Some docs for estimation of parameters

06a99aad · Ian Bell · 92f39f6c · 06a99aad
Commit 06a99aad authored 2 years ago by Ian Bell
--- a/doc/source/models/multifluid.ipynb
+++ b/doc/source/models/multifluid.ipynb
@@ -103,7 +103,7 @@
   "source": [
    "# By default teqp looks for fluids relative to the set of fluids in ROOT/dev/fluids\n",
    "# The name (case-sensitive) should match the .json file, without the json extension.\n",
-    "%timeit model = teqp.build_multifluid_model([\"Methane\", \"Ethane\"], teqp.get_datapath())"
+    "%timeit model = teqp.build_multifluid_model([\"Methane\"], teqp.get_datapath())"
   ]
  },
  {
@@ -122,7 +122,7 @@
    "# And if you provide valid aliases, alias lookup will be used to resolve the name\n",
    "# But beware, this is rather a lot slower than the above because all fluid files need to be read\n",
    "# in to build the alias map\n",
-    "%timeit model = teqp.build_multifluid_model([\"n-C1H4\", \"n-C3H8\"], teqp.get_datapath())"
+    "%timeit model = teqp.build_multifluid_model([\"n-C1H4\"], teqp.get_datapath())"
   ]
  },
  {
@@ -149,7 +149,8 @@
    "# Building this map takes a little while (somewhat faster in C++) due to all the file reads\n",
    "# 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())"
+    "aliasmap = teqp.build_alias_map(teqp.get_datapath())\n",
+    "aliasmap"
   ]
  },
  {
@@ -168,7 +169,7 @@
    "# Then load the absolute paths from the alias map, \n",
    "# which will guarantee that you hit exactly what you were looking for,\n",
    "# resolving aliases as needed\n",
-    "identifiers = [aliasmap[n] for n in [\"Neon\", \"Hydrogen\"]]\n",
+    "identifiers = [aliasmap[n] for n in [\"n-C1H4\"]]\n",
    "%timeit model = teqp.build_multifluid_model(identifiers, teqp.get_datapath())"
   ]
  },
@@ -276,9 +277,42 @@
    "params, swap_needed = teqp.get_BIPdep(BIP, ['74-82-8','Ethane'])\n",
    "params"
   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Estimation of interaction parameters\n",
+    "\n",
+    "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``. \n",
+    "\n",
+    "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",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params, swap_needed = teqp.get_BIPdep(BIP, ['74-82-8','74-84-0'], flags={'force-estimate':'yes', 'estimate': 'Lorentz-Berthelot'})\n",
+    "params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# And without the force, the forcing is ignored\n",
+    "params, swap_needed = teqp.get_BIPdep(BIP, ['74-82-8','74-84-0'], flags={'estimate': 'Lorentz-Berthelot'})\n",
+    "params"
+   ]
  }
 ],
 "metadata": {
+  "celltoolbar": "Edit Metadata",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
@@ -294,7 +328,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.10.6"
  }
 },
 "nbformat": 4,

 %% 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", "Ethane"], teqp.get_datapath())
+%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", "n-C3H8"], teqp.get_datapath())
+%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
 ```
 %% 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 ["Neon", "Hydrogen"]]
+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
+```