{ "cells": [ { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "slide" }, "tags": [] }, "source": [ "**Single-Stage Solvex Development** Cortix Tech 30Sep2025\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "slide" }, "tags": [] }, "source": [ "# Use-Case 04.1: TBP-Diluent-H$_2$O-HNO$_3$-UO$_2^{2+}$-Air Mixing Parameter Study" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "\"Cortix\n", "\n", "**Developer**: Valmor F. de Almeida, PhD
\n", "\n", "[Cortix Tech, Lowell, MA 01854, USA](https://cortix.tech)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Revision date: **02Dec25**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ":::{important}Demo:\n", "Early stage of parametric study simulation using Cortix. The goal here is to provide a mininum framework to help the testing of fixed-parameter simulations.\n", ":::" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "## Objectives" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "- Develop a usecase scenario for water, nitric acid, and uranyl extraction by TBP with a vapor phase parametric study.\n", "- Test implementation and present results.\n", "- Basic tentative flow scheme (pseudo-code):\n", " ```python\n", " import run_stg_script\n", " archive = list()\n", " for params in param_list:\n", " stg = stg_run_script(params)\n", " archive.append(stg)\n", " for stg in archive:\n", " stg.plot_efficiency()\n", " ```" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "'''AI assistance options'''\n", "# Set all to False if you do not have access to OpenAI API and/or AI codes below\n", "cortix_ai = True # Some basic cell AI explanation help" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "'''Generate proprietary knowledge database?'''\n", "db_save = False # set this to false if going public (online) with this notebook" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "'''Other helpers'''\n", "fig_count = 0\n", "tbl_count = 0\n", "markdown_display = True # if False code cell output is type: stream, else: markdown. Use True for in-house conversion to .md" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "## Mass Transfer and Inflow Relative Humidity Variation\n", "\n", "The entire system is prepared as a batch job." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cortix AI assistant: working on explanation...\n", "\n" ] }, { "data": { "text/markdown": [ "

Overview

\n", "\n", "This snippet does three things: declares a short module docstring, imports a callable named main from another module and aliases it as run, and — conditionally, when a variable named cortix_ai is truthy — constructs a CortixAI object and calls one of its methods with two keyword arguments.\n", "\n", "

Line-by-line explanation

\n", "\n", "- The first line is a module docstring: 'Read batch run file' (a short description attached to the module).\n", "- from run_tbp_h2o_hno3_uo22plus_air import main as run\n", " - Imports the object named main from the module run_tbp_h2o_hno3_uo22plus_air and binds it locally to the name run.\n", " - After this import, calling run(...) would invoke that main function (or callable).\n", "- if cortix_ai:\n", " - Tests the truthiness of the name cortix_ai. If it evaluates to True, the indented block runs.\n", " - This presumes cortix_ai is already defined in the surrounding scope before this snippet executes.\n", "- from cortix import CortixAI\n", " - Imports the CortixAI class (or callable) from the cortix package inside the conditional branch.\n", "- cortix_ai = CortixAI(llm_model='gpt-5-mini', llm_cleverness=0.8)\n", " - Instantiates CortixAI with two keyword arguments: llm_model set to 'gpt-5-mini' and llm_cleverness set to 0.8.\n", " - The resulting instance is assigned back to the name cortix_ai, replacing the previous value of cortix_ai in the current scope.\n", "- cortix_ai.markdown_header_level = 'h3'\n", " - Sets an attribute named markdown_header_level on the CortixAI instance to the string 'h3'.\n", "- if cortix_ai:\n", " - A second truthiness check of cortix_ai; if True, the next line runs.\n", " - Note this will be True if cortix_ai was truthy before and/or if the prior conditional created a CortixAI instance and assigned it to cortix_ai.\n", "- cortix_ai.explain(markdown_display=markdown_display, save_supporting_info=db_save)\n", " - Calls the explain method on the cortix_ai instance, passing two keyword arguments: markdown_display (value taken from a name in scope) and save_supporting_info set to the value of db_save in scope.\n", " - The snippet does not define markdown_display or db_save, so those names must exist in the surrounding context.\n", "\n", "

Runtime assumptions and observable side effects

\n", "\n", "- The snippet assumes the following names exist in scope before it runs:\n", " - cortix_ai (used as a condition and then reassigned if True)\n", " - markdown_display and db_save (passed as keyword arguments to the method call)\n", "- The import of run (main) makes a callable available locally as run; no call to run appears in this snippet.\n", "- The conditional import and instantiation of CortixAI occur only if cortix_ai is truthy at runtime.\n", "- The assignment cortix_ai = CortixAI(...) replaces the previous value of cortix_ai in the current namespace with the new instance.\n", "- The cortix_ai.explain(...) invocation is executed only if cortix_ai evaluates as truthy at that second check; it passes values from the current scope into the method." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "AI Parameters:\n", "+ LLM model (OpenAI) = gpt-5-mini\n", "+ LLM cleverness = 1.0\n", "+ Total # of tokens = 3449\n" ] } ], "source": [ "'''Read batch run file'''\n", "from run_tbp_h2o_hno3_uo22plus_air import main as run\n", "\n", "if cortix_ai:\n", " from cortix import CortixAI\n", " cortix_ai = CortixAI(llm_model='gpt-5-mini', llm_cleverness=0.8)\n", " cortix_ai.markdown_header_level = 'h3'\n", "\n", "if cortix_ai:\n", " cortix_ai.explain(markdown_display=markdown_display, save_supporting_info=db_save)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "'''Prepare params dictionary'''\n", "params = dict()\n", "params['make-plots'] = False\n", "params['verbose'] = False\n", "params['loglevel'] = 'error'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ":::{note}Note:\n", "Here the base relaxation time of all reactions is progressively increased and all stage data saved for future efficiency analysis.\n", ":::" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tau-factor 0.1\n", "inflow-relative-humidity 2.5\n", "Total mass rate density (mixture volume) residual [g/L-s]= -1.66533e-16\n", "total mass inflow rate [g/min] = 7.213e+02\n", "total mass outflow rate [g/min] = 7.171e+02\n", "\t net total mass flow rate [g/min] = -4.149e+00\n", "tau-factor 0.5\n", "inflow-relative-humidity 12.5\n", "Total mass rate density (mixture volume) residual [g/L-s]= -1.66533e-16\n", "total mass inflow rate [g/min] = 7.312e+02\n", "total mass outflow rate [g/min] = 7.349e+02\n", "\t net total mass flow rate [g/min] = 3.696e+00\n", "tau-factor 1.0\n", "inflow-relative-humidity 25.0\n", "Total mass rate density (mixture volume) residual [g/L-s]= -3.33067e-16\n", "total mass inflow rate [g/min] = 7.450e+02\n", "total mass outflow rate [g/min] = 7.484e+02\n", "\t net total mass flow rate [g/min] = 3.353e+00\n", "tau-factor 1.5\n", "inflow-relative-humidity 37.5\n", "Total mass rate density (mixture volume) residual [g/L-s]= 0.00000e+00\n", "total mass inflow rate [g/min] = 7.595e+02\n", "total mass outflow rate [g/min] = 7.438e+02\n", "\t net total mass flow rate [g/min] = -1.571e+01\n", "Cortix AI assistant: working on explanation...\n", "\n" ] }, { "data": { "text/markdown": [ "

Overview

\n", "\n", "This snippet runs a parameter sweep: it scales a set of nine \"reaction tau\" baseline values by several multipliers, scales an inflow relative-humidity baseline by corresponding multipliers, updates a params mapping accordingly, invokes run(params) for each parameter pair, and collects the returned stage objects in a list.\n", "\n", "

Data and variables

\n", "\n", "- The code defines:\n", " - rxn_tau_factors: tuple of 9 baseline floats (one per tau-factor index 0..8).\n", " - param_tau_factors: list of 4 scaling factors for the tau values.\n", " - inflow_relative_humidity: integer baseline 25.\n", " - param_inflow_rh_factors: list of 4 scaling factors for the inflow RH.\n", " - stages: initially empty list to collect results.\n", " - params: assumed to be an existing dictionary (mutated in-place).\n", " - cortix_ai, markdown_display, db_save: referenced at the end (used only if cortix_ai is truthy).\n", "\n", "

Loop mechanics and side effects

\n", "\n", "- The loop iterates over pairs from zip(param_tau_factors, param_inflow_rh_factors). Because both lists have length 4, the loop runs 4 iterations.\n", " \n", "- In each iteration:\n", " - It multiplies each of the nine rxn_tau_factors by the current param_tau_factor and assigns the results into params with keys 'tau-factor-0' through 'tau-factor-8'. These assignments overwrite any previous values in params for those keys.\n", " - It prints the current param_tau_factor via print('tau-factor ', param_tau_factor).\n", " - It computes params['inflow-relative-humidity'] = param_inflow_rh_factor * inflow_relative_humidity and prints that value via print('inflow-relative-humidity ', ...).\n", " - It calls stg = run(params) and appends stg to the stages list. run(params) is invoked with the current, mutated params dictionary.\n", " - After the iteration, params retains the last-assigned values until the next iteration modifies them.\n", "\n", "

Numeric examples (values produced)

\n", "\n", "- inflow-relative-humidity values computed each iteration:\n", " - 0.1 * 25 = 2.5\n", " - 0.5 * 25 = 12.5\n", " - 1.0 * 25 = 25.0\n", " - 1.5 * 25 = 37.5\n", "\n", "- tau-factor assignments by iteration (each entry is rxn_tau_factors[i] * param_tau_factor):\n", " - For param_tau_factor = 0.1:\n", " - 0.08, 0.11, 0.13, 0.10, 0.12, 0.07, 0.05, 0.09, 0.07\n", " - For param_tau_factor = 0.5:\n", " - 0.40, 0.55, 0.65, 0.50, 0.60, 0.35, 0.25, 0.45, 0.35\n", " - For param_tau_factor = 1.0:\n", " - 0.8, 1.1, 1.3, 1.0, 1.2, 0.7, 0.5, 0.9, 0.7 (same as the baseline tuple)\n", " - For param_tau_factor = 1.5:\n", " - 1.2, 1.65, 1.95, 1.5, 1.8, 1.05, 0.75, 1.35, 1.05\n", "\n", "

Outputs and final state

\n", "\n", "- stages becomes a list containing the stg object returned by run(params) from each of the 4 iterations, so len(stages) == 4 after the loop.\n", "- params ends up with the tau-factor and inflow-relative-humidity values from the final iteration (param_tau_factor = 1.5, param_inflow_rh_factor = 1.5), unless run(params) or other code mutates it further.\n", "- The print statements emit the current scaling factor and the computed inflow-relative-humidity each iteration.\n", "- If cortix_ai is truthy, the code calls cortix_ai.explain(markdown_display=markdown_display, save_supporting_info=db_save)." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "AI Parameters:\n", "+ LLM model (OpenAI) = gpt-5-mini\n", "+ LLM cleverness = 1.0\n", "+ Total # of tokens = 4262\n" ] } ], "source": [ "'''Vary parameter of study'''\n", "stages = list()\n", "\n", "rxn_tau_factors = (0.8, 1.1, 1.3, 1.0, 1.2, 0.7, 0.5, 0.9, 0.7)\n", "param_tau_factors = [0.1, 0.5, 1.0, 1.5]\n", "\n", "inflow_relative_humidity = 25\n", "param_inflow_rh_factors = [0.1, 0.5, 1.0, 1.5]\n", "\n", "for param_tau_factor, param_inflow_rh_factor in zip(param_tau_factors, param_inflow_rh_factors):\n", " \n", " params['tau-factor-0'] = rxn_tau_factors[0] * param_tau_factor\n", " params['tau-factor-1'] = rxn_tau_factors[1] * param_tau_factor\n", " params['tau-factor-2'] = rxn_tau_factors[2] * param_tau_factor\n", " params['tau-factor-3'] = rxn_tau_factors[3] * param_tau_factor\n", " params['tau-factor-4'] = rxn_tau_factors[4] * param_tau_factor\n", " params['tau-factor-5'] = rxn_tau_factors[5] * param_tau_factor\n", " params['tau-factor-6'] = rxn_tau_factors[6] * param_tau_factor\n", " params['tau-factor-7'] = rxn_tau_factors[7] * param_tau_factor\n", " params['tau-factor-8'] = rxn_tau_factors[8] * param_tau_factor\n", " print('tau-factor ', param_tau_factor)\n", "\n", " params['inflow-relative-humidity'] = param_inflow_rh_factor * inflow_relative_humidity\n", " print('inflow-relative-humidity ', params['inflow-relative-humidity'])\n", " \n", " stg = run(params)\n", "\n", " stages.append(stg)\n", "\n", "if cortix_ai:\n", " cortix_ai.explain(markdown_display=markdown_display, save_supporting_info=db_save)" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "## Overall Stage Efficiency\n", "\n", "Parameter variation impact on stage efficiency." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Cortix AI assistant: working on explanation...\n", "\n" ] }, { "data": { "text/markdown": [ "

Overview

\n", "\n", "- The script builds and plots per-stage efficiency histories for a sequence of stage objects, annotating each plotted quantity with parameter values and incrementing a figure counter before printing a summary line.\n", "\n", "

Imports and setup

\n", "\n", "- from cortix import Units as unit: imports unit definitions; unit.min is later used for x-axis scaling.\n", "- import matplotlib.pyplot as plt: standard plotting library (plt is not used directly in the shown snippet but is available).\n", "- efficiencies = list(): prepares an empty list to accumulate efficiency-history objects.\n", "\n", "

Collection of efficiency histories

\n", "\n", "- for stg in stages: iterates over an iterable named stages (each element expected to be a stage object).\n", " - stg.efficiency_history(mean=True) is called for each stage; the returned object is appended to efficiencies.\n", " - The appended objects (referred to later as eff) represent time-series quantities with metadata (name, value, unit, latex_name, and plotting methods).\n", "\n", "

Per-parameter plotting loop

\n", "\n", "- for eff, tau, rh in zip(efficiencies, param_tau_factors, param_inflow_rh_factors): iterates in parallel over the collected efficiency objects and two parameter lists (tau factors and relative-humidity factors).\n", " - quant = eff: assigns a local reference quant to the current efficiency object.\n", " - quant.name += f\"\"\"; @ parameter tau factor = {tau} and parameter relative humidity {rh}\"\"\": appends parameter information to the quantity's name string.\n", " - quant.value.name = quant.name: sets the nested value name to match the updated quantity name (so associated value metadata carries the same label).\n", " - quant.plot(...): calls the quantity's plot method with these arguments:\n", " - title: a raw string containing LaTeX fragments and then formatted with %2.1f for tau and rh (so the title shows numeric values with one decimal place). Example pattern: Stage Efficiency w/ $\\tau_{\\mathrm{f}}$ = 1.0; rh$_{\\mathrm{f}}$ = 0.5\n", " - x_scaling=1/unit.min: rescales the time axis from base units to minutes (factor 1 per minute).\n", " - x_label='Time [min]': label for the x axis.\n", " - y_label=quant.latex_name + ' [' + quant.unit + ']': constructs the y-axis label from the quantity's LaTeX name and its unit in square brackets.\n", " - show=True: display the plot immediately (handled by the quantity's plot method).\n", " - figsize=[10,3]: figure size in inches (wide and short).\n", " - error_data=True, error_fill=False: request plotting of error bars/data but disable a filled error band (specific rendering governed by the plot implementation).\n", "\n", "

Conditional auxiliary call and final output

\n", "\n", "- if cortix_ai: cortix_ai.explain(markdown_display=markdown_display, save_supporting_info=db_save)\n", " - The call to cortix_ai.explain is executed only if cortix_ai evaluates truthy; it is invoked with two keyword arguments passed through from local variables.\n", "- fig_count += 1: increments a running figure counter (assumes fig_count was defined earlier).\n", "- print(f'Figure {fig_count}: Stage efficiency variation with parameters.'): prints a one-line summary identifying the updated figure count and the plotted content.\n", "\n", "

Data and side-effect summary

\n", "\n", "- Side effects performed by the snippet:\n", " - Mutates quantity metadata (quant.name and quant.value.name) to include parameter annotations.\n", " - Produces one plot per zipped tuple (efficiency, tau, rh) via quant.plot.\n", " - Optionally calls cortix_ai.explain when cortix_ai is present.\n", " - Increments and prints fig_count.\n", "- Inputs expected (not defined in snippet):\n", " - stages: iterable of stage objects exposing efficiency_history(mean=True).\n", " - param_tau_factors and param_inflow_rh_factors: iterables of numeric parameter values of equal length to efficiencies.\n", " - cortix_ai, markdown_display, db_save, fig_count: external variables referenced by the snippet." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "AI Parameters:\n", "+ LLM model (OpenAI) = gpt-5-mini\n", "+ LLM cleverness = 1.0\n", "+ Total # of tokens = 3753\n", "Figure 1: Stage efficiency variation with parameters.\n" ] } ], "source": [ "'''Stage overall efficiency'''\n", "from cortix import Units as unit\n", "import matplotlib.pyplot as plt\n", "\n", "efficiencies = list()\n", "\n", "for stg in stages:\n", " efficiencies.append(stg.efficiency_history(mean=True))\n", "\n", "for eff, tau, rh in zip(efficiencies, param_tau_factors, param_inflow_rh_factors):\n", " quant = eff\n", " # Edit quant for AI use below\n", " quant.name += f\"\"\"; @ parameter tau factor = {tau} and parameter relative humidity {rh}\"\"\"\n", " quant.value.name = quant.name\n", " quant.plot(title=r'Stage Efficiency w/ $\\tau_{\\mathrm{f}}$ = %2.1f; rh$_{\\mathrm{f}}$ = %2.1f'%(tau,rh), x_scaling=1/unit.min, x_label='Time [min]', y_label=quant.latex_name+\n", " ' ['+quant.unit+']', show=True, figsize=[10,3], error_data=True, error_fill=False)\n", "if cortix_ai: \n", " cortix_ai.explain(markdown_display=markdown_display, save_supporting_info=db_save)\n", "\n", "fig_count += 1\n", "print(f'Figure {fig_count}: Stage efficiency variation with parameters.')" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "### Analysis\n", "\n", "LLM analysis." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cortix AI assistant: working on explanation...\n", "\n" ] }, { "data": { "text/markdown": [ "

Summary

\n", "\n", "- Four time-series (tau factor = 0.1, 0.5, 1.0, 1.5) share the same time grid (0 … 247.699 s) and identical initial values at t = 0 (column 0 = 15.0002, column 1 = 21.6026).\n", "- Column \"0\" increases with time and approaches a plateau; column \"1\" decreases with time and approaches a low steady value.\n", "- The rate and asymptotic values depend strongly on the tau factor: smaller tau → faster rise of column 0 and faster fall of column 1.\n", "\n", "

Time evolution — column 0 (first numeric column)

\n", "\n", "- At t = 0 all cases start at 15.0002.\n", "- Early-time growth (t ≈ 15–31 s) is much faster for small tau:\n", " - tau = 0.1: 81.53 (t = 15.48 s) → 87.78 (t = 30.96 s)\n", " - tau = 0.5: 44.03 → 57.24\n", " - tau = 1.0: 29.54 → 39.04\n", " - tau = 1.5: 23.79 → 30.37\n", "- Long-time plateau (t = 247.699 s) values:\n", " - tau = 0.1: 91.7701\n", " - tau = 0.5: 69.4544\n", " - tau = 1.0: 53.4794\n", " - tau = 1.5: 43.6286\n", "- Interpretation: increasing tau factor slows the approach to higher efficiency and lowers the asymptotic efficiency in column 0.\n", "\n", "

Time evolution — column 1 (second numeric column)

\n", "\n", "- All cases start at 21.6026 and monotonically decrease.\n", "- Early-time decrease (t ≈ 15–31 s):\n", " - tau = 0.1: 21.60 → 15.09 → 6.91 by 30.96 s\n", " - tau = 0.5: 21.60 → 19.21 → 15.29\n", " - tau = 1.0: 21.60 → 17.99 → 15.15\n", " - tau = 1.5: 21.60 → 17.37 → 14.53\n", "- Long-time plateau (t = 247.699 s) values:\n", " - tau = 0.1: 2.12651\n", " - tau = 0.5: 6.34104\n", " - tau = 1.0: 7.69490\n", " - tau = 1.5: 7.90588\n", "- Interpretation: smaller tau yields a faster and deeper reduction in column 1; larger tau produces a slower decline and a higher steady residual.\n", "\n", "

Direct comparison and key takeaways

\n", "\n", "- At intermediate and long times, the series are well separated by tau:\n", " - Column 0 ordering (largest to smallest) at t = 247.7 s: tau 0.1 > 0.5 > 1.0 > 1.5.\n", " - Column 1 ordering (smallest to largest) at t = 247.7 s: tau 0.1 < 0.5 < 1.0 < 1.5.\n", "- Magnitude of change relative to t = 0:\n", " - Column 0 net increase by t = 247.7 s:\n", " - +76.77 (tau 0.1), +54.45 (0.5), +38.48 (1.0), +28.63 (1.5).\n", " - Column 1 net decrease by t = 247.7 s:\n", " - −19.48 (tau 0.1), −15.26 (0.5), −13.91 (1.0), −13.70 (1.5).\n", "- Dynamics:\n", " - tau = 0.1 reaches near-steady values earlier (by ~30–120 s) compared with larger tau where approach to steady state is slower and plateaus at lower (column 0) / higher (column 1) magnitudes.\n", "- Overall conclusion: Tau factor is a controlling time-scale parameter here — smaller tau accelerates conversion of the quantity represented by column 1 into that represented by column 0 and produces a higher steady-state for column 0 and a lower residual for column 1." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "AI Parameters:\n", "+ LLM model (OpenAI) = gpt-5-mini\n", "+ LLM cleverness = 1.0\n", "+ Total # of tokens = 5879\n" ] } ], "source": [ "'''Analyze efficiencies'''\n", "if cortix_ai: \n", " cortix_ai.explain(quant=efficiencies, markdown_header_level='

', markdown_display=markdown_display, save_supporting_info=db_save)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Figure 2: Stage efficiency and deviation variation with parameters.\n" ] } ], "source": [ "'''Stage overall efficiency'''\n", "from cortix import Units as unit\n", "import matplotlib.pyplot as plt\n", "\n", "for eff, tau, rh in zip(efficiencies, param_tau_factors, param_inflow_rh_factors):\n", " quant = eff\n", " quant.plot(title=r'Stage Efficiency w/ $\\tau_\\mathrm{f}$ = %2.1f; rh$_{\\mathrm{f}}$ = %2.1f'%(tau,rh), x_scaling=1/unit.min, x_label='Time [min]', y_label=quant.latex_name+\n", " ' ['+quant.unit+']', legend=['E', 'std'], show=True, figsize=[10,3], error_data=False, error_fill=False)\n", "fig_count += 1\n", "print(f'Figure {fig_count}: Stage efficiency and deviation variation with parameters.')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Figure 3: Multiplot efficiency variation with parameters.\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "'''For debugging purposes'''\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "for eff in efficiencies:\n", " data = eff.value\n", " values = list()\n", " errors = list()\n", " for entry in data:\n", " values.append(entry[0])\n", " errors.append(entry[1])\n", " values = np.array(values)\n", " errors = np.array(errors)\n", " \n", " plt.plot(data.index/60, values)\n", " plt.fill_between(data.index/60, values - errors, values + errors, alpha=0.15)\n", " \n", "plt.grid()\n", "\n", "fig_count += 1\n", "print(f'Figure {fig_count}: Multiplot efficiency variation with parameters.')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.5" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": false, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false } }, "nbformat": 4, "nbformat_minor": 4 }