Fix writing of alias map

29a8e918 · Ian Bell · 9e81b24d · 29a8e918
Commit 29a8e918 authored 2 years ago by Ian Bell
--- a/doc/source/models/multifluid.ipynb
+++ b/doc/source/models/multifluid.ipynb
@@ -150,7 +150,7 @@
    "# If you know your files will not change, good idea to build this alias map yourself.\n",
    "%timeit aliasmap = teqp.build_alias_map(teqp.get_datapath())\n",
    "aliasmap = teqp.build_alias_map(teqp.get_datapath())\n",
-    "aliasmap.keys()[0:30] # the first 30 aliases in the dict"
+    "list(aliasmap.keys())[0:10] # the first 10 aliases in the dict"
   ]
  },
  {

 %% Cell type:markdown id: tags:

 # Multi-fluid EOS

 Peering into the innards of teqp

 %% Cell type:code id: tags:

 ``` python
 import timeit, json
 import pandas
 import numpy as np
 import teqp
 teqp.__version__
 ```

 %% Cell type:markdown id: tags:

 ## Ancillary Equations

 %% Cell type:markdown id: tags:

 Ancillary equations are provided along with multiparameter equations of state. The give a good *approximation* to the phase equilibrium densities.  There are routines in teqp to use the ancillary equations provided with the EOS. First a class containing the ancillary equations is obtained, then methods on that class are called

 %% Cell type:code id: tags:

 ``` python
 model = teqp.build_multifluid_model(["Methane"], teqp.get_datapath())
 anc = model.build_ancillaries()
 T = 100.0 # [K]
 rhoL, rhoV = anc.rhoL(T), anc.rhoV(T)
 print('Densities are:', rhoL, rhoV, 'mol/m^3')
 ```

 %% Cell type:markdown id: tags:

 But those densities do not correspond to the *true* phase equilibrium solution, so we need to polish the solution:

 %% Cell type:code id: tags:

 ``` python
 Niter = 10
 rhoLtrue, rhoVtrue = model.pure_VLE_T(T, rhoL, rhoV, Niter)
 print('VLE densities are:', rhoLtrue, rhoVtrue, 'mol/m^3')
 ```

 %% Cell type:markdown id: tags:

 And looking the densities, they are slightly different after the phase equilibrium calculation

 %% Cell type:markdown id: tags:

 ## Pure fluid loading

 %% Cell type:code id: tags:

 ``` python
 # By default teqp looks for fluids relative to the set of fluids in ROOT/dev/fluids
 # The name (case-sensitive) should match the .json file, without the json extension.
 %timeit model = teqp.build_multifluid_model(["Methane"], teqp.get_datapath())
 ```

 %% Cell type:code id: tags:

 ``` python
 # And if you provide valid aliases, alias lookup will be used to resolve the name
 # But beware, this is rather a lot slower than the above because all fluid files need to be read
 # in to build the alias map
 %timeit model = teqp.build_multifluid_model(["n-C1H4"], teqp.get_datapath())
 ```

 %% Cell type:markdown id: tags:

 So, how to make it faster? Only do it once and cache

 %% Cell type:code id: tags:

 ``` python
 # Here is the set of possible aliases to absolute paths of files
 # Building this map takes a little while (somewhat faster in C++) due to all the file reads
 # If you know your files will not change, good idea to build this alias map yourself.
 %timeit aliasmap = teqp.build_alias_map(teqp.get_datapath())
 aliasmap = teqp.build_alias_map(teqp.get_datapath())
-aliasmap.keys()[0:30] # the first 30 aliases in the dict
+list(aliasmap.keys())[0:10] # the first 10 aliases in the dict
 ```

 %% Cell type:code id: tags:

 ``` python
 # Then load the absolute paths from the alias map,
 # which will guarantee that you hit exactly what you were looking for,
 # resolving aliases as needed
 identifiers = [aliasmap[n] for n in ["n-C1H4"]]
 %timeit model = teqp.build_multifluid_model(identifiers, teqp.get_datapath())
 ```

 %% Cell type:markdown id: tags:

 At some point soon teqp will support in-memory loading of JSON data for the pure components, without requiring reads from the operating system

 %% Cell type:code id: tags:

 ``` python
 # And you can also load the JSON that teqp is loading for the pure fluids
 pureJSON = teqp.collect_component_json(['Neon','Hydrogen'], teqp.get_datapath())
 ```

 %% Cell type:markdown id: tags:

 ## Mixture model loading

 %% Cell type:code id: tags:

 ``` python
 # Load the default JSON for the binary interaction parameters
 BIP = json.load(open(teqp.get_datapath()+'/dev/mixtures/mixture_binary_pairs.json'))
 ```

 %% Cell type:code id: tags:

 ``` python
 # You can obtain interaction parameters either by pairs of names, where name is the name that teqp uses, the ["INFO"]["NAME"] field
 params, swap_needed = teqp.get_BIPdep(BIP, ['Methane','Ethane'])
 params
 ```

 %% Cell type:code id: tags:

 ``` python
 # Or also by CAS#
 params, swap_needed = teqp.get_BIPdep(BIP, ['74-82-8','74-84-0'])
 params
 ```

 %% Cell type:code id: tags:raises-exception

 ``` python
 # But mixing is not allowed
 params, swap_needed = teqp.get_BIPdep(BIP, ['74-82-8','Ethane'])
 params
 ```

 %% Cell type:markdown id: tags:

 ## Estimation of interaction parameters

 Estimation of interaction parameters can be used when no mixture model is present.  The ``flags`` keyword argument allows the user to control how estimation is applied. The ``flags`` keyword argument should be a dictionary, with keys of ``"estimate"`` to provide the desired estimation scheme as-needed. For now, the only allowed estimation scheme is ``Lorentz-Berthelot``.

 If it is desired to force the estimation, the ``"force-estimate"`` to force the use of the provided esimation scheme for all binaries, even when one is available. The value associated with ``"force-estimate"`` is ignored.

 %% Cell type:code id: tags:

 ``` python
 params, swap_needed = teqp.get_BIPdep(BIP, ['74-82-8','74-84-0'], flags={'force-estimate':'yes', 'estimate': 'Lorentz-Berthelot'})
 params
 ```

 %% Cell type:code id: tags:

 ``` python
 # And without the force, the forcing is ignored
 params, swap_needed = teqp.get_BIPdep(BIP, ['74-82-8','74-84-0'], flags={'estimate': 'Lorentz-Berthelot'})
 params
 ```