{ "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 02.1: TBP-Diluent-H$_2$O-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: **19Nov25**" ] }, { "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 extraction by TBP with a vapor phase parametric study.\n", "- Test implementation and present results." ] }, { "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" ] }, { "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 is a small Python module that imports a function, conditionally constructs a CortixAI object, configures it, and then calls a method on that object. It relies on several names (cortix_ai, markdown_display, db_save) that must exist in the surrounding runtime.\n", "\n", "

Line-by-line explanation

\n", "\n", "- The first line is a module-level docstring: 'Read batch run file'.\n", "- from run_tbp_h2o_air import main as run\n", " - Imports the name main from the module run_tbp_h2o_air and binds it locally as run.\n", "- if cortix_ai:\n", " - Tests the truthiness of the name cortix_ai at runtime. If that name is not defined, evaluating this condition raises a NameError; if defined and truthy, the block executes.\n", " - from cortix import CortixAI\n", " - Imports the CortixAI class (or factory) from the cortix package, at the moment the condition is true.\n", " - cortix_ai = CortixAI(llm_model='gpt-5-mini', llm_cleverness=0.8)\n", " - Creates an instance of CortixAI with the keyword arguments llm_model set to 'gpt-5-mini' and llm_cleverness set to 0.8, then assigns that instance to the name cortix_ai, overwriting the previous value of cortix_ai.\n", " - cortix_ai.markdown_header_level = 'h3'\n", " - Sets an attribute markdown_header_level on the newly created CortixAI instance to the string 'h3'.\n", "- if cortix_ai:\n", " - Re-evaluates the truthiness of cortix_ai; since cortix_ai was just assigned to an instance in the prior block (when that block ran), this will typically be true and the following call will execute.\n", " - cortix_ai.explain(markdown_display=markdown_display, save_supporting_info=db_save)\n", " - Invokes the explain method on the cortix_ai instance, passing two keyword arguments: markdown_display and save_supporting_info whose values are taken from the names markdown_display and db_save in the current namespace.\n", "\n", "

Runtime dependencies and behavior

\n", "\n", "- The snippet assumes these names exist in the runtime before execution:\n", " - cortix_ai (checked and later overwritten)\n", " - markdown_display (passed to explain)\n", " - db_save (passed as save_supporting_info)\n", "- The import of CortixAI happens only if the first if cortix_ai condition is true; that import is not executed at module import time unless the condition is met at runtime.\n", "- The code rebinds cortix_ai inside the first conditional, so the value checked by the second if cortix_ai may be the same object created in the first block.\n", "- The alias run refers to run_tbp_h2o_air.main and is available for use elsewhere in the module after this snippet.\n", "\n", "

CortixAI configuration present in the snippet

\n", "\n", "- llm_model='gpt-5-mini' — selects the language model identifier passed to CortixAI.\n", "- llm_cleverness=0.8 — sets a numeric parameter named llm_cleverness on instantiation.\n", "- markdown_header_level = 'h3' — sets the instance attribute that controls the header level for Markdown output.\n", "\n", "

Contextual notes

\n", "\n", "- This code pattern is typical where a runtime flag or pre-existing object determines whether to construct and use an AI helper object and to emit Markdown-formatted output in an interactive environment (for example, a Jupyter notebook)." ], "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 = 3509\n" ] } ], "source": [ "'''Read batch run file'''\n", "from run_tbp_h2o_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": {}, "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]= -5.71713e-17\n", "total mass inflow rate [g/min] = 7.471e+02\n", "total mass outflow rate [g/min] = 7.471e+02\n", "\t net total mass flow rate [g/min] = -3.770e-02\n", "tau-factor 0.5\n", "inflow-relative-humidity 12.5\n", "Total mass rate density (mixture volume) residual [g/L-s]= -1.08169e-16\n", "total mass inflow rate [g/min] = 7.471e+02\n", "total mass outflow rate [g/min] = 7.471e+02\n", "\t net total mass flow rate [g/min] = -3.769e-02\n", "tau-factor 1.0\n", "inflow-relative-humidity 25.0\n", "Total mass rate density (mixture volume) residual [g/L-s]= -3.44709e-17\n", "total mass inflow rate [g/min] = 7.471e+02\n", "total mass outflow rate [g/min] = 7.471e+02\n", "\t net total mass flow rate [g/min] = -3.767e-02\n", "tau-factor 1.5\n", "inflow-relative-humidity 37.5\n", "Total mass rate density (mixture volume) residual [g/L-s]= -3.07371e-17\n", "total mass inflow rate [g/min] = 7.471e+02\n", "total mass outflow rate [g/min] = 7.471e+02\n", "\t net total mass flow rate [g/min] = -3.649e-02\n", "Cortix AI assistant: working on explanation...\n", "\n" ] }, { "data": { "text/markdown": [ "

Overview

\n", "\n", "- This snippet runs a small parameter sweep: it varies two linked parameters (a set of \"tau\" multipliers and an inflow relative-humidity factor) across matching factor vectors, runs a simulation/function for each combination, and collects the returned stage results in a list named stages.\n", "\n", "

Key variables and their roles

\n", "\n", "- stages\n", " - An initially empty list that collects the return value from each call to run(params).\n", "- rxn_tau_multipliers\n", " - A tuple of base multipliers for reaction/flow residence time (comment: \"flow residence time multipliers\"). The code uses indices 0, 1 and 2 of this tuple.\n", "- param_tau_factors\n", " - A list of scalar factors by which the rxn_tau_multipliers are scaled for each sweep iteration.\n", "- inflow_relative_humidity\n", " - A base numeric value (25) representing the reference inflow relative humidity.\n", "- param_inflow_rh_factors\n", " - A list of scalar factors applied to inflow_relative_humidity for each sweep iteration.\n", "- params\n", " - An assumed pre-existing mapping (e.g., dict) that is being updated in-place inside the loop with keys 'tau-factor-0', 'tau-factor-1', 'tau-factor-2', and 'inflow-relative-humidity'.\n", "- run(params)\n", " - A call to an external function (assumed to perform the simulation or computation) whose return value is appended to stages.\n", "- cortix_ai (and its method call)\n", " - A variable checked after the sweep; if truthy, cortix_ai.explain(...) is invoked. (No explanation of that method's behavior is given here.)\n", "\n", "

Control flow and computations

\n", "\n", "- The assertion assert len(param_tau_factors) == len(param_inflow_rh_factors) enforces that the two factor lists have the same length; otherwise the script will raise an AssertionError and stop.\n", "- The for loop iterates in parallel over param_tau_factors and param_inflow_rh_factors using zip; with the provided lists the loop runs four iterations (one per factor pair).\n", "- Inside each iteration:\n", " - The code sets three params entries:\n", " - params['tau-factor-0'] = rxn_tau_multipliers[0] * param_tau_factor\n", " - params['tau-factor-1'] = rxn_tau_multipliers[1] * param_tau_factor\n", " - params['tau-factor-2'] = rxn_tau_multipliers[2] * param_tau_factor\n", " - A print statement outputs the current param_tau_factor.\n", " - The inflow-relative-humidity parameter is updated as params['inflow-relative-humidity'] = param_inflow_rh_factor * inflow_relative_humidity and printed.\n", " - run(params) is invoked; its return value is stored in stg and appended to stages.\n", "- After the loop, if cortix_ai is truthy, cortix_ai.explain(markdown_display=markdown_display, save_supporting_info=db_save) is called.\n", "\n", "

Concrete numeric example (first iteration)

\n", "\n", "- With the first pair param_tau_factor = 0.1 and param_inflow_rh_factor = 0.1:\n", " - params['tau-factor-0'] = 1.0 * 0.1 = 0.1\n", " - params['tau-factor-1'] = 1.2 * 0.1 = 0.12\n", " - params['tau-factor-2'] = 0.7 * 0.1 = 0.07\n", " - params['inflow-relative-humidity'] = 0.1 * 25 = 2.5\n", " - Then run(params) is executed and its result appended to stages.\n", "\n", "

Additional notes and observable behaviors

\n", "\n", "- Only the first three entries of rxn_tau_multipliers are used; the tuple contains six values but indices 3–5 are not referenced by this code.\n", "- The commented-out alternate lists are inactive and have no effect.\n", "- The printed lines provide runtime visibility of each iteration's param_tau_factor and the computed inflow-relative-humidity value.\n", "- stages ends up with one element per iteration (four elements for the provided factor lists), each element being whatever run(params) returned." ], "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 = 4061\n" ] } ], "source": [ "'''Vary Parameter of Study'''\n", "stages = list()\n", "\n", "rxn_tau_multipliers = (1.0, 1.2, 0.7, 0.5, 0.8, 0.9) # flow residence time multipliers\n", "param_tau_factors = [0.1, 0.5, 1.0, 1.5] # parameter variation\n", "#param_tau_factors = [1, 1, 1, 1]\n", "\n", "inflow_relative_humidity = 25\n", "param_inflow_rh_factors = [0.1, 0.5, 1.0, 1.5] # parameter variation\n", "#param_inflow_rh_factors = [0.2, 1, 1.7, 3.9]\n", "\n", "assert len(param_tau_factors) == len(param_inflow_rh_factors)\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_multipliers[0] * param_tau_factor\n", " params['tau-factor-1'] = rxn_tau_multipliers[1] * param_tau_factor\n", " params['tau-factor-2'] = rxn_tau_multipliers[2] * 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": {}, "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", "This snippet collects stage efficiencies, annotates each efficiency object with the corresponding parameter values, and produces a plot per paired parameter set. It also conditionally invokes a Cortix AI helper and updates a figure counter.\n", "\n", "- The script imports Units from cortix as `unit` and matplotlib.pyplot as `plt`, then declares an empty list `efficiencies`.\n", "\n", "- It builds `efficiencies` by iterating `for stg in stages:` and appending `stg.efficiency_history(mean=True)`. Each appended item is expected to be an object (hereafter \"quant\") representing a time series or quantity with attributes and a `plot` method.\n", "\n", "- The `for eff, tau, rh in zip(efficiencies, param_tau_factors, param_inflow_rh_factors):` loop iterates over tuples formed by zipping the three iterables; iteration stops when the shortest iterable is exhausted.\n", "\n", "- Inside the loop:\n", " - `quant = eff` binds the current efficiency object to the name `quant`.\n", " - `quant.name += f\"\"\"; @ parameter tau factor = {tau} and parameter relative humidity {rh}\"\"\"` appends a semicolon-delimited annotation containing the numeric `tau` and `rh` to the existing `quant.name` string.\n", " - `quant.value.name = quant.name` assigns the same annotated name to an inner attribute `quant.value.name`.\n", " - `quant.plot(...)` is called with these keyword arguments:\n", " - `title` built with a raw string that embeds `tau` and `rh` using the `%2.1f` format specifiers and includes LaTeX-style fragments inside the raw string.\n", " - `x_scaling=1/unit.min` — an x-axis scaling factor that converts the internal x units to minutes by multiplying x-data by this factor (since `unit.min` denotes one minute in the cortix Units system).\n", " - `x_label='Time [min]'`\n", " - `y_label=quant.latex_name + ' [' + quant.unit + ']'` — the y-axis label concatenates a LaTeX-ready name and the unit in brackets.\n", " - `show=True` — request to display the plot immediately.\n", " - `figsize=[10,3]` — figure dimensions in inches (width, height).\n", " - `error_data=True` and `error_fill=False` — plotting options controlling how uncertainty/error is rendered.\n", " - The call to `quant.plot` is a method on the `quant` object and is responsible for creating the matplotlib figure (and potentially other side effects like logging or storing the figure), based on the provided metadata and data.\n", "\n", "- After the plotting loop, the code conditionally executes `if cortix_ai: cortix_ai.explain(markdown_display=markdown_display, save_supporting_info=db_save)`. This calls the `explain` method on `cortix_ai` when `cortix_ai` is truthy, passing two named arguments; the contents or behavior of `explain` are not described here.\n", "\n", "- Finally, `fig_count += 1` increments a figure counter, and `print(f'Figure {fig_count}: Stage efficiency variation with parameters.')` emits a short status message referencing the new figure count.\n", "\n", "

Key variables and expected types

\n", "\n", "- `stages`: an iterable of stage objects; each stage must provide an `efficiency_history(mean=True)` method that returns a `quant`-like object.\n", "\n", "- `efficiencies`: Python list of the returned `quant` objects.\n", "\n", "- `param_tau_factors`, `param_inflow_rh_factors`: iterables (lists/tuples/arrays) of numeric parameter values; zipped with `efficiencies` for one-to-one plotting.\n", "\n", "- `quant` (each item in `efficiencies`):\n", " - attributes used: `name` (string), `value` (object with `.name`), `latex_name` (string), `unit` (string),\n", " - method used: `.plot(...)` taking plotting metadata and options.\n", "\n", "- `unit.min`: a numeric value from the cortix Units system representing one minute; used to compute `x_scaling = 1 / unit.min`.\n", "\n", "- `cortix_ai`, `markdown_display`, `db_save`: variables referenced for the conditional explain call; presence and truthiness control that invocation.\n", "\n", "- `fig_count`: an integer counter incremented after plotting.\n", "\n", "

Side effects and outputs

\n", "\n", "- The code mutates each `quant` by appending parameter information to `quant.name` and copying that to `quant.value.name`.\n", "\n", "- It produces one plot per zipped tuple; plots are created via `quant.plot` with `show=True`, so they are displayed immediately (typical effect in a Jupyter session is an inline figure).\n", "\n", "- The `zip(...)` means if the three sequences differ in length, only the length of the shortest sequence will be processed.\n", "\n", "- If `cortix_ai` is truthy, a method call with `markdown_display` and `save_supporting_info=db_save` is made on that object.\n", "\n", "- `fig_count` is incremented and a print statement logs the figure index and a short description." ], "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 = 4117\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": {}, "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": [ "

Overview

\n", "\n", "- Data: time histories (0 → 246.765 s) of Stage Efficiency reported as \"mean\" (column 0) and \"± std\" (column 1) for four parameter sets: (tau factor, relative humidity) = (0.1,0.1), (0.5,0.5), (1.0,1.0), (1.5,1.5).\n", "- All cases share the same t=0 values: mean = 22.5% and std = 23.0489%.\n", "\n", "

Steady-state summaries (final sample at 246.765 s)

\n", "\n", "- (tau=0.1, RH=0.1): mean ≈ 75.337% ; std ≈ 16.458% ; absolute increase from t=0 = +52.837 pp (+235% relative).\n", "- (tau=0.5, RH=0.5): mean ≈ 63.790% ; std ≈ 6.876% ; absolute increase = +41.290 pp (+184% relative).\n", "- (tau=1.0, RH=1.0): mean ≈ 55.865% ; std ≈ 7.705% ; absolute increase = +33.365 pp (+148% relative).\n", "- (tau=1.5, RH=1.5): mean ≈ 51.136% ; std ≈ 11.683% ; absolute increase = +28.636 pp (+127% relative).\n", "\n", "

Temporal dynamics and rise times

\n", "\n", "- All traces show a fast transient rise from the initial 22.5% toward a plateau; the plateau level decreases with increasing tau/RH.\n", "- Approximate time to reach 90% of the steady mean:\n", " - tau=0.1: reached by 15.42 s (90% threshold ≈ 67.8%, measured mean at 15.42 s = 72.01%).\n", " - tau=0.5: reached by 30.85 s (90% ≈ 57.41%, mean at 30.85 s = 60.21%).\n", " - tau=1.0: reached by 46.27 s (90% ≈ 50.28%, mean at 46.27 s = 53.15%).\n", " - tau=1.5: reached by 46.27 s (90% ≈ 46.02%, mean at 46.27 s = 47.52%).\n", "- Interpretation of time column: the transient is faster (shorter rise time) for smaller tau; larger tau delays approach to final mean.\n", "\n", "

Variability (std) — absolute and relative

\n", "\n", "- Absolute steady std values vary non-monotonically with tau:\n", " - 0.1 → std ≈ 16.46\n", " - 0.5 → std ≈ 6.88 (smallest absolute scatter)\n", " - 1.0 → std ≈ 7.70\n", " - 1.5 → std ≈ 11.68\n", "- Coefficient of variation (std / mean) at steady state:\n", " - tau=0.1: ≈ 21.9%\n", " - tau=0.5: ≈ 10.8% (lowest relative variability)\n", " - tau=1.0: ≈ 13.8%\n", " - tau=1.5: ≈ 22.9% (highest relative variability)\n", "- Early-time std behavior: all cases show std decreasing from the large t=0 value (23.0489) toward their steady std; the largest early stds at t≈15 s occur for the larger-tau cases (e.g., tau=1.5: 18.01; tau=1.0: 14.03), while tau=0.5 already has comparatively low std (7.71).\n", "\n", "

Direct comparisons and clear trends

\n", "\n", "- Mean efficiency vs. tau/RH: higher efficiency is obtained at lower tau/RH; mean steady value decreases monotonically as tau and RH increase across the provided series.\n", "- Speed vs. tau/RH: lower tau reaches the steady fraction faster (tau=0.1 reaches 90% by ≈15 s; tau ≥ 1.0 requires ≈46 s).\n", "- Variability minimum near tau=0.5: both absolute std and relative variability (CV) are smallest at tau=0.5, indicating the most consistent stage efficiency in that parameter set.\n", "- Non-monotonicity: absolute std is not strictly monotonic with tau (it is smallest at tau=0.5, larger at both smaller and larger tau in this dataset).\n", "\n", "

Concise conclusions

\n", "\n", "- Reducing tau (and RH, in these paired cases) raises the final mean stage efficiency and accelerates the transient approach to that level.\n", "- The most favorable trade-off here (high mean with low variability) appears near tau=0.5: reasonably high mean (~63.8%) with the lowest absolute and relative std.\n", "- Larger tau values (1.0 and 1.5) give lower final means and, especially at tau=1.5, increased relative variability compared with the mid-range tau." ], "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 = 6518\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.')" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "## Inflow Relative Humidity Variation" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "inflow-relative-humidity 2.5\n", "Total mass rate density (mixture volume) residual [g/L-s]= -8.99075e-17\n", "total mass inflow rate [g/min] = 7.471e+02\n", "total mass outflow rate [g/min] = 7.471e+02\n", "\t net total mass flow rate [g/min] = -3.650e-02\n", "inflow-relative-humidity 12.5\n", "Total mass rate density (mixture volume) residual [g/L-s]= -4.50012e-17\n", "total mass inflow rate [g/min] = 7.471e+02\n", "total mass outflow rate [g/min] = 7.471e+02\n", "\t net total mass flow rate [g/min] = -3.650e-02\n", "inflow-relative-humidity 25.0\n", "Total mass rate density (mixture volume) residual [g/L-s]= -2.05998e-17\n", "total mass inflow rate [g/min] = 7.471e+02\n", "total mass outflow rate [g/min] = 7.471e+02\n", "\t net total mass flow rate [g/min] = -3.650e-02\n", "inflow-relative-humidity 37.5\n", "Total mass rate density (mixture volume) residual [g/L-s]= -3.07371e-17\n", "total mass inflow rate [g/min] = 7.471e+02\n", "total mass outflow rate [g/min] = 7.471e+02\n", "\t net total mass flow rate [g/min] = -3.649e-02\n", "Cortix AI assistant: working on explanation...\n", "\n" ] }, { "data": { "text/markdown": [ "

Overview

\n", "\n", "This snippet performs a parameter sweep over a list of multiplicative factors for an \"inflow-relative-humidity\" parameter, runs a simulation/processing routine for each parameter set, and collects the results.\n", "\n", "

Variables and initial setup

\n", "\n", "- A new list stages is created to collect outputs.\n", "- inflow_relative_humidity is set to the baseline value 25 (an integer).\n", "- param_inflow_rh_factors is a list of multiplicative factors [0.1, 0.5, 1.0, 1.5] used to scale the baseline humidity.\n", "- An assertion checks that param_tau_factors and param_inflow_rh_factors have equal length; param_tau_factors is expected to be defined elsewhere (it is not shown in the snippet).\n", "- The snippet relies on several external names that must exist in the surrounding scope: params (a dict-like object), run (a callable), cortix_ai, markdown_display, and db_save.\n", "\n", "

Main loop behavior

\n", "\n", "- The loop iterates over pairs produced by zip(param_tau_factors, param_inflow_rh_factors). For each iteration:\n", " - param_tau_factor and param_inflow_rh_factor are bound to the corresponding elements from the two lists.\n", " - params['inflow-relative-humidity'] is set to param_inflow_rh_factor * inflow_relative_humidity (a numeric multiplication producing a float).\n", " - The new value of params['inflow-relative-humidity'] is printed.\n", " - run(params) is called and its return value is stored in stg.\n", " - stg is appended to the stages list.\n", "- Note: param_tau_factor is iterated but not referenced inside the loop body; it is present in the zip to keep iteration counts aligned with param_tau_factors.\n", "\n", "

After the loop

\n", "\n", "- If cortix_ai evaluates as truthy, cortix_ai.explain(markdown_display=markdown_display, save_supporting_info=db_save) is invoked. (No explanation of what that call does is provided here.)\n", "- The stages list contains one element per iteration (each element is the stg returned by run(params)).\n", "\n", "

Side effects, types, and assumptions

\n", "\n", "- Side effects:\n", " - params is mutated in-place on each iteration (its 'inflow-relative-humidity' key is overwritten).\n", " - The code prints the currently set inflow-relative-humidity each iteration.\n", " - run(params) is invoked repeatedly and may have its own side effects.\n", "- Types and values:\n", " - param_inflow_rh_factors contains floats; multiplying by an integer baseline yields floats.\n", " - stages becomes a list of whatever objects run(params) returns.\n", "- Preconditions and potential runtime errors:\n", " - param_tau_factors must be defined and have the same length as param_inflow_rh_factors, otherwise the assert will raise or NameError will occur.\n", " - params, run, cortix_ai, markdown_display, and db_save must be defined in the surrounding context for the snippet to run without NameError." ], "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 = 3312\n" ] } ], "source": [ "'''Vary parameter of study'''\n", "stages = list()\n", "\n", "inflow_relative_humidity = 25\n", "param_inflow_rh_factors = [0.1, 0.5, 1.0, 1.5] # parameter variation\n", "#param_inflow_rh_factors = [0.2, 1, 1.7, 3.9]\n", "\n", "\n", "assert len(param_tau_factors) == len(param_inflow_rh_factors)\n", "for param_tau_factor, param_inflow_rh_factor in zip(param_tau_factors, param_inflow_rh_factors):\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": {}, "source": [ "### Overall Stage Efficiency\n", "\n", "Parameter variation impact on stage efficiency." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "editable": true, "scrolled": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "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", "This snippet collects stage efficiency histories, annotates each history with parameter metadata, plots each history (scaled to minutes), optionally invokes a cortix_ai helper, increments a figure counter, and prints a caption-like summary.\n", "\n", "

Step-by-step behavior

\n", "\n", "- The code imports Units from cortix as unit and matplotlib.pyplot as plt.\n", "- It creates an empty list efficiencies.\n", "- It iterates over an iterable stages and appends stg.efficiency_history(mean=True) for each stage to efficiencies.\n", "- It loops over the paired sequences efficiencies and param_inflow_rh_factors using zip, giving eff and rh for each iteration.\n", " - It assigns eff to local name quant (alias).\n", " - It mutates quant.name by appending a formatted metadata suffix that embeds the current tau and rh values.\n", " - It sets quant.value.name equal to the new quant.name (propagating the label into the value object).\n", " - It calls quant.plot(...) with:\n", " - title built using a raw string and old-style % formatting to insert rh into the title text (includes LaTeX-like fragment rh$_{\\mathrm{f}}$).\n", " - x_scaling=1/unit.min to convert the x-axis units to minutes.\n", " - x_label set to 'Time [min]'.\n", " - y_label constructed from quant.latex_name and quant.unit.\n", " - show=True so the plot is displayed when plot executes.\n", " - figsize=[10,3] for the plot figure size.\n", " - error_data=True and error_fill=False to control error representation.\n", "- If cortix_ai is truthy, the code calls cortix_ai.explain(markdown_display=markdown_display, save_supporting_info=db_save). \n", "- It increments fig_count by 1 and prints a short line with the updated figure number and a brief description.\n", "\n", "

Key objects and attributes referenced

\n", "\n", "- stages: iterable of stage-like objects; each stage provides an efficiency_history(mean=True) method.\n", "- stg.efficiency_history(mean=True): returns an object (here aliased quant) that is expected to have at least these attributes/methods:\n", " - name: a string label that the code mutates.\n", " - value: an object with a name attribute (the code sets quant.value.name).\n", " - latex_name: string used for y-axis label content.\n", " - unit: string used for y-axis unit display.\n", " - plot(...): a plotting method that accepts title, x_scaling, x_label, y_label, show, figsize, error_data, and error_fill among others.\n", "- param_inflow_rh_factors: iterable providing rh values zipped with efficiencies.\n", "- tau: external variable whose value is interpolated into quant.name (assumed defined elsewhere in the surrounding scope).\n", "- unit.min: a unit object representing minutes; used by x_scaling=1/unit.min to rescale the plot x axis to minutes.\n", "- cortix_ai, markdown_display, db_save: names in the outer scope; cortix_ai is tested for truthiness and then cortix_ai.explain(...) is invoked (behavior of explain() is not described here).\n", "- fig_count: integer in the outer scope incremented by this snippet and used in the printed summary.\n", "\n", "

Side effects produced by this snippet

\n", "\n", "- Mutates quant.name and quant.value.name for each plotted efficiency history.\n", "- Produces one plot per paired (efficiency, rh) item via quant.plot(...) with immediate display (show=True).\n", "- Optionally invokes cortix_ai.explain(...) when cortix_ai is truthy.\n", "- Increments the external fig_count variable and prints a summary line indicating the new figure number." ], "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 = 3585\n", "Figure 4: 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, rh in zip(efficiencies, 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/ rh$_{\\mathrm{f}}$ = %2.1f'%(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": {}, "source": [ "### Analysis\n", "\n", "LLM analysis." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cortix AI assistant: working on explanation...\n", "\n" ] }, { "data": { "text/markdown": [ "

Overview

\n", "\n", "- Four time-series show Stage Efficiency (mean and standard deviation [%]) at tau factor = 1.5 for relative humidity (RH) = 0.1, 0.5, 1.0, 1.5.\n", "- Each table columns: index (row id), time [s] (uniform steps of 15.4228 s), column \"0\" = mean efficiency [%], column \"1\" = standard deviation [%].\n", "- Common qualitative pattern: mean efficiency rises from an initial ~22.5% toward a plateau ≈50–51% while the standard deviation falls from ≈23% to ≈10–12%.\n", "\n", "

Time / index column analysis

\n", "\n", "- Time values are evenly spaced: 0, 15.4228, 30.8457, …, 246.765 s (increment = 15.4228 s).\n", "- Index column is a simple row identifier (0..16) and maps directly to the time steps.\n", "\n", "

Mean-efficiency (column 0) behavior

\n", "\n", "- Common transient: rapid increase during the first ≈4–7 steps, then asymptotic approach to a steady value.\n", "- Numerical endpoints (mean at t = 0 and t = 246.765 s):\n", " - RH = 0.1: 22.5 -> 50.4771 (Δ ≈ +27.98)\n", " - RH = 0.5: 22.5 -> 50.6654 (Δ ≈ +28.17)\n", " - RH = 1.0: 22.5 -> 50.9007 (Δ ≈ +28.40)\n", " - RH = 1.5: 22.5 -> 51.1360 (Δ ≈ +28.64)\n", "- Effect of RH on means:\n", " - Higher RH yields slightly larger mean at all nonzero times. Example at t = 15.4228 s: means = [35.7254, 35.8446, 35.9937, 36.1428] for RH = [0.1,0.5,1.0,1.5].\n", " - Long-time plateau increases modestly with RH (~0.66 percentage-point difference between RH 0.1 and 1.5).\n", "- Time-to-plateau:\n", " - Means reach ≈50% by roughly t ≈ 92.5 s (index 6) for all RH, with higher RH slightly exceeding 50 earlier and by a larger margin.\n", "\n", "

Standard-deviation (column 1) behavior

\n", "\n", "- Common transient: std drops steadily from an initial ≈23.0489% toward a lower steady value.\n", "- Numerical endpoints (std at t = 0 and t = 246.765 s):\n", " - RH = 0.1: 23.0489 -> 10.6134 (Δ ≈ −12.4355)\n", " - RH = 0.5: 23.0489 -> 10.9095 (Δ ≈ −12.1394)\n", " - RH = 1.0: 23.0489 -> 11.2907 (Δ ≈ −11.7582)\n", " - RH = 1.5: 23.0489 -> 11.6832 (Δ ≈ −11.3657)\n", "- Effect of RH on uncertainty:\n", " - Higher RH corresponds to a consistently larger standard deviation at each time step (e.g., at t = 15.4228 s std = [17.5673, 17.6917, 17.8508, 18.0137] for increasing RH).\n", " - Long-time std rises with RH from ≈10.61% (RH 0.1) to ≈11.68% (RH 1.5) — a modest increase (~1.07 percentage points).\n", "\n", "

Comparative summary and interpretation

\n", "\n", "- All four series share the same initial state and identical time sampling; differences arise only from relative humidity.\n", "- Mean efficiency:\n", " - Increases strongly from the initial value and then plateaus near 50–51%.\n", " - Higher RH yields a slightly higher plateau and slightly faster approach to that plateau.\n", "- Variability (std):\n", " - Decreases substantially from the initial high uncertainty and stabilizes at a lower value.\n", " - Higher RH leaves a higher residual uncertainty at long times.\n", "- Magnitude of RH effect:\n", " - Plateau mean shifts by ≲0.7 percentage points across the RH range (0.1 -> 1.5).\n", " - Long-time standard deviation shifts by ≈1.07 percentage points across the same RH range.\n", "- Practical takeaway:\n", " - RH produces small but systematic changes: higher RH raises both the steady-state mean efficiency and the steady-state uncertainty, while the transient dynamics (rise of mean, fall of std) are qualitatively the same for all RH values.\n", "\n", "

Key numeric highlights

\n", "\n", "- Time increment: 15.4228 s (constant).\n", "- Mean rise (t = 0 -> final): +~28 percentage points for all RH (small RH dependence).\n", "- Std fall (t = 0 -> final): −~11.4 to −12.4 percentage points (larger absolute drop at lower RH).\n", "- Final values (mean, std) by RH:\n", " - RH 0.1: (50.48%, 10.61%)\n", " - RH 0.5: (50.67%, 10.91%)\n", " - RH 1.0: (50.90%, 11.29%)\n", " - RH 1.5: (51.14%, 11.68%)" ], "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 = 6062\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": 14, "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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oKB0/flyrV6/WnDlztGfPHkdfwzD02GOPKTk5WXfddVe96wlVGRkZSkxMdFzv2bPHaTfE8PBwZWRk1Dg+KSnJp/P4Snl5uUaNGqUffvjB5Z7ZbFafPn00fPhwtW/fXi1atFBJSYmys7O1bt06/fzzz9q4caNP63EnlF6jb7zxhp5++mnHdUpKikaPHq1+/fopJSVFJSUl2r59uz7//HNt2LDBaeyRI0d0xx136Ouvv/ZorUA8N+PGjdNDDz3kFMp7/fXXdeWVV3pUY1XZ2dn63//+59Q2evRodezY0eu5ajNgwADFxsaqsLDQ0TZ//nwNGzas1nE7d+5UVlaW23s//fSTCgoKFBcXV+sc1UN36enp6tSpk4eVS+vXr9eOHTsc1/Hx8XXW7Q+PPfaYU7gyKSnJ8TpOTU1VcXGx9u3bpzlz5ni8C18gfzbgH5s3b3Zp69KlS73mioiIUNu2bZ1+5nbs2CGr1VqvEB8AAAAAIEQE7WBbAAAAAACaiCNHjhjR0dGGJJePyy+/3FiyZIlf158xY4bLullZWQ2aMyYmxrj55puNuXPnGkVFRR6NWb9+vTFixAiXWu6++26Pxh89etRITU11Gd+vXz9j7dq1NY7buXOncfHFFzv6u3suFi1a5FENjz32mMvYtm3bGh9//LFhtVprHFdeXm5Mnz7diIuLcxobERFhZGZmerR2Q2RlZbnUfdFFF/l93Uo333yz09odOnQI2jyLFi2q9/N/9913u/05vuqqq4zNmzfXOX7btm3GE088YaSlpRkzZszwS43Beo1OmjTJZd2oqChDkhEeHm48/fTTRnFxsduxdrvdePHFFw2z2ewyx48//ujR4w7UczNx4kSn+U0mk7Ft2zaPaqxq6tSpLrV+/fXXXs/jid/85jcuf2bW5Y033nB5nN7W2rp1a6cxt99+u1d1T5kyxWn8Nddc49X4+nD3OrZYLI7Pjz76qFFQUFDjeHev8WD/bATLRRdd5PZnMhAfkyZN8vvje+SRR1zWnTx5cr3nc/f92rFjhw8rrr8OHTq41AYAAAAAqBvHywIAAAAA0EApKSmaOHGi23uzZs3SoEGD1LZtW916662aPn261q5dK6vVGuAqvXPgwAHNnDlTo0aNUnR0tEdjunfvrrlz5+rWW291ap85c6bT7mU1efTRR3XkyBGntkGDBmnx4sXq2bNnjePS09M1Z84cXXXVVZIqjlesjx9//NFll7wBAwZo/fr1uuaaa2SxWGocGxYWpttuu01Lly512tWvrKxMjzzySL3qQWDNnTtXr776qlObyWTS1KlT9emnn+qss86qc44uXbro0Ucf1a5du/Sb3/zG5zWG2mu0pKREkZGRmjNnjv72t78pKirKbT+TyaQ//elPbo+zffPNN+tcJ5DPTfUjZg3D0BtvvFHn/NXHVD/qukOHDho9erRX83iq+pGumZmZdf6ZW32XuquvvrrW+9Vt2rRJBw4cqLWOulQ/Wnbs2LFejfcVm80ms9msDz74QE888YRiY2Nr7FvTa7y6QP1swH8OHTrk0tauXbt6z+du7OHDh+s9HwAAAAAg+AjdAQAAAADgA0888YQGDBhQ4/39+/drxowZ+sMf/qDevXsrPj5e/fv315/+9Cd98sknIfeL12bNmtVrnNls1quvvur0y+XCwkJ98MEHtY7Ly8vTu+++61LDxx9/rJiYmDrXDQsL03/+8x+1b9++XnVL0pQpU2S32x3XrVu31uzZs736XvTq1UuvvfaaU9ucOXO0du3aetdVX6tWrVLv3r0b/LFly5aA1x4MTzzxhEvbQw89pPvuu8/ruaKiojw+vtkbofgafe655zR8+HCP+j7wwANq06aNU9u3335b57hAPjddu3Z1eTwzZsxQaWmpx2vMnz9f27dvd2q74447ZDb7559iq4fd7Ha7Fi5cWGN/wzCc7kdERLh8j+fNm1frmtVDeSaTyaujYffv369Vq1Y5rsPCwnTJJZd4PN7X/vznP+uaa67x6ZyB+NmA/xw/ftylra4jl2vjbmx2dna95wMAAAAABF9YsAsAAAAAAKApiIqK0uzZszVu3DjNmjWrzv4lJSVasWKFVqxYoZdfflkmk0mDBw/WuHHjdNNNNykyMjIAVftHVFSUrrnmGv2///f/HG1Lly7VH//4xxrH/Pe//1VhYaFT29/+9jevgkuxsbF66qmn9Lvf/c7rmtevX6/Zs2c7tT311FNKTEz0eq4bb7xRkydP1rZt2xxtX3zxhXr16uX1XA1RWFjok7BffXcObEyWLFmiH3/80amte/fumjx5cpAqchWKr9FOnTq57AxXm/DwcF133XVOfzbs27dPR44cUWpqqtsxwXhuJk6cqAULFjius7Oz9fHHH+v3v/+9R+Nff/11p+vw8HBNmDDBpzVW1bNnT6WkpOjo0aOOtvnz57vsXldp9erVTmGfAQMGqFu3bjrjjDMcr4kNGzbo0KFDNf4ZXD1016tXLyUnJ3tc81dffSXDMBzXF110Ub1ey74QHx+vxx9/3KdzBuJnI9i6dOminJycoKztj1BzddX/TiLJ451/3XE3tqioqN7zAQAAAACCj9AdAAAAAAA+kpiYqC+//FLvvfeennjiCZedjmpjGIa+//57ff/995o8ebKeeuop3XTTTX6s1r/OOOMMp+uffvqp1v7Vd2WyWCy6+eabvV736quv1sSJE70OAnzyySdO1/Hx8bruuuu8Xl+q2PFp9OjRToGmxYsXa9KkSfWaD/731VdfubT95S9/UVhY6PzTWSi+Rm+99Vavd28777zzXNq2bNlSY7AoGM/N5Zdfrg4dOmj37t2OtmnTpnkUujtw4IBL8Pqqq67ya3Cqcpe5jz76yNFW2/Gw1e9V7pQ3YsQIp9fE/Pnz3YaYbTabFi9e7HYOT1U/WnbMmDFejfel6667rkE7mLkTiJ+NqqxWq9555x198sknWrt2rY4fP+7YnbFZs2Z+Ccc19eNvy8vLXdo8PV7YHXehu7KysnrPBwAAAAAIvtD5l0MAAAAAAJoAk8mk3//+97rhhhs0d+5cvf/++/rmm2+Ul5fn8Rx79uzR7373O82fP1/Tpk0LiV3vsrOztWTJEq1bt06bNm3SiRMnlJ+fr8LCQqfdiipVP5Zt7969tc5fPZR3zjnnqFWrVl7XGRUVpeHDh+vTTz/1atz333/vdH3uuec26Jfr6enpTterV6+u91zwv+oBovDwcF1//fXBKaYGofgaveiii7we07lzZ5e23NzcGvsH47mxWCy688479dBDDznali9frl9//VU9e/asdez06dNltVqd2u666y6/1FnVxRdf7BS62759u/bs2eP2yO3qobsRI0Y45qh69PC8efPchu5Wrlzp8p7mTeguLy9PixYtcmoLZuhu6NChPp8zED8blQoKCjRixIg6w+1oOJPJ5NOx7v7+BAAAAABoPAjdAQAAAADgB2FhYbrssst02WWXyWazac2aNVq6dKlWrlyp1atXa8uWLbLZbLXOMXPmTBUVFTkFKQJtwYIFeuGFF/Ttt9+63fXFU1arVQUFBW53E8rJydG+ffuc2s4999x6r3XOOed4Fbqz2WwuYYVff/1VvXv3rncN1UOHubm5Ki8vV3h4eL3n9NZFF13kEliCq9LSUpfAWe/evRUTExOkilyF6mu0+o6WnmjWrJlLW03BomA+N7fddpsmT56skpISR9vrr7/ucnRsVTabTdOnT3dqy8jIqFcAy1vuQm/z5s1zOda2tLRUS5cudVwnJiaqb9++kirCZxaLxfHeVPWI3aqqh/YiIyM1aNAgj2udM2eO0w5f55xzjttwYKA05P2mJv7+2ajq73//u9OfD+3atdMZZ5zh+Fn29S5+pwt3fxY25Lh1d2MjIiLqPR8AAAAAIPgI3QEAAAAA4GcWi0V9+vRRnz59HG1FRUX6+eeftWjRIv3vf//T5s2b3Y79+OOPNXDgQN1zzz2BKldSxU5Et99+uz7++GOfzZmbm+v2l//Z2dkubR07dqz3OtV38KpLdna2U7BGkk6cOKETJ07UuwZ3jh8/rrS0NJ/OiYY7duyYSwD27LPPDlI17oXqazQpKcnrNdwFWWoK9AbzuUlOTtZ1112nd955x9H23nvv6dlnn1V8fLzbMV999ZX279/v1HbnnXf6tc5KHTt2VOfOnbVjxw5H2/z5811Cd8uWLXMK/wwZMkQWi0VSRQCvT58+WrFihSRp//792rhxozIyMpzmqB66GzBggFdByFA6WlaSX47+9ffPRiWbzeb0Gn3yySf197//3eu14crda9rXobvY2Nh6zwcAAAAACD5zsAsAAAAAAOB0FBMTo6FDh+qJJ57Qpk2bNHfuXHXv3t1t3ylTpqioqChgteXl5WnUqFE+DdxJNYcH3AWH3O344ylvx7oL/flDQ35ZD/+pvuObJDVv3jwIldQsVF+j/t65MdjPzd133+10XVBQoP/+97819p82bZrTdUxMjMaNG+eX2typvtvdggULXI6vrOlo2ZrmqN6/qKhIP/74Y61jalNeXq7Zs2c7tQU7dJeQkODzOQO1q+m2bdscR/2GhYXpgQceCMi6p4MWLVq4tBUUFNR7Pndj3a0BAAAAAGg8CN0BAAAAABACRo0apZUrV2r06NEu944cOaKvvvoqYLX85S9/cTnKUqo4Lu/Pf/6zPv74Y61YsUIHDhxQbm6uSktLZRiG08eMGTM8Xq+0tNSlrSFHrkVGRnrV39e7haFxqQysVBVqxzGerq/RYD83/fr103nnnefUVtPxstu3b9e8efOc2m688cYGBYi9VT38dvToUa1du9aprXqIrvqYukJ3P/zwg9PRsO7G1Gbx4sVOR6Z26NChQcck+0JYWOM9DKZqILdly5YBPcK8qXO36+e+ffvqPd/evXs9WgMAAAAA0Hg03n9RAAAAAACgiYmOjtaHH36ozp0769ixY073FixYoOuvv97vNaxbt05vv/22U1tcXJymTZumG2+8USaTyaN5vNkxy10oJT8/3+Px1bkL6tQmOjrape26667Thx9+WO8a0Hi42+WqIbsZ+cPp+hoNhedm4sSJTrvV/frrr1q+fLkuuOACp35vvPGGy65ygTpattLQoUNlNptlt9sdbfPnz3eE2nJycpSZmem4165dO5155plOc1xwwQWKiYlx7K66ePFiWa1WRzBtwYIFTv2bNWumvn37elxjqB0t29hV3UG28pjgQLntttu0atWqgK5Z6c477/T7z5e7o+p3795d7/n27NnjdG2xWNS+fft6zwcAAAAACD5CdwAAAAAAhJCEhATdcsstmjp1qlP7li1bArL+Rx995BIceeedd3TVVVd5NY+7YyFr4u64yIYcp+nt2OTkZJc2b+pH4+bueL9Q21nudH2NhsJzc+211+q+++7T0aNHHW2vv/66U+iutLTUZXfPfv36qU+fPgGrU6r4fvXu3Vu//PKLo23+/Pm6//77JUkLFy50CuRVP1pWqtgpdNCgQfr2228lVQSgf/75Z1144YWO+aoaMmSIV2Gv6ru2Errz3uLFizV06FCX9t27d7sNxmdlZaljx44+r2P79u0uOykGyqFDh/y+xllnneXStn379nrNVVZW5rLTXefOnRv1LosAAAAAAI6XBQAAAAAg5FQ/zlCSy853/lL9eMTu3bt7HbiTpJ07d3rcNzU11eVI2HXr1nm9ZqVff/3Vq/4pKSkuQYWG7GaDxiU5Odkl+ODta8jfTtfXaCg8N5GRkbr99tud2j755BOncO///vc/l7DvXXfdFZD6qqt+1OuSJUscx8HWdbRsTe2V444dO+YSsvLmaNnMzEyn4FHz5s01ePBgj8cDgXTOOefIbHb+9cmqVatktVq9nmvVqlVOuxJK0rnnntug+gAAAAAAwUfoDgAAAACAEOPuuNVA7YZSfSeWQYMG1WueH3/80eO+4eHhjuMPK61YscJpRyZv/PTTT171j4qKUq9evZzatm7dqsOHD9drfTQuERERLuGHNWvWqLCwMEgVuTpdX6Oh8tzceeedTru5lZSUOO1s9/rrrzv1b968eUCOA3enegiuqKhIy5Ytk+QcujOZTBo+fLhHc1SGsRcsWOCyE6o3obvqR8tecskl7PRVD0lJSRo1apRGjRqlfv36OdqjoqIc7VU/3B1PjbrFxsbqnHPOcWorLCzU6tWrvZ5r6dKlLm0ETgEAAACg8SN0BwAAAABAiHEXpElLS6uxv7vQgs1mq9fa1XfUS0pK8nqOdevWadOmTV6NGTBggNP1oUOHtGjRIq/X3rp1q1atWuX1OHfHLH722Wdez4PGaciQIU7XVqtVH374YXCKqcHp+hoNheemXbt2uuKKK5za3njjDRmGoV9//VXLly93unfzzTcHLeg0cOBAl51D58+frz179mjbtm2Otp49eyo1NdXtHL169VJKSorj+ueff1ZBQYHLTnlt27ZV165dPa6teuiOo2Xrp2fPnpo7d67mzp2rZ5991tGelpbmaK/6UdvfHxpi8eLFMgwjKB+PP/64Xx5Tdb/5zW9c2j755BOv53E3xt3cAAAAAIDGhdAdAAAAAAAhZuHChS5tnTt3rrF/fHy8S1tBQUG91o6NjXW6rs+xtv/v//0/r8dcd911Lm1Tp071ep7nnnvO6zGS+/DH1KlT63WMHBqfK6+80qXt+eefD6nn/3R9jYbKczNx4kSn6+3bt2v+/PmaNm2aS98777wzUGW5iI6O1oUXXujUNn/+fI+PlpUqdsEbNmyY49pqtWrx4sVasGCBU7+adspzJysry+lo4MjISI0ePdrj8UAwuNuxcsaMGY4jmz2xevVqrVy50qmtf//+Sk9Pb3B9AAAAAIDgInQHAAAAAEADzZo1S1lZWT6Za8eOHfr4449d2i+99NIaxzRv3tylbefOnfVav1WrVk7X8+fP9+qY1/nz5+udd97xet3zzz/f5Ri3uXPn6r333vN4joULF+qtt97yem1JuvDCC1121Nq5c6fuu+++es2HxuX88893Oepvw4YNmjRpUpAqcnW6vkZD5bkZNmyYMjIynNqee+45lz+jhg0bprPOOiuQpbmoHqjLzMx02WmrrmNhq++s+MYbb7i8zzXkaNlhw4YpLi7O4/FAMPTo0UMDBw50ajt69Kief/55j+d46KGHXNruuuuuBtcGAAAAAAg+QncAAAAAADTQN998ozPPPFPjx4/X5s2b6z3PgQMHdOWVV6qoqMipPSUlxe3RkpV69Ojh0jZ79ux61TBo0CCn6507d7rdycmdX375RTfccIMMw6jX2o8++qhL24QJEzw6QvOHH37Q2LFj6722JE2ZMkUmk8mp7aWXXtKkSZPqPe/69es1btw4nThxot51ITAee+wxl7ann366Xjs3lpaW6tChQ74oy8np+hoNlefm7rvvdrqeN2+e8vPzndqCuctdpeo70NlsNs2ZM8dxHRER4RJkrK56oO7rr7+uc53acLQsGqtHHnnEpW3SpEn65Zdf6hz7yiuv6Ntvv3Vq69Spk2688UaP17/llltkMpmcPgJ1vC4AAAAAoHaE7gAAAAAA8AGr1aqZM2eqW7duOv/88/XKK6/o4MGDHo0tKirStGnTdM4552jdunUu95977jlFRUXVOD4pKUldu3Z1apsxY4aef/555ebmevU4rr32Wpe2e++9V6+99lqNoR6bzaZXX31VQ4cOdRxHm5CQ4NW6UsUxktXXLysr09VXX63rrrtOS5Yscdl1b+XKlbrjjjs0dOhQR/hlwIABXq8tVewk5m73rCeeeELDhg3TkiVLPJonOztbb775pkaMGKGePXvq3Xfflc1mq1dNCJzhw4frL3/5i1ObYRi677779Nvf/lZbt26tc46srCw9+eST6tixo+bOnevzGk/X12ioPDfjxo2r9c+2li1bauzYsfWa25f69u2rxMTEGu9fcMEFiomJqXWODh06qEuXLjXez8jIcNkZtSbHjx93em2aTCZdccUVHo0Fgm3UqFEuIdHS0lINHTpUs2bNcjumvLxc//jHP3TPPfe43HvppZcUHh7ul1oBAAAAAIEVFuwCAAAAAABoan7++Wf9/PPPuueee9SxY0f1799fGRkZSk5OVosWLWQymZSXl6fdu3dr7dq1WrhwoQoLC93Ode211+rmm2+uc81bb71VDz74oOPaZrPpL3/5i+677z61bdtWzZo1k8VicRpz5513uuzKNHz4cA0ePFg//PCDo81qteruu+/Wiy++qCuvvFIZGRmKjo7W0aNHtX79en355Zc6cOCAo39aWpruu+8+p3o89frrr2vTpk0u4cOPP/5YH3/8sWJiYtSyZUtZLBYdPHhQBQUFTv3OPfdcPfbYYxo9erRTe/XHXpPHHntMmzdv1ocffujUvnjxYg0ePFhnnnmmhgwZou7duyspKUmRkZHKycnRiRMntHHjRmVmZmrTpk0hEWBatWqVevfu7ZO5nnjiidMiJPPMM89o9erVWrRokVP7p59+qs8//1x9+/bV8OHD1aFDByUlJamkpETHjx/X+vXrtXLlSq1du9bvNTal16g3QuG5iYuL07hx4/TKK6+4vX/bbbeFRJjGbDZr6NCh+vzzz93e9/RY2Isvvljbt29v0BxSxS55VV9v5513nseBPSAUTJ8+XZmZmdq3b5+jLS8vT1dccYX69u2rMWPGKD09XcXFxdq2bZv++9//av/+/S7zTJw4UZdeemkgS5ckXXLJJU5/T6vO3b26/v4we/ZstW7duqGlAQAAAECjRugOAAAAAAA/2rVrl3bt2lWvsTfffLPeeustj/refffdeuedd7RhwwandsMwtHfvXu3du9dlTE1HLL777rs677zzdPjwYaf2rVu36plnnqm1joSEBH3zzTdud+zzRFJSkhYuXKgRI0ZozZo1LveLioq0c+dOt2N79Oihr7/+2uV7UFmXJ0wmk/773/+qc+fOeuqpp1x299u6datHu2qFgsLCQp+FwI4fP+6TeUJdWFiYZs+erVtvvVUffPCB0z273a4VK1ZoxYoVQaquQlN6jXojVJ6bu+++W6+++qrL991isegPf/iD39f31MUXX1xj6K6248qrz1HT8eLehO44WhaNXUpKir777jsNGzbM5e9Oq1at0qpVq+qc49prr9ULL7zgpwprt3HjRu3evdurMXX9/aGsrKwhJQEAAABAk8DxsgAAAAAANNDvf/973XTTTbUe5+eNTp066csvv9TMmTM93qEtJiZG3377rcdhitq0b99eCxcu1FlnneXVuLPOOkvLly9Xnz59GrR+cnKyfvrpJ02aNEmRkZF19rdYLLrjjju0bNkytWrVSidOnHDp06xZM4/XN5vNmjJlimbPnq1evXp5Vbu7dW+77TbFxcU1aB4ETlRUlN5//329/PLLSk1NrdccYWFhatGihY8rO+V0fY2GwnPTtWtXDR8+3KX90ksvVbt27eo9r6/VFIpLTExU3759PZpj2LBhMptd//k4LCxMQ4YM8WiOkpISffvtt05thO7QGHXr1k0rVqzQwIEDvRoXHh6uSZMm6YMPPvD473QAAAAAgMaB0B0AAAAAAA104YUX6r333tORI0e0YMECPfbYYxo2bJhXIZa0tDTddNNN+uabb7Rly5Z6HeXZpk0bfffdd8rMzNTf/vY3jRo1Sh07dlRiYqLCwrzb7D4jI0OrVq3Sk08+qZYtW9bat1u3bnrppZf066+/qnv37l7X7U5kZKQef/xx7dq1S6+88opGjhypLl26KDY2VhEREWrZsqWGDBmiyZMna9u2bZo2bZpjNzt3O/glJSV5XcNvfvMbrVmzRrNmzdJVV13l8RydOnXS7bffrk8++UQHDx7U9OnTFRUV5fX6CK6JEydq586devbZZ3Xeeee5DR9VZTab1b9/f/3jH//Qrl27dPnll/u9xtP1NRrs58ZdaO2uu+5q0Jy+duaZZ7oNAQ4dOrTO71el5s2buw1Rn3feeYq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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Figure 5: 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/ rh$_{\\mathrm{f}}$ = %2.1f'%(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": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Figure 6: 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.')\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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 }