From 1e8c7258c43bd5a62886f69dfc125794fb3e6953 Mon Sep 17 00:00:00 2001 From: Sunniva Indrehus Date: Tue, 22 Oct 2024 21:10:53 +0200 Subject: [PATCH] content: talk structure --- demo/demo/parameters_values_lmfit.py | 38 ++-- talk/slides.html | 279 +++++++++++++++------------ talk/slides.md | 185 ++++++++++-------- 3 files changed, 283 insertions(+), 219 deletions(-) diff --git a/demo/demo/parameters_values_lmfit.py b/demo/demo/parameters_values_lmfit.py index 7a06b15..70cae82 100644 --- a/demo/demo/parameters_values_lmfit.py +++ b/demo/demo/parameters_values_lmfit.py @@ -1,11 +1,11 @@ import numpy as np -from lmfit import Minimizer, create_params, report_fit, minimize +from lmfit import Minimizer, create_params, report_fit import matplotlib.pyplot as plt import matplotlib -matplotlib.use("Agg") +matplotlib.use("Agg") # create data to be fitted @@ -20,17 +20,16 @@ ) -# define objective function: returns the array to be minimized -def fcn2min(params, x, data): - """Model a decaying sine wave and subtract data.""" - v = params.valuesdict() - - model = v["amp"] * np.sin(x * v["omega"] + v["shift"]) * np.exp(-x * x * v["decay"]) +def residual(parameters, x, data): + model = ( + parameters["amp"] + * np.sin(x * parameters["omega"] + parameters["shift"]) + * np.exp(-x * x * parameters["decay"]) + ) return model - data -# create a set of Parameters -params = create_params( +parameters_initial = create_params( amp=dict(value=10, min=0), decay=0.1, omega=3.0, @@ -38,24 +37,23 @@ def fcn2min(params, x, data): ) initial_model = ( - params["amp"].value - * np.sin(x * params["omega"].value + params["shift"].value) - * np.exp(-x * x * params["decay"].value) + parameters_initial["amp"].value + * np.sin(x * parameters_initial["omega"].value + parameters_initial["shift"].value) + * np.exp(-x * x * parameters_initial["decay"].value) ) -# do fit, here with the default leastsq algorithm -minner = Minimizer(fcn2min, params, fcn_args=(x, data)) -result = minner.minimize(method="least_squares") -# calculate final result +minimizer = Minimizer(residual, parameters_initial, fcn_args=(x, data)) +result = minimizer.minimize(method="least_squares") + +# calculate final final = data + result.residual # write error report report_fit(result) -# try to plot results -plt.plot(x, data, "+", label="data", color="red") +plt.scatter(x, data, label="data", color="red") plt.plot(x, initial_model, label="initial", linestyle="--", color="green") plt.plot(x, final, label="final", color="blue") plt.legend() -plt.savefig("parameters_values_lmfit.png") +plt.savefig("parameters_values_lmfit.png", bbox_inches="tight", pad_inches=0.03) diff --git a/talk/slides.html b/talk/slides.html index dd01bd2..8ba9750 100755 --- a/talk/slides.html +++ b/talk/slides.html @@ -12,17 +12,49 @@ /* content:""; 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:where(.hljs-title),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-title.class_),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-title.class_.inherited__),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-title.function_){color:var(--color-prettylights-syntax-entity)}div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-attr),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-attribute),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-literal),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-meta),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-number),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-operator),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-selector-attr),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-selector-class),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-selector-id),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-variable){color:var(--color-prettylights-syntax-constant)}div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-meta .hljs-string),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-regexp),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-string){color:var(--color-prettylights-syntax-string)}div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-built_in),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-symbol){color:var(--color-prettylights-syntax-variable)}div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-code),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-comment),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-formula){color:var(--color-prettylights-syntax-comment)}div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-name),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-quote),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-selector-pseudo),div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-selector-tag){color:var(--color-prettylights-syntax-entity-tag)}div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-subst){color:var(--color-prettylights-syntax-storage-modifier-import)}div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-section){color:var(--color-prettylights-syntax-markup-heading);font-weight:700}div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-bullet){color:var(--color-prettylights-syntax-markup-list)}div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-emphasis){color:var(--color-prettylights-syntax-markup-italic);font-style:italic}div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-strong){color:var(--color-prettylights-syntax-markup-bold);font-weight:700}div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-addition){background-color:var(--color-prettylights-syntax-markup-inserted-bg);color:var(--color-prettylights-syntax-markup-inserted-text)}div#\:\$p>svg>foreignObject>section :is(pre,marp-pre) :where(.hljs-deletion){background-color:var(--color-prettylights-syntax-markup-deleted-bg);color:var(--color-prettylights-syntax-markup-deleted-text)}div#\:\$p>svg>foreignObject>section footer,div#\:\$p>svg>foreignObject>section header{color:var(--header-footer-color);font-size:18px;left:30px;margin:0;position:absolute}div#\:\$p>svg>foreignObject>section header{top:21px}div#\:\$p>svg>foreignObject>section footer{bottom:21px}div#\:\$p>svg>foreignObject>section{--h1-color:#246;--header-footer-color:hsla(0,0%,40%,.75);--heading-strong-color:#48c;--paginate-color:#777;align-items:stretch;display:flex;flex-flow:column nowrap;font-size:29px;height:720px;justify-content:center;padding:78.5px;width:1280px}div#\:\$p>svg>foreignObject>section{--marpit-root-font-size:29px}div#\:\$p>svg>foreignObject>section:where(.invert){--h1-color:#cee7ff;--header-footer-color:hsla(0,0%,60%,.75);--heading-strong-color:#7bf;--paginate-color:#999}div#\:\$p>svg>foreignObject>section>:last-child,div#\:\$p>svg>foreignObject>section[data-footer]>:nth-last-child(2){margin-bottom:0}div#\:\$p>svg>foreignObject>section>:first-child,div#\:\$p>svg>foreignObject>section>header:first-child+*{margin-top:0}div#\:\$p>svg>foreignObject>section:after{bottom:21px;color:var(--paginate-color);font-size:24px;padding:0;position:absolute;right:30px}div#\:\$p>svg>foreignObject>section:after{--marpit-root-font-size:24px}div#\:\$p>svg>foreignObject>section[data-color] :is(h1,marp-h1),div#\:\$p>svg>foreignObject>section[data-color] :is(h2,marp-h2),div#\:\$p>svg>foreignObject>section[data-color] :is(h3,marp-h3),div#\:\$p>svg>foreignObject>section[data-color] :is(h4,marp-h4),div#\:\$p>svg>foreignObject>section[data-color] :is(h5,marp-h5),div#\:\$p>svg>foreignObject>section[data-color] :is(h6,marp-h6){color:currentcolor}div#\:\$p>svg>foreignObject>section mjx-container[jax=SVG]{direction:ltr}div#\:\$p>svg>foreignObject>section mjx-container[jax=SVG]>svg{overflow:visible;min-height:1px;min-width:1px}div#\:\$p>svg>foreignObject>section mjx-container[jax=SVG]>svg a{fill:blue;stroke:blue}div#\:\$p>svg>foreignObject>section mjx-container[jax=SVG][display=true]{display:block;text-align:center;margin:1em 0}div#\:\$p>svg>foreignObject>section mjx-container[jax=SVG][display=true][width=full]{display:flex}div#\:\$p>svg>foreignObject>section mjx-container[jax=SVG][justify=left]{text-align:left}div#\:\$p>svg>foreignObject>section mjx-container[jax=SVG][justify=right]{text-align:right}div#\:\$p>svg>foreignObject>section g[data-mml-node=merror]>g{fill:red;stroke:red}div#\:\$p>svg>foreignObject>section g[data-mml-node=merror]>rect[data-background]{fill:yellow;stroke:none}div#\:\$p>svg>foreignObject>section g[data-mml-node=mtable]>line[data-line],div#\:\$p>svg>foreignObject>section svg[data-table]>g>line[data-line]{stroke-width:70px;fill:none}div#\:\$p>svg>foreignObject>section g[data-mml-node=mtable]>rect[data-frame],div#\:\$p>svg>foreignObject>section svg[data-table]>g>rect[data-frame]{stroke-width:70px;fill:none}div#\:\$p>svg>foreignObject>section g[data-mml-node=mtable]>.mjx-dashed,div#\:\$p>svg>foreignObject>section svg[data-table]>g>.mjx-dashed{stroke-dasharray:140}div#\:\$p>svg>foreignObject>section g[data-mml-node=mtable]>.mjx-dotted,div#\:\$p>svg>foreignObject>section svg[data-table]>g>.mjx-dotted{stroke-linecap:round;stroke-dasharray:0,140}div#\:\$p>svg>foreignObject>section g[data-mml-node=mtable]>g>svg{overflow:visible}div#\:\$p>svg>foreignObject>section [jax=SVG] mjx-tool{display:inline-block;position:relative;width:0;height:0}div#\:\$p>svg>foreignObject>section [jax=SVG] mjx-tool>mjx-tip{position:absolute;top:0;left:0}div#\:\$p>svg>foreignObject>section mjx-tool>mjx-tip{display:inline-block;padding:.2em;border:1px solid #888;font-size:70%;background-color:#F8F8F8;color:black;box-shadow:2px 2px 5px #AAAAAA}div#\:\$p>svg>foreignObject>section g[data-mml-node=maction][data-toggle]{cursor:pointer}div#\:\$p>svg>foreignObject>section mjx-status{display:block;position:fixed;left:1em;bottom:1em;min-width:25%;padding:.2em .4em;border:1px solid #888;font-size:90%;background-color:#F8F8F8;color:black}div#\:\$p>svg>foreignObject>section foreignObject[data-mjx-xml]{font-family:initial;line-height:normal;overflow:visible}div#\:\$p>svg>foreignObject>section mjx-container[jax=SVG] path[data-c],div#\:\$p>svg>foreignObject>section mjx-container[jax=SVG] use[data-c]{stroke-width:3}@media print{div#\:\$p>svg>foreignObject>section mjx-container[jax=SVG] path[data-c],div#\:\$p>svg>foreignObject>section mjx-container[jax=SVG] use[data-c]{stroke-width:0}}div#\:\$p>svg>foreignObject>section img[data-marp-twemoji]{background:transparent;height:1em;margin:0 .05em 0 .1em;vertical-align:-.1em;width:1em} /* ngi-theme.css*/ /* @theme ngi-theme */div#\:\$p>svg>foreignObject>section{ - /* Override default background */background-color:#F5F5F5;color:black;font-family:'Times New Roman' - /*justify-content: flex-start;*/}div#\:\$p>svg>foreignObject>section :is(h1,marp-h1){color:#B22222}div#\:\$p>svg>foreignObject>section slide{font-family:'Times New Roman',serif!important}div#\:\$p>svg>foreignObject>section:after{content:attr(data-marpit-pagination) ' / ' attr(data-marpit-pagination-total)}div#\:\$p>svg>foreignObject>section td,div#\:\$p>svg>foreignObject>section th{border:none!important} + /* Override default background */background-color:#F5F5F5;color:black;font-family:'Times New Roman' + /*justify-content: flex-start;*/}div#\:\$p>svg>foreignObject>section :is(h1,marp-h1){color:#B22222}div#\:\$p>svg>foreignObject>section slide{font-family:'Times New Roman',serif!important}div#\:\$p>svg>foreignObject>section:after{content:attr(data-marpit-pagination) ' / ' attr(data-marpit-pagination-total)}div#\:\$p>svg>foreignObject>section td,div#\:\$p>svg>foreignObject>section th{border:none!important} /* Reset table styling provided by theme */div#\:\$p>svg>foreignObject>section table,div#\:\$p>svg>foreignObject>section td,div#\:\$p>svg>foreignObject>section th,div#\:\$p>svg>foreignObject>section tr{all:unset; /* Override contextual styling */border:0!important;background:transparent!important}div#\:\$p>svg>foreignObject>section table{display:table}div#\:\$p>svg>foreignObject>section tr{display:table-row} -/* ...and layout freely :) */div#\:\$p>svg>foreignObject>section table{width:100%}div#\:\$p>svg>foreignObject>section td{text-align:center;vertical-align:middle}div#\:\$p>svg>foreignObject>section td,div#\:\$p>svg>foreignObject>section th{display:table-cell}div#\:\$p>svg>foreignObject>section ul{list-style-type:none;margin-left:0.2px}div#\:\$p>svg>foreignObject>section li:before{content:"";background-image:url(./figures/logo/NGI/NGI_pil_RGB_dark_grey_medium.png);width:15px;height:15px;display:inline-block;background-size:contain;margin-right:calc(var(--marpit-root-font-size, 1rem) * 0.25)}div#\:\$p>svg>foreignObject>section :is(h2,marp-h2){color:gray}div#\:\$p>svg>foreignObject>section img{background-color:transparent!important;box-sizing:initial;max-width:100%}div#\:\$p>svg>foreignObject>section figure{border:thin #c0c0c0 solid;display:flex;flex-flow:column;padding:5px;max-width:220px;margin:auto}div#\:\$p>svg>foreignObject>section.title{--title-height:130px;--subtitle-height:70px;--subsubtitle-height:50px;overflow:visible;display:grid;grid-template-columns:1fr;grid-template-rows:1fr var(--title-height) var(--subtitle-height) var(--subsubtitle-height) 1fr;grid-template-areas:"." "title" "subtitle" "subsubtitle" "."}div#\:\$p>svg>foreignObject>section.title :is(h1,marp-h1),div#\:\$p>svg>foreignObject>section.title :is(h2,marp-h2),div#\:\$p>svg>foreignObject>section.title :is(h3,marp-h3){margin:0;padding:0;text-align:center;height:var(--area-height);line-height:var(--area-height);font-size:calc(var(--area-height) * 0.7);border:0px dashed gray /* debug */}div#\:\$p>svg>foreignObject>section.title :is(h1,marp-h1){grid-area:title;--area-height:var(--title-height)}div#\:\$p>svg>foreignObject>section.title :is(h2,marp-h2){grid-area:subtitle;--area-height:var(--subtitle-height)}div#\:\$p>svg>foreignObject>section.title :is(h3,marp-h3){grid-area:subsubtitle;text-align:center;--area-height:var(--subsubtitle-height)}div#\:\$p>svg>foreignObject>section.split-text{overflow:visible;display:grid;grid-template-columns:500px 500px;grid-template-rows:100px auto;grid-template-areas:"slideheading slideheading" "leftpanel rightpanel"} -/* debug */div#\:\$p>svg>foreignObject>section.split-text .ldiv,div#\:\$p>svg>foreignObject>section.split-text .rdiv,div#\:\$p>svg>foreignObject>section.split-text :is(h3,marp-h3){border:none}div#\:\$p>svg>foreignObject>section.split-text :is(h3,marp-h3){grid-area:slideheading;font-size:50px}div#\:\$p>svg>foreignObject>section.split-text .ldiv{grid-area:leftpanel;text-align:left}div#\:\$p>svg>foreignObject>section.split-text .rdiv{grid-area:rightpanel;text-align:left}div#\:\$p>svg>foreignObject>section.split-text-image{overflow:visible;display:grid;grid-template-columns:500px 500px;grid-template-rows:100px auto;grid-template-areas:"slideheading slideheading" "leftpanel rightpanel"} -/* debug */div#\:\$p>svg>foreignObject>section.split-text-image .ldiv,div#\:\$p>svg>foreignObject>section.split-text-image .rdiv,div#\:\$p>svg>foreignObject>section.split-text-image :is(h3,marp-h3){border:none}div#\:\$p>svg>foreignObject>section.split-text-image :is(h3,marp-h3){grid-area:slideheading;font-size:50px}div#\:\$p>svg>foreignObject>section.split-text-image .ldiv{grid-area:leftpanel;text-align:left}div#\:\$p>svg>foreignObject>section.split-text-image .rdiv{grid-area:rightpanel;text-align:right}div#\:\$p>svg>foreignObject>section.body-quote{margin:1em;font:calc(var(--marpit-root-font-size, 1rem) * 1.2)/1.4 Georgia,serif}div#\:\$p>svg>foreignObject>section.body-quote{margin:0;background:#eee;padding:1em;border-radius:1em}div#\:\$p>svg>foreignObject>section figcaption{position:right;bottom:0;padding:15px;color:black;background-color:#F5F5F5;text-align:right;font-size:calc(var(--marpit-root-font-size, 1rem) * 1);width:100%}div#\:\$p>svg>foreignObject>section div.twocols{margin-top:35px;column-count:2}div#\:\$p>svg>foreignObject>section div.twocols :is(h1,marp-h1):first-child,div#\:\$p>svg>foreignObject>section div.twocols :is(h2,marp-h2):first-child,div#\:\$p>svg>foreignObject>section div.twocols p:first-child,div#\:\$p>svg>foreignObject>section div.twocols ul li p:first-child,div#\:\$p>svg>foreignObject>section div.twocols ul li:first-child,div#\:\$p>svg>foreignObject>section div.twocols ul:first-child{margin-top:0!important}div#\:\$p>svg>foreignObject>section div.twocols p.break{break-before:column;margin-top:0} -/* @theme 9127mw0v1aa60ya31jxrvpved83w2s404ewrxe4u8p2 */div#\:\$p>svg>foreignObject>section[data-marpit-advanced-background=background]{columns:initial!important;display:block!important;padding:0!important}div#\:\$p>svg>foreignObject>section[data-marpit-advanced-background=background]:after,div#\:\$p>svg>foreignObject>section[data-marpit-advanced-background=background]:before,div#\:\$p>svg>foreignObject>section[data-marpit-advanced-background=content]:after,div#\:\$p>svg>foreignObject>section[data-marpit-advanced-background=content]:before{display:none!important}div#\:\$p>svg>foreignObject>section[data-marpit-advanced-background=background]>div[data-marpit-advanced-background-container]{all:initial;display:flex;flex-direction:row;height:100%;overflow:hidden;width:100%}div#\:\$p>svg>foreignObject>section[data-marpit-advanced-background=background]>div[data-marpit-advanced-background-container][data-marpit-advanced-background-direction=vertical]{flex-direction:column}div#\:\$p>svg>foreignObject>section[data-marpit-advanced-background=background][data-marpit-advanced-background-split]>div[data-marpit-advanced-background-container]{width:var(--marpit-advanced-background-split,50%)}div#\:\$p>svg>foreignObject>section[data-marpit-advanced-background=background][data-marpit-advanced-background-split=right]>div[data-marpit-advanced-background-container]{margin-left:calc(100% - var(--marpit-advanced-background-split, 50%))}div#\:\$p>svg>foreignObject>section[data-marpit-advanced-background=background]>div[data-marpit-advanced-background-container]>figure{all:initial;background-position:center;background-repeat:no-repeat;background-size:cover;flex:auto;margin:0}div#\:\$p>svg>foreignObject>section[data-marpit-advanced-background=content],div#\:\$p>svg>foreignObject>section[data-marpit-advanced-background=pseudo]{background:transparent!important}div#\:\$p>svg>foreignObject>section[data-marpit-advanced-background=pseudo],div#\:\$p>svg[data-marpit-svg]>foreignObject[data-marpit-advanced-background=pseudo]{pointer-events:none!important}div#\:\$p>svg>foreignObject>section[data-marpit-advanced-background-split]{width:100%;height:100%}
+/* ...and layout freely :) */div#\:\$p>svg>foreignObject>section table{width:100%}div#\:\$p>svg>foreignObject>section td{text-align:center;vertical-align:middle}div#\:\$p>svg>foreignObject>section td,div#\:\$p>svg>foreignObject>section th{display:table-cell}div#\:\$p>svg>foreignObject>section ul{list-style-type:none;margin-left:0.2px}div#\:\$p>svg>foreignObject>section li:before{content:"";background-image:url(./figures/logo/NGI/NGI_pil_RGB_dark_grey_medium.png);width:15px;height:15px;display:inline-block;background-size:contain;margin-right:calc(var(--marpit-root-font-size, 1rem) * 0.25)}div#\:\$p>svg>foreignObject>section :is(h2,marp-h2){color:gray}div#\:\$p>svg>foreignObject>section img{background-color:transparent!important;box-sizing:initial;max-width:100%}div#\:\$p>svg>foreignObject>section figure{border:thin #c0c0c0 solid;display:flex;flex-flow:column;padding:5px;max-width:220px;margin:auto}div#\:\$p>svg>foreignObject>section.title{--title-height:130px;--subtitle-height:70px;--subsubtitle-height:50px;overflow:visible;display:grid;grid-template-columns:1fr;grid-template-rows:1fr var(--title-height) var(--subtitle-height) var(--subsubtitle-height) 1fr;grid-template-areas:"." "title" "subtitle" "subsubtitle" "."}div#\:\$p>svg>foreignObject>section.title :is(h1,marp-h1),div#\:\$p>svg>foreignObject>section.title :is(h2,marp-h2),div#\:\$p>svg>foreignObject>section.title :is(h3,marp-h3){margin:0;padding:0;text-align:center;height:var(--area-height);line-height:var(--area-height);font-size:calc(var(--area-height) * 0.7);border:0px dashed gray + /* debug */}div#\:\$p>svg>foreignObject>section.title :is(h1,marp-h1){grid-area:title;--area-height:var(--title-height)}div#\:\$p>svg>foreignObject>section.title :is(h2,marp-h2){grid-area:subtitle;--area-height:var(--subtitle-height)}div#\:\$p>svg>foreignObject>section.title :is(h3,marp-h3){grid-area:subsubtitle;text-align:center;--area-height:var(--subsubtitle-height)}div#\:\$p>svg>foreignObject>section.split-text{overflow:visible;display:grid;grid-template-columns:500px 500px;grid-template-rows:100px auto;grid-template-areas:"slideheading slideheading" "leftpanel rightpanel"} + +/* debug */div#\:\$p>svg>foreignObject>section.split-text .ldiv,div#\:\$p>svg>foreignObject>section.split-text .rdiv,div#\:\$p>svg>foreignObject>section.split-text :is(h3,marp-h3){border:none}div#\:\$p>svg>foreignObject>section.split-text :is(h3,marp-h3){grid-area:slideheading;font-size:50px}div#\:\$p>svg>foreignObject>section.split-text .ldiv{grid-area:leftpanel;text-align:left}div#\:\$p>svg>foreignObject>section.split-text .rdiv{grid-area:rightpanel;text-align:left}div#\:\$p>svg>foreignObject>section.split-text-image{overflow:visible;display:grid;grid-template-columns:1fr 1fr; 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24.10.2024

Optimize your curves,

@@ -32,11 +64,15 @@

Oslo Python MeetUp

Sunniva Indrehus

-
echo $(whoami)
+

+
📈 + 💪 + 🐍 = ❓

+

+

+
echo $(whoami)
 

 

 

-

+

 
Agenda

 
#TODO update content in the end

 
Curve Fitting

@@ -55,15 +91,23 @@ 
Curve Fitting

 
 

 

-

+

 
Problem

-
right

-
    
-
  • What are the optimal model parameters for my data?
    • 
-

+
+
+
+
+
+
+
+
+
+
+
+
Which model parameters describe this data?

 

 

-

+

 

 
Optimization: an act, process, or methodology of making something (such as a design, system, or decision) as fully perfect, functional, or effective as possible

 specifically : the mathematical procedures (such as finding the maximum of a function) involved in this

@@ -74,7 +118,7 @@ 
Problem

 

 

 

-

+

 
What is Curve Fitting?

 
    
 
  • Identify the best-fit curve for a dataset with a known model
@@ -85,98 +129,64 @@ 
    What is Curve Fitting?
    
 
  • Categories of problems
 
      
 
    • Linear vs non-linear
      • 
-
    • Constrained vs unconstrained
      • 
+
    • Unconstrained vs Constrained
      • 
 
    
 
    • 
 

 

 

-

-
Difference of observed and predicted values

-

-

-

-

-

+

+
Difference between observed and predicted values

+

+

+

+

+

+

 

 

 

-

-
Curve fitting vs. model optimization

+

+
The real world is complicated

 
-
-
-
+
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
+

 
 
AspectCurve FittingModel Optimization
 

 
 
 
ScopeSpecific; find a curve that fits dataBroad; enhance model performance in various contexts
ApplicationsScientific, engineering, and data analysisMachine learning, operations research, statistics, etc.
TechniquesPrimarily uses least squares regression, polynomial fitting, non-linear fittingIncludes hyperparameter tuning, model selection, regularization, etc.
GoalModel relationships between variables through a curveImprove performance across various model types
 

 
 

 

 

-

-
The complicated real world is non-linear

-

-

-

-

+

 
Real (NGI work) life example

 

-

+

 


-Monopile system

+Offshore structure

 

-

+

 


 Horizontal displacement curves under load

 

 

 

-

-
📈 + 💪 + 🐍 = ❓

-

-

-

-
📈 + 💪 + 🐍 = ❤️

-

-

-

-
Curve fitting in Python needs

-
    
-
  • Estimate of uncertainties and parameter correlations estimates
    • 
-
  • Non-linear problem formulation
    • 
-
  • Easy change of the fitting algorithm
+
    
+
    Curve fitting requirements
    
 
      
-
    • Functionality for handeling parameter bounds
      • 
-
    
-
+
  • Provide estimates of parameter uncertanties
    • 
+
  • Handle non-linear problem formulations
    • 
+
  • Easy changebility of the optimization algorithm
    • 
+
  • High level functionality for handling parameter bounds
    • 
 
 
    
 
    
-
    
-
    Recommended tool
    
+
    
+
    Recommended tool (by me)
    
 
@@ -209,65 +219,90 @@ 
    Recommended tool
    
 
 
    
 
    
 
    
 
    
-
    
-
    Tiling can improve the access pattern
    
-
    
-
    Left
    
-
    
-
-
    
+
    
+
    ✨ Let's see some code ✨ 
    
 
    
 
    
-
    
-
    Demo: Curve Fitting with lmfit**
    
-
    Step 1: Problem Setup
    
-
    import numpy as np
-import matplotlib.pyplot as plt
-from lmfit import Model
+
    
+
    Problem set up
    
+
    from lmfit import Minimizer, create_params
+
+def residual(parameters, x, data):
+    model = (
+        parameters["amp"]
+        * np.sin(x * parameters["omega"] + parameters["shift"])
+        * np.exp(-x * x * parameters["decay"])
+    )
+    return model - data
 
-def model_function(x, a, b, c):
-    return a * np.exp(-b * x) + c
+parameters_initial = create_params(
+    amp=10,
+    decay=0.1,
+    omega=3.0,
+    shift=value=0.0,
+)
 
-x_data = np.linspace(0, 10, 100)
-y_true = model_function(x_data, a=3, b=1, c=0.5)
-noise = np.random.normal(0, 0.2, x_data.shape)
-y_data = y_true + noise
+minimizer = Minimizer(residual, parameters_initial, fcn_args=(x, data))
+result = minimizer.minimize(method="least_squares")
 
-plt.scatter(x_data, y_data, label='Noisy Data')
-plt.plot(x_data, y_true, color='red', label='True Model')
-plt.legend()
-plt.show()
 
    
 
    
 
    
-
    
-
    Interpreting the Results
    
-
      
-
    • Parameter Estimates: Best-fit values for a, b, and c
      • 
-
    • Fit Report: Provides uncertainties and goodness-of-fit statistics
      • 
-
    • Visualization: Compare the fitted model to the noisy data
      • 
-
    • Adding Constraints:
-
        
-
      • Constrain parameters (e.g., a > 0)
        • 
-
      
-
      • 
-
    • Fitting Different Models:
-
        
-
      • Experiment with other functions (e.g., polynomials, sine)
        • 
-
      
-
      • 
-
    • Advanced Features:
-
        
-
      • Handle multiple datasets, parameter bounds, and more
        • 
-
      
-
      • 
-
    
+
    
+
    Handeling bounds
    
+
    from lmfit import Parameters 
+
+params = Parameters()
+params.add('amp', value=10, min=0)
+params.add('decay', value=0.1)
+params.add('shift', value=0.0, min=-np.pi/2., max=np.pi/2.)
+params.add('omega', value=3.0)
+
+
    
+
    from lmfit import create_params
+
+params = create_params(amp=dict(value=10, min=0),
+                       decay=0.1,
+                       omega=3,
+                       shift=dict(value=0, min=-np.pi/2, max=np.pi/2))
+
    
 
    
 
    
-
    
-
    ✨ 👩‍💻 Let's see some code 👩‍💻 ✨ 
    
+
    
+
    Interperet simulation results
    
+
    [[Fit Statistics]]
+    # fitting method   = least_squares
+    # function evals   = 58
+    # data points      = 301
+    # variables        = 4
+    chi-square         = 12.1867036
+    reduced chi-square = 0.04103267
+    Akaike info crit   = -957.236198
+    Bayesian info crit = -942.407756
+[[Variables]]
+    amp:    5.03088066 +/- 0.04005821 (0.80%) (init = 10)
+    decay:  0.02495457 +/- 4.5396e-04 (1.82%) (init = 0.1)
+    omega:  2.00026311 +/- 0.00326183 (0.16%) (init = 3)
+    shift: -0.10264955 +/- 0.01022294 (9.96%) (init = 0)
+[[Correlations]] (unreported correlations are < 0.100)
+    C(omega, shift) = -0.7852
+    C(amp, decay)   = +0.5840
+    C(amp, shift)   = -0.1179
+
    
+
    
+
    
+
    
+
    👩‍💻 Demo 👩‍💻  
    
+
    
+
    
+
    
+
    📈 + 💪 + 🐍 = ❓
    
+
    
+
    
+
    
+
    📈 + 💪 + 🐍 = ❤️
    
 
    
 
    
 
    - What are the optimal model parameters for my data?
    
\ No newline at end of file
diff --git a/talk/slides.md b/talk/slides.md
index 7d16413..a19556a 100755
--- a/talk/slides.md
+++ b/talk/slides.md
@@ -18,6 +18,12 @@ footer: '![width:90 height:40](figures/logo/NGI/NGI_logo_transparent.gif)'
 #### Sunniva Indrehus
 
 
+--- 
+
+
+
+# :chart_with_upwards_trend: + :muscle: + :snake: = :question: 
+
 --- 
 
 
@@ -44,10 +50,14 @@ echo $(whoami)
 --- 
 # Problem
 
-![h:500 right](figures/plot/observed_low.png)
+|![h:500](figures/plot/observed_low.png)|
+|:--:|
+| Which model parameters describe this data? |
+
 
 
-- What are the optimal model parameters for my data? 
+
+
 
 ---
 
@@ -73,7 +83,7 @@ specifically : the mathematical procedures (such as finding the maximum of a fun
       $$ 
       y_1 = ax + b \qquad \text{vs} \quad y_2 = \sin(\omega t)\text{e}^{-x^2}
       $$
-    * Constrained vs unconstrained
+    * Unconstrained vs Constrained
       $$
         a\in [-\infty,\infty] \quad \text{vs} \quad a \in [-\pi,\pi/3]
       $$
@@ -82,42 +92,36 @@ specifically : the mathematical procedures (such as finding the maximum of a fun
 
 ---
 
-# Difference of *observed* and *predicted* values 
+# Difference between *observed* and *predicted* values 
 
-
 
-
    
 
+
    
 
-![](figures/plot/observed_vs_predicted_low.png)
 
+##
 
-
    
+![w:600 h:300](figures/plot/observed_vs_predicted_low.png)
 
 
-
    
 
-![](figures/plot/chi_square_plot_low.png)
+
    
 
-
    
+###
+![w:600 h:300](figures/plot/chi_square_plot_low.png)
 
----
+
    
 
-# Curve fitting vs. model optimization 
 
-| Aspect              | Curve Fitting                                   | Model Optimization                              |
-|:---------------------|:------------------------------------------------|:------------------------------------------------|
-| **Scope**           | Specific; find a curve that fits data | Broad; enhance model performance in various contexts |
-| **Applications**     | Scientific, engineering, and data analysis | Machine learning, operations research, statistics, etc. |
-| **Techniques**      | Primarily uses least squares regression, polynomial fitting, non-linear fitting | Includes hyperparameter tuning, model selection, regularization, etc. |
-| **Goal**            | Model relationships between variables through a curve | Improve performance across various model types |
 
 
 ---
 
-# The complicated real world is non-linear 
+# The real world is complicated
 
-![](figures/plot/observed_sin.png)
+| |
+|-----|
+|![](figures/plot/parameters_values_lmfit.png)|
  
 --- 
 
@@ -128,7 +132,7 @@ specifically : the mathematical procedures (such as finding the maximum of a fun
 
 ##
 ![w:450 h:350](figures/illustrations/full_model.png)
-Monopile system 
+Offshore structure
 
 
 
@@ -141,32 +145,22 @@ Horizontal displacement curves under load
 
 
 
----
-
-
-# :chart_with_upwards_trend: + :muscle: + :snake: = :question: 
-
---- 
-
-
-# :chart_with_upwards_trend: + :muscle: + :snake: = :heart: 
-
 --- 
 
 
 
 
-# Curve fitting in Python needs
-  - Estimate of uncertainties and parameter correlations estimates
-  - Non-linear problem formulation
-  - Easy change of the fitting algorithm 
-    - Functionality for handeling parameter [bounds](https://lmfit.github.io/lmfit-py/bounds.html)
+# Curve fitting requirements 
+  - Provide estimates of parameter uncertanties
+  - Handle non-linear problem formulations
+  - Easy changebility of the optimization algorithm 
+  - High level functionality for handling parameter [bounds](https://lmfit.github.io/lmfit-py/bounds.html)
   
 
 
 --- 
 
-# Recommended tool 
+# Recommended tool (by me) 
 
 
 | **Topic**                        | **Details**                                                                         |
@@ -180,63 +174,100 @@ Horizontal displacement curves under load
 ---
 
 
-# Tiling can improve the access pattern
 
-
    
+# 
    :sparkles: Let's see some code :sparkles: 
    
 
+---
 
-## Left 
 
-
    
+# Problem set up
 
+```python
+from lmfit import Minimizer, create_params
+
+def residual(parameters, x, data):
+    model = (
+        parameters["amp"]
+        * np.sin(x * parameters["omega"] + parameters["shift"])
+        * np.exp(-x * x * parameters["decay"])
+    )
+    return model - data
+
+parameters_initial = create_params(
+    amp=10,
+    decay=0.1,
+    omega=3.0,
+    shift=value=0.0,
+)
+
+minimizer = Minimizer(residual, parameters_initial, fcn_args=(x, data))
+result = minimizer.minimize(method="least_squares")
 
-## Right 
-
    
+```
 
+--- 
 
----
+# Handeling bounds 
+
+```python
+from lmfit import Parameters 
 
-# Demo: Curve Fitting with `lmfit`**
+params = Parameters()
+params.add('amp', value=10, min=0)
+params.add('decay', value=0.1)
+params.add('shift', value=0.0, min=-np.pi/2., max=np.pi/2.)
+params.add('omega', value=3.0)
 
-### Step 1: Problem Setup
+```
 
 ```python
-import numpy as np
-import matplotlib.pyplot as plt
-from lmfit import Model
-
-def model_function(x, a, b, c):
-    return a * np.exp(-b * x) + c
-
-x_data = np.linspace(0, 10, 100)
-y_true = model_function(x_data, a=3, b=1, c=0.5)
-noise = np.random.normal(0, 0.2, x_data.shape)
-y_data = y_true + noise
-
-plt.scatter(x_data, y_data, label='Noisy Data')
-plt.plot(x_data, y_true, color='red', label='True Model')
-plt.legend()
-plt.show()
+from lmfit import create_params
+
+params = create_params(amp=dict(value=10, min=0),
+                       decay=0.1,
+                       omega=3,
+                       shift=dict(value=0, min=-np.pi/2, max=np.pi/2))
 ```
 
+
 ---
 
-# Interpreting the Results
 
-- **Parameter Estimates**: Best-fit values for `a`, `b`, and `c`
-- **Fit Report**: Provides uncertainties and goodness-of-fit statistics
-- **Visualization**: Compare the fitted model to the noisy data
-- **Adding Constraints**:
-  - Constrain parameters (e.g., `a > 0`)
-- **Fitting Different Models**:
-  - Experiment with other functions (e.g., polynomials, sine)
-- **Advanced Features**:
-  - Handle multiple datasets, parameter bounds, and more
+# Interperet simulation results
+
+```bash
+[[Fit Statistics]]
+    # fitting method   = least_squares
+    # function evals   = 58
+    # data points      = 301
+    # variables        = 4
+    chi-square         = 12.1867036
+    reduced chi-square = 0.04103267
+    Akaike info crit   = -957.236198
+    Bayesian info crit = -942.407756
+[[Variables]]
+    amp:    5.03088066 +/- 0.04005821 (0.80%) (init = 10)
+    decay:  0.02495457 +/- 4.5396e-04 (1.82%) (init = 0.1)
+    omega:  2.00026311 +/- 0.00326183 (0.16%) (init = 3)
+    shift: -0.10264955 +/- 0.01022294 (9.96%) (init = 0)
+[[Correlations]] (unreported correlations are < 0.100)
+    C(omega, shift) = -0.7852
+    C(amp, decay)   = +0.5840
+    C(amp, shift)   = -0.1179
+```
+---
+
+
+# 
    :woman_technologist: Demo :woman_technologist:  
    
 
 ---
-
 
-# 
    :sparkles: :woman_technologist: Let's see some code :woman_technologist: :sparkles: 
    
+
+
+# :chart_with_upwards_trend: + :muscle: + :snake: = :question: 
+
+--- 
 
+
+
+# :chart_with_upwards_trend: + :muscle: + :snake: = :heart: