -9,45 +9,32 @@
 from pyAp.ApStoic import stoi_
 #####################################################
 
-## load data
 # read concentration data
 data = pd.read_excel('data_ap_major_volatile.xlsx')
 
 # read errors (could be in the same file as the concentrations)
 errors = pd.read_excel('data_ap_errors.xlsx')
 
-# col = data.columns[1:-3]
 oxides = {'SIO2','TIO2','AL2O3','FEO','CAO','MGO','MNO','K2O','NA2O','P2O5','SO3','CO2', 'F','CL','H2O','CE2O3','SRO'}
 col = oxides.intersection(list(data))
 
-# calculate apfu using a fixed oxygen number 
-# results = stoi_(data,assume_oxy = 26)
-
-
 ## mc
 # set up mc for error calculation
 
-mc = 1000   # default = 1000; better to be >= default
+mc = 1000 # default = 1000; better to be >= default
 print('>> MC sampling starts ...')
 
 ap_mc_stoic = pd.DataFrame([]); ap_mc_res = pd.DataFrame([])
 for i in range(len(data)):
     df_iter = ap_mc(data[col], errors[col], i, mc)  # sample values within uncertainty ranges for "mc" times
     ap_mc_stoic = ap_mc_stoic.append(df_iter)
 
-
-# ap_mc_stoic.columns = col
-# ap_mc_stoic['H2O'] = np.nan
-# ap_mc_stoic['sample'] = data.loc[data.index.repeat(mc)]['sample']
-# ap_mc_stoic['TOTAL'] = data.loc[data.index.repeat(mc)]['TOTAL']
-# # ap_mc_stoic['OXYGEN NUMBER'] = data.loc[data.index.repeat(mc)]['OXYGEN NUMBER']
-# ap_mc_stoic = ap_mc_stoic[data.columns]
-# ap_mc_stoic.reset_index(inplace=True, drop=True)
-# # ap_mc_stoic['TOTAL'] = ap_mc_stoic.iloc[:, 1:-2].sum(axis=1)
-
-
+ap_mc_stoic.columns = col
+ap_mc_stoic['sample'] = data.loc[data.index.repeat(mc)]['sample']
+ap_mc_stoic.reset_index(inplace=True, drop=True)
 ## calculate stoichiometry for all mc samples 
 ap_mc_res = stoi_(ap_mc_stoic)
+
 print('\n>> Simulation completed') 
 ap_mc_res.groupby('sample').agg(
-    ['mean', 'median', 'std']).to_excel('mc_stoic.xlsx')
+    ['mean', 'median', 'std']).to_csv('mc_stoic.csv')

example/mc_stoic.csv

@@ -0,0 +1,6 @@
+,SI,SI,SI,TI,TI,TI,AL,AL,AL,FE,FE,FE,CA,CA,CA,MG,MG,MG,MN,MN,MN,K,K,K,NA,NA,NA,P,P,P,S,S,S,C,C,C,XF,XF,XF,XCL,XCL,XCL,XOH,XOH,XOH,CE,CE,CE,SR,SR,SR,OXYGEN NUMBER,OXYGEN NUMBER,OXYGEN NUMBER,"stoi,(Ca/P-5/3)/(5/3)*100%","stoi,(Ca/P-5/3)/(5/3)*100%","stoi,(Ca/P-5/3)/(5/3)*100%"
+,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std,mean,median,std
+sample,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+Ap1,0.017344479135102085,0.017342228300728013,0.0017908722157866377,0.013046905051169814,0.013045211923221972,0.0013471341270121595,0.020440492476205045,0.02043783986480461,0.0021105453653287795,0.014505238118554191,0.014503355739250264,0.0014977116192157734,10.032674943707208,10.032690217217464,0.012152340247874798,0.025857476586524396,0.025854121000192035,0.0026698660725672733,0.014689262847997367,0.014687356587365929,0.0015167127533755448,0.02212433771628308,0.022121466588274713,0.0022844077011562867,0.033625566519423465,0.033621202849555956,0.003471945876878386,5.872407866536885,5.872416305824266,0.006714703430250736,0.026030076322771146,0.02602841019595041,0.0013256507300568013,0.0023707308082076954,0.0023676204663870223,0.002474737729447024,0.8226521955310178,0.8226500408392783,0.001714376505873365,0.0734733692817735,0.07347156590736953,0.0014348515163738818,0.10387443518720872,0.10387839325335213,0.0031492280222472457,0.012697830309536058,0.012697017549889045,0.0006466707131875912,0.010056517143765037,0.010055212085579414,0.0010383671368892223,25.89612556481279,25.896121606746647,0.0031492280222475875,3.6567764378830026,3.6566060950920587,0.09732761214092958
+Ap2,0.01731154801715278,0.01727222631816335,0.0017740760061681247,0.013022133527864512,0.012992554866598898,0.0013344996425514084,0.02040168310848448,0.02035534243223331,0.0020907510092306987,0.014477697729266964,0.01444481287855309,0.0014836649004813622,10.032898404737278,10.033165230399673,0.012038366033322088,0.02580838225485208,0.025749760724447995,0.0026448259665156565,0.014661373059917378,0.014628071006417453,0.001502487827045124,0.02208233131359955,0.022032173188858896,0.002262982727188556,0.0335617232936605,0.033485490712980126,0.003439383234933236,5.872531338601898,5.872678771536956,0.006651727654901543,0.026005699842882597,0.025976592886778803,0.001313217733806877,0.002325224557486049,0.002270887407566982,0.002451527690624473,0.6855090639563624,0.6854671918328618,0.001889143438323114,0.05875233941447077,0.05872038142267262,0.0014418478324059161,0.25573859662916687,0.25581242674446564,0.003330991270729024,0.012685939126378143,0.01267174035166653,0.000640605726106377,0.01003742332436343,0.010014624176754043,0.0010286285123584352,25.744261403370835,25.744187573255537,0.00333099127072927,3.6549859057120706,3.652801890539381,0.09641865734051885
+Ap3,0.01740396944022352,0.017374471240946866,0.0016977139028648603,0.01309165498896493,0.013069465783849848,0.001277058360887953,0.020510601882476054,0.020475838214124905,0.0020007581656359503,0.014554990032970894,0.014530320603509013,0.001419803052395891,10.032271259724112,10.032471425962187,0.01152019491357101,0.02594616585530097,0.025902189383525745,0.002530983902831781,0.014739645953885383,0.014714663548859424,0.0014378157778985174,0.022200222589567494,0.022162595162549627,0.002165576460384731,0.033740899773108395,0.03368371191210351,0.003291340796518901,5.872184813349537,5.872295414015582,0.006365415280142676,0.026074112611748298,0.02605227726857766,0.0012566924959365791,0.0024529381825376772,0.002412175750658292,0.0023460058245311334,0.7678684788036372,0.7678389712928878,0.0016982498073294472,0.11760177257304329,0.11757915880066239,0.001301493538883217,0.11452974862331951,0.11458186990644981,0.002999743346212655,0.012719311818770222,0.012708660237157736,0.000613031935319782,0.010091010267985224,0.010073906892068014,0.0009843529365385142,25.885470251376677,25.885418130093548,0.0029997433462129403,3.6600046492751375,3.6583583626282823,0.09226848394950578

example/outputs_melt_water.csv

@@ -1,4 +1,4 @@
 ,MeltWater_calcfromF,MeltWater_calcfromCl,sample,MeltWater_Fmedian,MeltWater_F1sd,"MeltWater_F_error,100%",MeltWater_Clmedian,MeltWater_Cl1sd,"MeltWater_Cl_error,100%"
-0,4.327076530132149,3.3731299430193364,Ap1,4.346498606843621,0.8113013717398452,18.665630548286387,3.40028429799228,1.203161374982391,35.38414054650682
-1,1.4654389777629047,1.3368403714574024,Ap2,1.4662965877232188,0.23607456845026908,16.1000557749938,1.3382439476316728,0.3999399644719813,29.885430468769624
-2,0.673664893178862,0.5901605443126956,Ap3,0.6768668334961083,0.17735348385385313,26.202123531123323,0.5942722324708125,0.23441090566865136,39.44503762089614
+0,4.327076530132149,3.3731299430193364,Ap1,4.290550924599762,0.7790266419724337,18.1567974757252,3.3226235049528103,1.1117309031133422,33.45943052097717
+1,1.4654389777629047,1.3368403714574024,Ap2,1.4685470015865802,0.23677763693767395,16.12325902282089,1.3419341085288565,0.40159067944458604,29.926259187557598
+2,0.673664893178862,0.5901605443126956,Ap3,0.6863782598849355,0.18084108039395358,26.347145730441664,0.6065997042840845,0.24243821392834006,39.96675438779982