From d9f9e1df181577a7ecc4e5c758a472023476a81f Mon Sep 17 00:00:00 2001
From: Adam Fekete <adam@fekete.co.uk>
Date: Wed, 25 Oct 2023 09:22:39 +0200
Subject: [PATCH] backup
---
README.md | 28 +-
periodictable.ipynb | 238 ++++
properties.ipynb | 1 +
testing.ipynb | 244 ++++
utils/matminer/matminer.ipynb | 4 +-
utils/webelements/atomic_properties_webele.py | 763 ++++++++++
utils/webelements/webelements.ipynb | 1261 +++++++++++++++++
utils/webelements/webelements_scrape.ipynb | 227 +++
8 files changed, 2750 insertions(+), 16 deletions(-)
create mode 100644 periodictable.ipynb
create mode 100644 properties.ipynb
create mode 100644 testing.ipynb
create mode 100644 utils/webelements/atomic_properties_webele.py
create mode 100644 utils/webelements/webelements.ipynb
create mode 100644 utils/webelements/webelements_scrape.ipynb
diff --git a/README.md b/README.md
index 5eb341c..0689a15 100644
--- a/README.md
+++ b/README.md
@@ -40,12 +40,12 @@ Currently data evaluated are available from following functionals 'HSE06', 'REVP
Spin Setting can be set to 'TRUE' or 'FALSE'.
-One can now acess individually different properties using element symbol as following
+One can now access individually different properties using element symbol as following
```
- dft.C.atomic_ea
- dft.C.atomic_r_p
- dft.C.atomic_ip_by_half_charged_homo
+ dft['C'].atomic_ea
+ dft['C'].atomic_r_p
+ dft['C'].atomic_ip_by_half_charged_homo
```
To get a list of properties accessible one can just press Tab after typing in `dft.C.`
@@ -55,7 +55,7 @@ Use symbol method from atomic_properties_dft module to acess property of interes
```
atomic_number = []
for i in ls:
- atomic_number.append(dft.symbol(i).atomic_number)
+ atomic_number.append(dft['C'].atomic_number)
```
**-------------------------------------------------------------------------------------------------------------------------------------------------------**
@@ -68,9 +68,9 @@ This module contains several atomic features accessible from pymatgen
One can now acess individually different properties using element symbol as following
```
- pymat.C.atomic_ea
- pymat.C.atomic_r_p
- pymat.C.atomic_ip_by_half_charged_homo
+ pymat['C'].atomic_ea
+ pymat['C'].atomic_r_p
+ pymat['C'].atomic_ip_by_half_charged_homo
```
To get a list of properties accessible one can just press Tab after typing in `pymat.C.`
@@ -85,10 +85,10 @@ This module contains several atomic features accessible from lda2015 paper
One can acess individually different properties using element symbol as following
```
- lda.C.atomic_ea
- lda.C.atomic_r_p
+ lda['C'].atomic_ea
+ lda['C'].atomic_r_p
```
-To get a list of properties accessible one can just press Tab after typing in `lda.C.`
+To get a list of properties accessible one can just press Tab after typing in `lda['C'].`
Use symbol method from atomic_properties_lda2015 module to acess property of interest of elements at once in similar manner as described for atomic_properties_dft module above.
@@ -101,10 +101,10 @@ This module contains several atomic features accessible from Webelements website
One can acess individually different properties using element symbol as following
```
- webele.C.atomic_radius
- webele.C.melting_point
+ webele['C'].atomic_radius
+ webele['C'].melting_point
```
-To get a list of properties accessible one can just press Tab after typing in `webele.C.`
+To get a list of properties accessible one can just press Tab after typing in `webele.'C'].`
Use symbol method from atomic_properties_webele module to acess property of interest of elements at once in similar manner as described for atomic_properties_dft module above.
diff --git a/periodictable.ipynb b/periodictable.ipynb
new file mode 100644
index 0000000..e677349
--- /dev/null
+++ b/periodictable.ipynb
@@ -0,0 +1,238 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from atomic_features_package.legacy.atomic_properties import periodictable"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ "application/vnd.bokehjs_load.v0+json": ""
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": "(function(root) {\n function embed_document(root) {\n const docs_json = {\"0c52b29d-0506-4f0e-bb71-c46e0abecb54\":{\"version\":\"3.2.2\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p1004\",\"attributes\":{\"width\":950,\"height\":500,\"x_range\":{\"type\":\"object\",\"name\":\"FactorRange\",\"id\":\"p1014\",\"attributes\":{\"factors\":[\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\"]}},\"y_range\":{\"type\":\"object\",\"name\":\"FactorRange\",\"id\":\"p1015\",\"attributes\":{\"factors\":[\"AC\",\"LC\",\" \",\"VII\",\"VI\",\"V\",\"IV\",\"III\",\"II\",\"I\"]}},\"x_scale\":{\"type\":\"object\",\"name\":\"CategoricalScale\",\"id\":\"p1016\"},\"y_scale\":{\"type\":\"object\",\"name\":\"CategoricalScale\",\"id\":\"p1017\"},\"title\":{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p1007\",\"attributes\":{\"text\":\"Periodic 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+ "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\nconst JS_MIME_TYPE = 'application/javascript';\n const HTML_MIME_TYPE = 'text/html';\n const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n const CLASS_NAME = 'output_bokeh rendered_html';\n\n /**\n * Render data to the DOM node\n */\n function render(props, node) {\n const script = document.createElement(\"script\");\n node.appendChild(script);\n }\n\n /**\n * Handle when an output is cleared or removed\n */\n function handleClearOutput(event, handle) {\n const cell = handle.cell;\n\n const id = cell.output_area._bokeh_element_id;\n const server_id = cell.output_area._bokeh_server_id;\n // Clean up Bokeh references\n if (id != null && id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n\n if (server_id !== undefined) {\n // Clean up Bokeh references\n const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n cell.notebook.kernel.execute(cmd_clean, {\n iopub: {\n output: function(msg) {\n const id = msg.content.text.trim();\n if (id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n }\n }\n });\n // Destroy server and session\n const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n cell.notebook.kernel.execute(cmd_destroy);\n }\n }\n\n /**\n * Handle when a new output is added\n */\n function handleAddOutput(event, handle) {\n const output_area = handle.output_area;\n const output = handle.output;\n\n // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n return\n }\n\n const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n const bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n const script_attrs = bk_div.children[0].attributes;\n for (let i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n }\n\n function register_renderer(events, OutputArea) {\n\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n const toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[toinsert.length - 1]);\n element.append(toinsert);\n return toinsert\n }\n\n /* Handle when an output is cleared or removed */\n events.on('clear_output.CodeCell', handleClearOutput);\n events.on('delete.Cell', handleClearOutput);\n\n /* Handle when a new output is added */\n events.on('output_added.OutputArea', handleAddOutput);\n\n /**\n * Register the mime type and append_mime function with output_area\n */\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n /* Is output safe? */\n safe: true,\n /* Index of renderer in `output_area.display_order` */\n index: 0\n });\n }\n\n // register the mime type if in Jupyter Notebook environment and previously unregistered\n if (root.Jupyter !== undefined) {\n const events = require('base/js/events');\n const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n }\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"<div style='background-color: #fdd'>\\n\"+\n \"<p>\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"</p>\\n\"+\n \"<ul>\\n\"+\n \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n \"<li>use INLINE resources instead, as so:</li>\\n\"+\n \"</ul>\\n\"+\n \"<code>\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"</code>\\n\"+\n \"</div>\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.2.2.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));",
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+ },
+ "metadata": {},
+ "output_type": "display_data"
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If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"</p>\\n\"+\n \"<ul>\\n\"+\n \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n \"<li>use INLINE resources instead, as so:</li>\\n\"+\n \"</ul>\\n\"+\n \"<code>\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"</code>\\n\"+\n \"</div>\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.2.2.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));",
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+ },
+ "metadata": {},
+ "output_type": "display_data"
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+ "source": [
+ "periodictable.heatmap(Spin = 'False', method = 'hse06')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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const render_items = [{\"docid\":\"c4ff6ec5-aff5-4371-869a-a2fb5d3ed80e\",\"roots\":{\"p1307\":\"f48729b2-a0a2-4714-a1c1-29ca0660ce8e\"},\"root_ids\":[\"p1307\"]}];\n root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n }\n if (root.Bokeh !== undefined) {\n embed_document(root);\n } else {\n let attempts = 0;\n const timer = setInterval(function(root) {\n if (root.Bokeh !== undefined) {\n clearInterval(timer);\n embed_document(root);\n } else {\n attempts++;\n if (attempts > 100) {\n clearInterval(timer);\n console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n }\n }\n }, 10, root)\n }\n})(window);",
+ "application/vnd.bokehjs_exec.v0+json": ""
+ },
+ "metadata": {
+ "application/vnd.bokehjs_exec.v0+json": {
+ "id": "p1307"
+ }
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from bokeh.plotting import figure, show\n",
+ "from bokeh.sampledata.periodic_table import elements\n",
+ "from bokeh.transform import dodge, factor_cmap\n",
+ "\n",
+ "periods = [\"I\", \"II\", \"III\", \"IV\", \"V\", \"VI\", \"VII\"]\n",
+ "groups = [str(x) for x in range(1, 19)]\n",
+ "\n",
+ "df = elements.copy()\n",
+ "df[\"atomic mass\"] = df[\"atomic mass\"].astype(str)\n",
+ "df[\"group\"] = df[\"group\"].astype(str)\n",
+ "df[\"period\"] = [periods[x-1] for x in df.period]\n",
+ "df = df[df.group != \"-\"]\n",
+ "df = df[df.symbol != \"Lr\"]\n",
+ "df = df[df.symbol != \"Lu\"]\n",
+ "\n",
+ "cmap = {\n",
+ " \"alkali metal\" : \"#a6cee3\",\n",
+ " \"alkaline earth metal\" : \"#1f78b4\",\n",
+ " \"metal\" : \"#d93b43\",\n",
+ " \"halogen\" : \"#999d9a\",\n",
+ " \"metalloid\" : \"#e08d49\",\n",
+ " \"noble gas\" : \"#eaeaea\",\n",
+ " \"nonmetal\" : \"#f1d4Af\",\n",
+ " \"transition metal\" : \"#599d7A\",\n",
+ "}\n",
+ "\n",
+ "TOOLTIPS = [\n",
+ " (\"Name\", \"@name\"),\n",
+ " (\"Atomic number\", \"@{atomic number}\"),\n",
+ " (\"Atomic mass\", \"@{atomic mass}\"),\n",
+ " (\"Type\", \"@metal\"),\n",
+ " (\"CPK color\", \"$color[hex, swatch]:CPK\"),\n",
+ " (\"Electronic configuration\", \"@{electronic configuration}\"),\n",
+ "]\n",
+ "\n",
+ "p = figure(title=\"Periodic Table (omitting LA and AC Series)\", width=1000, height=450,\n",
+ " x_range=groups, y_range=list(reversed(periods)),\n",
+ " tools=\"hover\", toolbar_location=None, tooltips=TOOLTIPS)\n",
+ "\n",
+ "r = p.rect(\"group\", \"period\", 0.95, 0.95, source=df, fill_alpha=0.6, legend_field=\"metal\",\n",
+ " color=factor_cmap('metal', palette=list(cmap.values()), factors=list(cmap.keys())))\n",
+ "\n",
+ "text_props = dict(source=df, text_align=\"left\", text_baseline=\"middle\")\n",
+ "\n",
+ "x = dodge(\"group\", -0.4, range=p.x_range)\n",
+ "\n",
+ "p.text(x=x, y=\"period\", text=\"symbol\", text_font_style=\"bold\", **text_props)\n",
+ "\n",
+ "p.text(x=x, y=dodge(\"period\", 0.3, range=p.y_range), text=\"atomic number\",\n",
+ " text_font_size=\"11px\", **text_props)\n",
+ "\n",
+ "p.text(x=x, y=dodge(\"period\", -0.35, range=p.y_range), text=\"name\",\n",
+ " text_font_size=\"7px\", **text_props)\n",
+ "\n",
+ "p.text(x=x, y=dodge(\"period\", -0.2, range=p.y_range), text=\"atomic mass\",\n",
+ " text_font_size=\"7px\", **text_props)\n",
+ "\n",
+ "p.text(x=[\"3\", \"3\"], y=[\"VI\", \"VII\"], text=[\"LA\", \"AC\"], text_align=\"center\", text_baseline=\"middle\")\n",
+ "\n",
+ "p.outline_line_color = None\n",
+ "p.grid.grid_line_color = None\n",
+ "p.axis.axis_line_color = None\n",
+ "p.axis.major_tick_line_color = None\n",
+ "p.axis.major_label_standoff = 0\n",
+ "p.legend.orientation = \"horizontal\"\n",
+ "p.legend.location =\"top_center\"\n",
+ "p.hover.renderers = [r] # only hover element boxes\n",
+ "\n",
+ "show(p)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": ".venv",
+ "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": "3.9.16"
+ },
+ "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/properties.ipynb b/properties.ipynb
new file mode 100644
index 0000000..f97c9fc
--- /dev/null
+++ b/properties.ipynb
@@ -0,0 +1 @@
+{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from atomic_features_package import datasets\n","\n","\n","db = datasets.load(name='qm9')\n","\n","db.describe()\n","\n","db['C'].atomic_size # return value\n","\n","elements = ['C', 'O', 'N', 'F', 'H']\n","table = db.get_table(elements, ['atomic_size'])\n","\n","for element in elements:\n"," ...\n","\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from atomic_features_package.category import root\n","\n","root.find_property('atomic_size') # search for slug/property name or description\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from atomic_features_package.property.atomic_properties.physical_poperties import atomic_size\n","\n","print(atomic_size.describe())\n","dbs = atomic_size.list_dataset()\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# dataset needs to be registered dataset object\n","# a dataset's metadata needs to be registered into the poperty\n","# property needs to be registered into category and tags\n","\n","# only properties of already imported datasets can be seen (plugin mechanism?)\n","# lazy load: loading the dataset's metadata not the dataset itself"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["package\n","- multiple datasets (separate metadata)\n","p"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"code","execution_count":22,"metadata":{},"outputs":[],"source":["from typing import Dict, Generic, TypeVar\n","\n","T = TypeVar(\"T\")"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["a=123 sibling=None\n"]}],"source":["from __future__ import annotations\n","\n","from pydantic import BaseModel\n","\n","\n","class Foo(BaseModel):\n"," a: int = 123\n"," #: The sibling of `Foo` is referenced directly by type\n"," sibling: Foo = None\n","\n","\n","print(Foo())"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[],"source":["x = Foo()\n","x.a = 1.654654\n"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[{"data":{"text/plain":["Foo(a=1.654654, sibling=None)"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["x"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["## Properties\n","\n","\n","## Taxonomies:\n","\n","Fetures:\n","- name\n","- slug\n","- descrition\n","\n","- hierarchical: category\n","- not hierachical: tags\n","\n","behind the schene\n","register a new taxonomy\n","crete relationship database\n","\n"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[{"data":{"text/plain":["{'1', '2'}"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["set(['1','2'])"]},{"cell_type":"code","execution_count":36,"metadata":{},"outputs":[],"source":["# ?? slug vs name of the object\n","# https://github.com/fekad/optimade-properties\n","# https://github.com/fekad/optimade-prototype\n","# https://github.com/fekad/optimade-notes\n","\n","# Metaclasses\n","class Taxonomy:\n"," pass\n","\n","class Term(BaseModel):\n"," name: str\n"," slug: str = None\n"," description: str = None\n","\n","\n","class HierarchicalTaxonomy(Taxonomy, Term):\n"," # childs: dict[str, HierarchicalTaxonomy] = None\n"," childs: list[HierarchicalTaxonomy] = None\n"," # childs: dict[slug, HierarchicalTaxonomy] = None\n"," # parent: HierarchicalTaxonomy = None\n","\n"," # def __getitem__(self, key: str) -> HierarchicalTaxonomy:\n"," # return self.childs[key]\n","\n","\n","class NonHierarchicalTaxonomy(Taxonomy, Term):\n"," terms: set[Term] = None\n","\n"," def __getitem__(self, key: str) -> Term:\n"," if key not in self.terms:\n"," raise KeyError(f\"{key} not in {self.name}\")\n"," return key\n","\n","# TODO parametric type\n","class Category(HierarchicalTaxonomy):\n"," pass\n","\n","# TODO parametric type\n","class Tags(NonHierarchicalTaxonomy):\n"," pass\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["root = Category(\n"," name=\"Propeties\",\n"," description=\"Category description\",\n",")\n","\n","atomic_properties=Category(\n"," name=\"Atomic properties\",\n"," description=\"Atomic Properties\",\n"," slug=\"atomic_properties\",\n"," parent=root,\n",")\n","\n","abundances = Category(\n"," name=\"Abundances\",\n"," slug=\"abundances\",\n"," parent=atomic_properties\n",")\n","atom_sizes = Category(\n"," name=\"Atom sizes\",\n"," slug=\"atom_sizes\",\n"," parent=atomic_properties\n",")\n","crystal_structure = Category(\n"," name=\"Crystal structure\",\n"," slug=\"crystal_structure\",\n"," parent=atomic_properties\n",")\n","electronegativities = Category(\n"," name=\"Electronegativities\",\n"," slug=\"electronegativities\",\n"," parent=atomic_properties\n",")\n","heat_properties = Category(\n"," name=\"Heat properties\",\n"," slug=\"heat_properties\",\n"," parent=atomic_properties\n",")\n","isotopes_and_nmr = Category(\n"," name=\"Isotopes and NMR\",\n"," slug=\"isotopes_and_nmr\",\n"," parent=atomic_properties\n",")\n","orbital_properties = Category(\n"," name=\"Orbital properties\",\n"," slug=\"orbital_properties\",\n"," parent=atomic_properties\n",")\n","physical_properties = Category(\n"," name=\"Physical properties\",\n"," slug=\"physical_properties\",\n"," parent=atomic_properties\n",")\n","\n","\n","print(root.json(indent=2, exclude_none=True))\n","\n","\n","# usage:\n","# from atomic_features_package.categories import root\n","# print(root['atomic_properties']['atom_sizes'])\n","\n","# from atomic_features_package.categories import atom_sizes\n","# print(atom_sizes)\n","\n","# pros:\n","# - it can be more compact\n"]},{"cell_type":"code","execution_count":37,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["{\n"," \"name\": \"Propeties\",\n"," \"description\": \"Category description\",\n"," \"childs\": [\n"," {\n"," \"name\": \"Atomic properties\",\n"," \"slug\": \"atomic_properties\",\n"," \"description\": \"Atomic Properties\",\n"," \"childs\": [\n"," {\n"," \"name\": \"Abundances\"\n"," },\n"," {\n"," \"name\": \"Atom sizes\"\n"," },\n"," {\n"," \"name\": \"Atom sizes\"\n"," },\n"," {\n"," \"name\": \"Crystal structure\"\n"," },\n"," {\n"," \"name\": \"Electronegativities\"\n"," },\n"," {\n"," \"name\": \"Heat properties\"\n"," },\n"," {\n"," \"name\": \"Isotopes and NMR\"\n"," },\n"," {\n"," \"name\": \"Orbital properties\"\n"," },\n"," {\n"," \"name\": \"Physical properties\"\n"," }\n"," ]\n"," }\n"," ]\n","}\n"]}],"source":["# Instnatiation\n","\n","root = Category(\n"," name=\"Propeties\",\n"," description=\"Category description\",\n"," childs=[\n"," Category(\n"," name=\"Atomic properties\",\n"," slug=\"atomic_properties\",\n"," description=\"Atomic Properties\",\n"," childs=[\n"," Category(name=\"Abundances\", slug=\"abundances\"),\n"," Category(name=\"Atom sizes\", slug=\"atom_sizes\"),\n"," Category(name=\"Crystal structure\", slug=\"crystal_structure\"),\n"," Category(name=\"Electronegativities\", slug=\"electronegativities\"),\n"," Category(name=\"Heat properties\", slug=\"heat_properties\"),\n"," Category(name=\"Isotopes and NMR\", slug=\"isotopes_and_nmr\"),\n"," Category(name=\"Orbital properties\", slug=\"orbital_properties\"),\n"," Category(name=\"Physical properties\", slug=\"physical_properties\"),\n"," ]\n"," ),\n"," ]\n",")\n","print(root.json(indent=2, exclude_none=True))\n","\n","\n","# usage:\n","# from atomic_features_package.categories import root\n","# print(root['atomic_properties']['atom_sizes'])\n","\n","# pros:\n","# - you can see the full hiearchy\n","# cons:\n","# - it could verbose"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"code","execution_count":38,"metadata":{},"outputs":[{"ename":"TypeError","evalue":"'Category' object is not subscriptable","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)","\u001b[0;32m/var/folders/5q/g7by8qk10j1d8j0d31ljzm9c0000gn/T/ipykernel_59724/116230986.py\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcategory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'atomic_properties'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'physical_properties'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mTypeError\u001b[0m: 'Category' object is not subscriptable"]}],"source":["category['atomic_properties']['physical_properties']\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["\n","tags = Category(\n"," name=\"tags\",\n"," description=\"Tags\",\n",")\n","\n","calculated = Tag(name=\"calculated\", slug=\"calculated\", parent=tags)\n","estimated = Tag(name=\"estimated\", slug=\"estimated\", parent=tags)\n","measured = Tag(name=\"measured\", slug=\"measured\", parent=tags)\n","elementary = Tag(name=\"elementary\", slug=\"elementary\", parent=tags)\n","derived_value = Tag(name=\"derived value\", slug=\"derived value\", parent=tags)\n","electron = Tag(name=\"electron\", slug=\"electron\", parent=tags)\n","proton = Tag(name=\"proton\", slug=\"proton\", parent=tags)\n","\n","\n","print(tags.json(indent=2, exclude_none=True))"]},{"cell_type":"code","execution_count":27,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["{\n"," \"name\": \"tags\",\n"," \"description\": \"Tags\",\n"," \"terms\": [\n"," \"measured\",\n"," \"estimated\",\n"," \"electron\",\n"," \"elementary\",\n"," \"calculated\",\n"," \"proton\",\n"," \"derived value\"\n"," ]\n","}\n"]}],"source":["tags = NonHierarchicalTaxonomy(\n"," name=\"tags\",\n"," description=\"Tags\",\n"," terms={\n"," \"calculated\",\n"," \"estimated\",\n"," \"measured\",\n"," \"elementary\",\n"," \"derived value\",\n"," \"electron\",\n"," \"proton\",\n"," }\n",")\n","\n","print(tags.json(indent=2, exclude_none=True))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"code","execution_count":24,"metadata":{},"outputs":[],"source":["# label: Optional[str] = Field(None, title=\"Latex label\", description='Latex symbol of the property')\n","# synonyms: List[str] = Field(None, description='List of possible synonyms ()')\n","# dtype: Optional[DType] = Field(DType.float, description='Type of the property')\n","# shape: Optional[List[int]]\n","# tensor_rank: Optional[int] = Field(0, description='The rank od the property (default: 0)', ge=0)\n","# units: Optional[str] = Field(None, description='')\n","# unit_system: Optional[str] = Field(None, description='')\n","# dimension: Optional[str] = Field(None, description='')\n","# class Config:\n","# # TODO: temporary :)\n","# extra = 'allow'\n","#\n","# def as_unit(self, str):\n","# # TODO: lazy unit conversation\n","# pass\n","\n","class Property(BaseModel):\n"," name: str\n"," uri: str\n"," unit: str\n"," description: str = None\n"," category: Category = None # category: Category = None\n"," tags: list[Tag] = None #"]},{"cell_type":"code","execution_count":30,"metadata":{},"outputs":[],"source":["# catorgory ref: category/atomic_properties/physical_properties\n","# tags ref: tags/estimated, tags/measured\n","# Note: tag and catogory are singleton objects (uniqueness)\n","# TODO: type and shape\n","# TODO: complex types > list, dict, list of dicts\n","# TODO: doi vs slugs: doi is unique identifier, the name of the object is the slug itself\n","\n","density_of_solid = Property(\n"," name=\"Density of solid\",\n"," uri=None,\n"," description=\"The density of a solid material is defined as its mass per unit volume.\",\n"," unit=\"g/cm3\",\n"," category=category['atomic_properties']['physical_properties'],\n"," tags=[tags['estimated'],tags['measured']]\n",")\n","\n","\n","density_of_solid = Property(\n"," name=\"Density of solid\",\n"," uri=None,\n"," description=\"The density of a solid material is defined as its mass per unit volume.\",\n"," unit=\"g/cm3\",\n"," category=category.atomic_properties.physical_properties,\n"," tags=[tags.estimated,tags.measured]\n",")\n","\n","\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# ionisation energies\n","# Ionisation energy number\tEnthalpy / kJ mol‑1\n","# 1st\t1086.45\n","# 2nd\t2352.62\n","# 3rd\t4620.47\n","# 4th\t6222.68\n","# 5th\t37831.0\n","# 6th\t47277.1\n","\n","# [1086.45, 2352.62, 4620.47, 6222.68, 37831.0, 47277.1]"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Effective nuclear charges for carbon\n","# 1s\t5.6727\n","# 2s\t3.22\t2p\t3.14\n","# 3s\t(no data)\t3p\t(no data)\t3d\t(no data)\n","# 4s\t(no data)\t4p\t(no data)\t4d\t(no data)\t4f\t(no data)\n","# 5s\t(no data)\t5p\t(no data)\t5d\t(no data)\n","# 6s\t(no data)\t6p\t(no data)\n","# 7s\n","\n","# {'1s':5.6727, '2s':3.22, '2p':3.14}"]},{"cell_type":"code","execution_count":33,"metadata":{},"outputs":[{"data":{"text/plain":["[{'Isotope': '12C',\n"," 'Mass / Da': 12.0,\n"," 'Natural abundance (atom %)': 98.93,\n"," 'Nuclear spin (I)': 0,\n"," 'Magnetic moment (μ/μN)': 0},\n"," {'Isotope': '13C',\n"," 'Mass / Da': 13.0033548378,\n"," 'Natural abundance (atom %)': 1.07,\n"," 'Nuclear spin (I)': 0.5,\n"," 'Magnetic moment (μ/μN)': 0.702411}]"]},"execution_count":33,"metadata":{},"output_type":"execute_result"}],"source":["# Isotope\tMass / Da\tNatural abundance (atom %)\tNuclear spin (I)\tMagnetic moment (μ/μN)\n","[\n"," {\n"," 'Isotope':'12C',\n"," 'Mass / Da': 12.0000000,\n"," 'Natural abundance (atom %)': 98.93,\n"," 'Nuclear spin (I)': 0,\n"," 'Magnetic moment (μ/μN)': 0\n"," },\n"," {\n"," 'Isotope':'13C',\n"," 'Mass / Da': 13.0033548378,\n"," 'Natural abundance (atom %)': 1.07,\n"," 'Nuclear spin (I)': 1/2,\n"," 'Magnetic moment (μ/μN)': 0.702411\n"," }\n","]"]},{"cell_type":"code","execution_count":26,"metadata":{},"outputs":[{"data":{"text/plain":["HierarchicalTaxonomy(name='Atomic properties', description='Atomic Properties', childs={'abundances': HierarchicalTaxonomy(name='Abundances', description=None, childs=None), 'atom_sizes': HierarchicalTaxonomy(name='Atom sizes', description=None, childs=None), 'crystal_structure': HierarchicalTaxonomy(name='Crystal structure', description=None, childs=None), 'electronegativities': HierarchicalTaxonomy(name='Electronegativities', description=None, childs=None), 'heat_properties': HierarchicalTaxonomy(name='Heat properties', description=None, childs=None), 'isotopes_and_nmr': HierarchicalTaxonomy(name='Isotopes and NMR', description=None, childs=None), 'orbital_properties': HierarchicalTaxonomy(name='Orbital properties', description=None, childs=None), 'physical_properties': HierarchicalTaxonomy(name='Physical properties', description=None, childs=None)})"]},"execution_count":26,"metadata":{},"output_type":"execute_result"}],"source":["class Material(BaseModel):\n"," name: str\n"," slug: str\n"," description: str = None\n","\n"," density_of_solid: build_property(density_of_solid, unit=\"g/cm3\")\n"," density_of_solid: density_of_solid.definiton(unit=\"g/cm3\")\n"]},{"cell_type":"markdown","metadata":{},"source":["\n","refering to a property:\n","\n","- uri? vd code?\n","\n"]},{"cell_type":"markdown","metadata":{},"source":[]}],"metadata":{"kernelspec":{"display_name":".venv","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":"3.9.16"},"orig_nbformat":4},"nbformat":4,"nbformat_minor":2}
diff --git a/testing.ipynb b/testing.ipynb
new file mode 100644
index 0000000..ab5abe4
--- /dev/null
+++ b/testing.ipynb
@@ -0,0 +1,244 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import atomic_features_package"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import atomic_features_package.atomic_properties.atomic_properties_dft"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from atomic_features_package.atomic_properties import atomic_properties_dft as dft"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dft.method(method = 'pbe', Spin = 'False')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<table><tbody><tr><th>Magnitude</th><td style='text-align:left;'><pre>[0.63665]</pre></td></tr><tr><th>Units</th><td style='text-align:left;'>angstrom</td></tr></tbody></table>"
+ ],
+ "text/latex": [
+ "$\\begin{pmatrix}0.63665\\end{pmatrix}\\ \\mathrm{angstrom}$"
+ ],
+ "text/plain": [
+ "array([0.63665]) <Unit('angstrom')>"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dft.C.atomic_r_val"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<table><tbody><tr><th>Magnitude</th><td style='text-align:left;'><pre>[nan]</pre></td></tr><tr><th>Units</th><td style='text-align:left;'>angstrom</td></tr></tbody></table>"
+ ],
+ "text/latex": [
+ "$\\begin{pmatrix}nan\\end{pmatrix}\\ \\mathrm{angstrom}$"
+ ],
+ "text/plain": [
+ "array([nan]) <Unit('angstrom')>"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dft.C.atomic_r_d"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from atomic_features_package.atomic_properties import periodictable"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"</p>\\n\"+\n \"<ul>\\n\"+\n \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n \"<li>use INLINE resources instead, as so:</li>\\n\"+\n \"</ul>\\n\"+\n \"<code>\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"</code>\\n\"+\n \"</div>\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.2.2.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));",
+ "application/vnd.bokehjs_load.v0+json": ""
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": "(function(root) {\n function embed_document(root) {\n const docs_json = {\"52cef895-168b-4baf-9842-d6da123cb7f0\":{\"version\":\"3.2.2\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p1004\",\"attributes\":{\"width\":950,\"height\":500,\"x_range\":{\"type\":\"object\",\"name\":\"FactorRange\",\"id\":\"p1014\",\"attributes\":{\"factors\":[\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\"]}},\"y_range\":{\"type\":\"object\",\"name\":\"FactorRange\",\"id\":\"p1015\",\"attributes\":{\"factors\":[\"AC\",\"LC\",\" \",\"VII\",\"VI\",\"V\",\"IV\",\"III\",\"II\",\"I\"]}},\"x_scale\":{\"type\":\"object\",\"name\":\"CategoricalScale\",\"id\":\"p1016\"},\"y_scale\":{\"type\":\"object\",\"name\":\"CategoricalScale\",\"id\":\"p1017\"},\"title\":{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p1007\",\"attributes\":{\"text\":\"Periodic 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+ {
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+ "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\nconst JS_MIME_TYPE = 'application/javascript';\n const HTML_MIME_TYPE = 'text/html';\n const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n const CLASS_NAME = 'output_bokeh rendered_html';\n\n /**\n * Render data to the DOM node\n */\n function render(props, node) {\n const script = document.createElement(\"script\");\n node.appendChild(script);\n }\n\n /**\n * Handle when an output is cleared or removed\n */\n function handleClearOutput(event, handle) {\n const cell = handle.cell;\n\n const id = cell.output_area._bokeh_element_id;\n const server_id = cell.output_area._bokeh_server_id;\n // Clean up Bokeh references\n if (id != null && id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n\n if (server_id !== undefined) {\n // Clean up Bokeh references\n const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n cell.notebook.kernel.execute(cmd_clean, {\n iopub: {\n output: function(msg) {\n const id = msg.content.text.trim();\n if (id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n }\n }\n });\n // Destroy server and session\n const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n cell.notebook.kernel.execute(cmd_destroy);\n }\n }\n\n /**\n * Handle when a new output is added\n */\n function handleAddOutput(event, handle) {\n const output_area = handle.output_area;\n const output = handle.output;\n\n // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n return\n }\n\n const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n const bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n const script_attrs = bk_div.children[0].attributes;\n for (let i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n }\n\n function register_renderer(events, OutputArea) {\n\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n const toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[toinsert.length - 1]);\n element.append(toinsert);\n return toinsert\n }\n\n /* Handle when an output is cleared or removed */\n events.on('clear_output.CodeCell', handleClearOutput);\n events.on('delete.Cell', handleClearOutput);\n\n /* Handle when a new output is added */\n events.on('output_added.OutputArea', handleAddOutput);\n\n /**\n * Register the mime type and append_mime function with output_area\n */\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n /* Is output safe? */\n safe: true,\n /* Index of renderer in `output_area.display_order` */\n index: 0\n });\n }\n\n // register the mime type if in Jupyter Notebook environment and previously unregistered\n if (root.Jupyter !== undefined) {\n const events = require('base/js/events');\n const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n }\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"<div style='background-color: #fdd'>\\n\"+\n \"<p>\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"</p>\\n\"+\n \"<ul>\\n\"+\n \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n \"<li>use INLINE resources instead, as so:</li>\\n\"+\n \"</ul>\\n\"+\n \"<code>\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"</code>\\n\"+\n \"</div>\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.2.2.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));",
+ "application/vnd.bokehjs_load.v0+json": ""
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "periodictable.heatmap(Spin = 'False', method = 'hse06')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "ModuleNotFoundError",
+ "evalue": "No module named 'atomic_features_package.pymatgen'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m/var/folders/5q/g7by8qk10j1d8j0d31ljzm9c0000gn/T/ipykernel_33262/413968622.py\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0matomic_features_package\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpymatgen\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPymatgenElement\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'atomic_features_package.pymatgen'"
+ ]
+ }
+ ],
+ "source": [
+ "from atomic_features_package.pymatgen import PymatgenElement"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "db = PymatgenData.load()\n",
+ "element = db.elements\n",
+ "element['H'].atomic_mass"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1.00794"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": ".venv",
+ "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": "3.9.16"
+ },
+ "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/utils/matminer/matminer.ipynb b/utils/matminer/matminer.ipynb
index 75613e3..202d9e3 100644
--- a/utils/matminer/matminer.ipynb
+++ b/utils/matminer/matminer.ipynb
@@ -10,7 +10,7 @@
"from pymatgen.core.periodic_table import Element\n",
"from matminer.utils.data import CohesiveEnergyData, DemlData, MagpieData, PymatgenData, MatscholarElementData, MEGNetElementData, IUCrBondValenceData\n",
"\n",
- "from atomic_features_package.atomic_properties.matminer import MatminerData"
+ "from atomic_features_package.datasets.matminer import MatminerData"
]
},
{
@@ -145,7 +145,7 @@
"outputs": [],
"source": [
"\n",
- " \n",
+ "\n",
"\n",
"# import requests\n",
"#\n",
diff --git a/utils/webelements/atomic_properties_webele.py b/utils/webelements/atomic_properties_webele.py
new file mode 100644
index 0000000..0ba5a03
--- /dev/null
+++ b/utils/webelements/atomic_properties_webele.py
@@ -0,0 +1,763 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+'''
+Module : atomic_properties_webele
+
+This module with help of atomic_properties_webele class from metainfo module instantiates objects with atomic element symbol as identifier to access atomic features available
+'''
+
+import os
+import re
+import json
+import numpy as np
+import pandas as pd
+
+from pathlib import Path
+
+
+
+
+# from ast import literal_eval
+
+# from ..metainfo.metainfo import atomic_properties_webele, metadata
+
+
+# This method_list object could be called after importing this module so one can easily see all the quantities accessible.
+
+# method_list = [method for method in dir(atomic_properties_webele) if (method.startswith('m_') or method.startswith('_') or method.startswith('__') or method.startswith('val') or method.startswith('section_') or method.startswith('get')) is False]
+
+
+path = Path(__file__).parent / "data/Webele.csv"
+path = Path.cwd() / "data/Webele.csv"
+
+df_Webele = pd.read_csv(path,converters={'Electron_bin_En': json.load,'Eff_Nuc_Char':json.load,
+ 'Ionisation_En':json.load})
+
+
+path = os.path.abspath(os.path.join(os.path.dirname(__file__),".."))
+path_new = os.path.join(path, "data","webele", "atomic_sizes.csv")
+df_atomic_sizes = pd.read_csv(path_new,converters={'atom_radii': literal_eval,
+ 'orbital_radii':literal_eval,
+ 'ionic_radii':literal_eval,
+ 'paul_ionic_radii':literal_eval})
+
+
+path = os.path.abspath(os.path.join(os.path.dirname(__file__),".."))
+path_new = os.path.join(path, "data","webele", "cryst_struct.csv")
+df_crys_struct = pd.read_csv(path_new,converters={'cryst_struct': literal_eval})
+
+
+
+
+path = os.path.abspath(os.path.join(os.path.dirname(__file__),".."))
+path_new = os.path.join(path, "data","webele", "electronegativity.csv")
+df_en = pd.read_csv(path_new,converters={'electronegativity': literal_eval})
+
+
+
+
+df_Webele=df_Webele.set_index('Unnamed: 0')
+
+
+path = os.path.abspath(os.path.join(os.path.dirname(__file__),".."))
+path_new = os.path.join(path, "data","webele", "isotopes.csv")
+df_iso = pd.read_csv(path_new,converters={'nat_isotopes': literal_eval,'radiosotope_data':literal_eval,
+ 'nmr_properties':literal_eval})
+
+
+path = os.path.abspath(os.path.join(os.path.dirname(__file__),".."))
+path_new = os.path.join(path, "data","webele", "thermo_chem.csv")
+df_therm = pd.read_csv(path_new,converters={'thermo_chem': literal_eval})
+
+
+objs = []
+for i in df_Webele['Element_symbol']:
+ webele = atomic_properties_webele()
+ prop = webele.m_create(metadata)
+ objs.append(webele)
+
+
+
+for i in df_Webele['Electron_bin_En']:
+ for key,value in i.items():
+ i.update({key:value.replace(r" [2]",'').replace(r" [1]",'').replace(r" [3]",'').
+ replace(r" [2, values derived from reference 1]",'').
+ replace(r" [2, one-particle approximation not valid owing to short core-hole lifetime]",'').
+ replace(r" [2, one-particle approximation not valid owing to short core-hole lifetime, values derived from reference 1]",'').
+ replace(r" [3, one-particle approximation not valid owing to short core-hole lifetime]",'')})
+
+for i in df_Webele['Ionisation_En']:
+ for key,value in i.items():
+ i.update({key:value.replace(r" (calculated)",'').replace(r" (inferred)",'')})
+
+for i in df_Webele['Eff_Nuc_Char']:
+ for key,value in i.items():
+ i.update({key:value.replace(r"(no data)",'')})
+
+
+
+
+for i in range(len(df_atomic_sizes)):
+ k=df_atomic_sizes.loc[i,'atom_radii']['Covalent radius (2008 values)']
+ df_atomic_sizes.loc[i,'atom_radii']['Covalent radius (2008 values)']=re.sub(r"( \(.*?\))|(-)", '', k)
+ l=df_atomic_sizes.loc[i,'atom_radii']['Covalent radius (empirical)']
+ df_atomic_sizes.loc[i,'atom_radii']['Covalent radius (empirical)']=re.sub(r"( \(.*?\))|(-)", '', l)
+
+ m=df_atomic_sizes.loc[i,'atom_radii']['Atomic radius (empirical)']
+ df_atomic_sizes.loc[i,'atom_radii']['Atomic radius (empirical)']=re.sub(r"( \(.*?\))|(-)", '', m)
+
+ n=df_atomic_sizes.loc[i,'atom_radii']['Atomic radius (calculated)']
+ df_atomic_sizes.loc[i,'atom_radii']['Atomic radius (calculated)']=re.sub(r"( \(.*?\))|(-)", '', n)
+
+ o=df_atomic_sizes.loc[i,'atom_radii']['van der Waals radius']
+ df_atomic_sizes.loc[i,'atom_radii']['van der Waals radius']=re.sub(r"( \(.*?\))|(-)", '', o)
+
+
+
+
+# replace empty values with np.nan
+for i in range(len(df_atomic_sizes)):
+ a=df_atomic_sizes.loc[i,'paul_ionic_radii']
+ for k in df_atomic_sizes.loc[i,'paul_ionic_radii'].keys():
+ temp=df_atomic_sizes.loc[i,'paul_ionic_radii'][k]
+ df_atomic_sizes.loc[i,'paul_ionic_radii'][k]=re.sub(r"( \(.*?\))|(-)", '', temp)
+ if df_atomic_sizes.loc[i,'paul_ionic_radii'][k]=="":
+ df_atomic_sizes.loc[i,'paul_ionic_radii'][k]=np.nan
+
+# replace empty values with np.nan
+for i in range(len(df_atomic_sizes)):
+ a=df_atomic_sizes.loc[i,'orbital_radii']
+ for k in df_atomic_sizes.loc[i,'orbital_radii'].keys():
+ temp=df_atomic_sizes.loc[i,'orbital_radii'][k]
+ df_atomic_sizes.loc[i,'orbital_radii'][k]=re.sub(r"( \(.*?\))|(-)", '', temp)
+ if df_atomic_sizes.loc[i,'orbital_radii'][k]=="":
+ df_atomic_sizes.loc[i,'orbital_radii'][k]=np.nan
+
+# replace empty values with np.nan
+for i in range(len(df_atomic_sizes)):
+ a=df_atomic_sizes.loc[i,'ionic_radii']
+ if a=={}:
+ df_atomic_sizes.loc[i,'ionic_radii']=np.nan
+
+
+
+
+# extract data for covalent_radius_mol attribute dictionary
+
+df_as_copy = df_atomic_sizes.copy(deep=True)
+df_as_copy['atom_radii'] = df_as_copy.atom_radii.apply(dict.copy)
+
+
+rem_keys= ['Atomic radius (empirical)', 'Atomic radius (calculated)',
+ 'Covalent radius (2008 values)','Covalent radius (empirical)', 'van der Waals radius']
+
+for dictionary in df_as_copy['atom_radii']:
+ count=0
+ for key in dictionary.copy(): # avoid dictionary changed size error during iteration using .copy method
+ for k in rem_keys:
+ if key==k:
+ del dictionary[k]
+ count=count+1
+
+
+df_Webele['Density_of_solid']=df_Webele['Density_of_solid'].str.replace(r"(\(.*?\))", '', regex=True)
+df_Webele['Density_of_solid'] = np.where(df_Webele['Density_of_solid'] == ' ',np.nan, df_Webele['Density_of_solid'])
+
+df_Webele['Molar_vol']=df_Webele['Molar_vol'].str.replace(r"(\(.*?\))", '', regex=True)
+df_Webele['Molar_vol']=np.where(df_Webele['Molar_vol'] == ' ',np.nan, df_Webele['Molar_vol'])
+
+df_Webele['Youngs_mod']=df_Webele['Youngs_mod'].str.replace(r"(\(.*?\))", '', regex=True)
+df_Webele['Youngs_mod']=np.where(df_Webele['Youngs_mod'] == ' ',np.nan, df_Webele['Youngs_mod'])
+
+df_Webele['Rigidity_mod']=df_Webele['Rigidity_mod'].str.replace(r"(\(.*?\))", '', regex=True)
+df_Webele['Rigidity_mod']=np.where(df_Webele['Rigidity_mod'] == ' ',np.nan, df_Webele['Rigidity_mod'])
+
+df_Webele['Bulk_mod']=df_Webele['Bulk_mod'].str.replace(r"(\(.*?\))", '', regex=True)
+df_Webele['Bulk_mod']=np.where(df_Webele['Bulk_mod'] == ' ',np.nan, df_Webele['Bulk_mod'])
+
+df_Webele['Poissons_ratio']=df_Webele['Poissons_ratio'].str.replace(r"(\(.*?\))", '', regex=True)
+df_Webele['Poissons_ratio']=np.where(df_Webele['Poissons_ratio'] == ' ',np.nan, df_Webele['Poissons_ratio'])
+
+df_Webele['Mineral_hardness']=df_Webele['Mineral_hardness'].str.replace(r"(\(.*?\))", '', regex=True)
+df_Webele['Mineral_hardness']=np.where(df_Webele['Mineral_hardness'] == ' ',np.nan, df_Webele['Mineral_hardness'])
+
+df_Webele['Brinell_hardness']=df_Webele['Brinell_hardness'].str.replace(r"(\(.*?\))", '', regex=True)
+df_Webele['Brinell_hardness']=np.where(df_Webele['Brinell_hardness'] == ' ',np.nan, df_Webele['Brinell_hardness'])
+
+df_Webele['Vickers_hardness']=df_Webele['Vickers_hardness'].str.replace(r"(\(.*?\))", '', regex=True)
+df_Webele['Vickers_hardness']=np.where(df_Webele['Vickers_hardness'] == ' ',np.nan, df_Webele['Vickers_hardness'])
+
+df_Webele['Electrical_resistivity']=df_Webele['Electrical_resistivity'].str.replace(r"(\(.*?\))|(about )|( - direction dependent)|(> )|( high)", '', regex=True)
+df_Webele['Electrical_resistivity']=np.where(df_Webele['Electrical_resistivity'] == ' ',np.nan, df_Webele['Electrical_resistivity'])
+
+df_Webele['Thermal_conductivity']=df_Webele['Thermal_conductivity'].str.replace(r"(\(.*?\))", '', regex=True)
+df_Webele['Thermal_conductivity']=np.where(df_Webele['Thermal_conductivity'] == ' ',np.nan, df_Webele['Thermal_conductivity'])
+
+df_Webele['Coeff_of_lte']=df_Webele['Coeff_of_lte'].str.replace(r"(\(.*?\))", '', regex=True)
+df_Webele['Coeff_of_lte']=np.where(df_Webele['Coeff_of_lte'] == ' ',np.nan, df_Webele['Coeff_of_lte'])
+
+df_Webele['Reflectivity']=df_Webele['Reflectivity'].str.replace(r"(\(.*?\))", '', regex=True)
+df_Webele['Reflectivity']=np.where(df_Webele['Reflectivity'] == ' ',np.nan, df_Webele['Reflectivity'])
+
+df_Webele['Refractive_index']=df_Webele['Refractive_index'].str.replace(r"(\(.*?\))", '', regex=True)
+df_Webele['Refractive_index']=np.where(df_Webele['Refractive_index'] == ' ',np.nan, df_Webele['Refractive_index'])
+
+df_Webele['Velocity_of_sound']=df_Webele['Velocity_of_sound'].str.replace(r"(\(.*?\))", '', regex=True)
+df_Webele['Velocity_of_sound']=np.where(df_Webele['Velocity_of_sound'] == ' ',np.nan, df_Webele['Velocity_of_sound'])
+
+df_Webele['Electron_aff']=df_Webele['Electron_aff'].str.replace(r"(\(.*?\))|( http://dx.doi.org/10.1088/0953-4075/34/14/102)|(>= )|(is )", '', regex=True)
+df_Webele['Electron_aff']=np.where(df_Webele['Electron_aff'] == ' ',np.nan, df_Webele['Electron_aff'])
+
+
+
+for i in range(len(df_crys_struct)):
+ a=df_crys_struct.loc[i,'cryst_struct']['Space group']
+ if a=="" or a=="unknown":
+ df_crys_struct.loc[i,'cryst_struct']['Space group']='-'
+ b=df_crys_struct.loc[i,'cryst_struct']['Space group number']
+ if b=="" or b=="unknown":
+ df_crys_struct.loc[i,'cryst_struct']['Space group number']=np.nan
+ c=df_crys_struct.loc[i,'cryst_struct']['Structure']
+ if c=="" or c=="unknown":
+ df_crys_struct.loc[i,'cryst_struct']['Structure']='-'
+
+
+
+df_crys_struct['atomic_cellparam']=''
+df_crys_struct['atomic_cellparam'] = df_crys_struct['cryst_struct'].copy(deep=True)
+df_crys_struct['atomic_cellparam'] = df_crys_struct['cryst_struct'].apply(dict.copy)
+
+
+rem_keys= ['Space group','Space group number','Structure']
+
+for dictionary in df_crys_struct['atomic_cellparam']:
+ count=0
+ for key in dictionary.copy(): # avoid dictionary changed size error during iteration using .copy method
+ for k in rem_keys:
+ if key==k:
+ del dictionary[k]
+ count=count+1
+
+
+
+for i in df_crys_struct['atomic_cellparam']:
+ for k,v in i.items():
+ #print(v)
+ if v=="" or v=="unknown":
+ i[k]=np.nan
+
+
+for i in df_en['electronegativity']:
+ for k,v in i.items():
+ if k=='Allen electronegativity':
+ if i[k]=='(no data)':
+ i[k]=np.nan
+ #print(k,i[k])
+ else:
+ i[k]==float(i[k])
+ #print(k,i[k])
+ if k=='Pauling electronegativity':
+ if i[k]=='(no data)':
+ i[k]=np.nan
+ #print(k,i[k])
+ else:
+ i[k]==float(i[k])
+ #print(k,i[k])
+ if k=='Allred Rochow electronegativity':
+ if i[k]=='(no data)':
+ i[k]=np.nan
+ #print(k,i[k])
+ else:
+ i[k]==float(i[k])
+ #print(k,i[k])
+ if k=='Sanderson electronegativity':
+ if i[k]=='(no data)':
+ i[k]=np.nan
+ #print(k,i[k])
+ else:
+ i[k]==float(i[k])
+ #print(k,i[k])
+ if k=='Mulliken-Jaffe electronegativity':
+ if i[k]=='(no data)':
+ i[k]=np.nan
+ #print(k,i[k])
+ else:
+ pass
+ #print(k,i[k])
+
+
+# Clean isotopes data
+
+for i in df_iso['nat_isotopes']:
+ #print(i.values())
+ for k,v in i.items():
+ #pass
+ for key,value in v.items():
+ if value=='no data':
+ v[key]=np.nan
+
+for i in df_iso['radiosotope_data']:
+ #print(i.values())
+ for k,v in i.copy().items():
+ #pass
+ #print(k)
+ #print(v.values())
+ try:
+ for key,value in v.items():
+ #print(value)
+ if value=='no data':
+ v[key]=np.nan
+ except AttributeError:
+ del i[k]
+ #print("Hello")
+
+
+# Clean and structure nmr data
+# Create new nmr_mod column to organize nmr data
+
+df_iso['nmr_mod']=""
+
+# Create structured nmr_mod named python dict to store nmr data systematically
+nmr_mod={}
+for i in df_Webele.index:
+ nmr_mod.update({i:{}})
+
+# Store isotope names in a list and remove missing data values from df_iso['nmr_properties'] dataframe
+
+Keys= list(df_iso.loc[0,'nmr_properties'].keys())
+for k in Keys:
+ for i in df_iso['nmr_properties']:
+ for ele in i[k]:
+ try:
+ while ele == 'no data':
+ i[k].remove(ele)
+ while ele == 'none':
+ i[k].remove(ele)
+ except ValueError:
+ pass
+
+#Create dictionary keys
+for row,col in df_iso.iterrows():
+ for k in nmr_mod.keys():
+ if col[0]==k:
+ #for ele in range(len(col[3]['Isotope'])):
+ for nam in col[3]['Isotope']:
+ nmr_mod[k].update({'Isotope_{}'.format(nam):{}})
+ row=row+1
+
+
+
+#Create nested dictionary keys inside nmr_data dictionary
+for row,col in df_iso.iterrows():
+ for k in nmr_mod.keys():
+ if col[0]==k:
+ for iso in nmr_mod[k].keys():
+ for nam in col[3]['Natural abundance /%']:
+ nmr_mod[k][iso].update({'Natural abundance %':"",
+ "Spin (I)":"",
+ 'Frequency relative to 1H = 100 (MHz)':"",
+ 'Receptivity_DP_relative_to_1H = 1.00':"",
+ 'Receptivity_DC_relative_to_13C = 1.00':"",
+ 'Magnetogyric_ratio (γ)':"",
+ 'Magnetic_moment μ (μN)':"",
+ 'Nuclear_quad_mom (Q/millibarn)':"",
+ 'Line_width_factor 1056 l (m4)':""})
+ row=row+1
+
+# Finally fill data in the dictionary
+for row,col in df_iso.iterrows():
+ for k in nmr_mod.keys():
+ if col[0]==k:
+ ind=0
+ for iso in nmr_mod[k].keys():
+ try:
+ nmr_mod[k][iso].update({'Natural abundance %':col[3]['Natural abundance /%'][ind]})
+ nmr_mod[k][iso].update({'Spin (I)':col[3]['Spin (I)'][ind]})
+ nmr_mod[k][iso].update({'Frequency relative to 1H = 100 (MHz)':col[3]['Frequency relative to 1H = 100 (MHz)'][ind]})
+ nmr_mod[k][iso].update({'Receptivity_DP_relative_to_1H = 1.00':col[3]['Receptivity, DP, relative to 1H = 1.00'][ind]})
+ nmr_mod[k][iso].update({'Receptivity_DC_relative_to_13C = 1.00':col[3]['Receptivity, DC, relative to 13C = 1.00'][ind]})
+ nmr_mod[k][iso].update({'Magnetogyric_ratio (γ)':col[3]['Magnetogyric ratio, γ (107 rad\xa0T‑1\xa0s-1)'][ind]})
+ nmr_mod[k][iso].update({'Magnetic_moment μ (μN)':col[3]['Magnetic moment, μ (μN)'][ind]})
+ nmr_mod[k][iso].update({'Nuclear_quad_mom (Q/millibarn)':col[3]['Nuclear quadrupole moment, Q/millibarn'][ind]})
+ nmr_mod[k][iso].update({'Line_width_factor 1056 l (m4)':col[3]['Line width factor, 1056 l (m4)'][ind]})
+ ind=ind+1
+ row=row+1
+ except IndexError:
+ pass
+
+
+
+# correcting erroneous data filled for some elements in nmr_mod dictionary manually.
+
+nmr_mod['carbon']['Isotope_11C'].update({'Natural abundance %':"",
+ "Spin (I)":"",
+ 'Frequency relative to 1H = 100 (MHz)':"",
+ 'Receptivity_DP_relative_to_1H = 1.00':"",
+ 'Receptivity_DC_relative_to_13C = 1.00':"",
+ 'Magnetogyric_ratio (γ)':"",
+ 'Magnetic_moment μ (μN)':"",
+ 'Nuclear_quad_mom (Q/millibarn)':'33.27(24)',
+ 'Line_width_factor 1056 l (m4)':""})
+
+nmr_mod['carbon']['Isotope_13C'].update({'Natural abundance %':1.07,
+ "Spin (I)":"1/2",
+ 'Frequency relative to 1H = 100 (MHz)':25.145020,
+ 'Receptivity_DP_relative_to_1H = 1.00':0.000170,
+ 'Receptivity_DC_relative_to_13C = 1.00':1.00,
+ 'Magnetogyric_ratio (γ)':6.728284,
+ 'Magnetic_moment μ (μN)':1.216613,
+ 'Nuclear_quad_mom (Q/millibarn)':'-',
+ 'Line_width_factor 1056 l (m4)':"-"})
+
+nmr_mod['calcium']['Isotope_41Ca'].update({'Natural abundance %':"",
+ "Spin (I)":"",
+ 'Frequency relative to 1H = 100 (MHz)':"",
+ 'Receptivity_DP_relative_to_1H = 1.00':"",
+ 'Receptivity_DC_relative_to_13C = 1.00':"",
+ 'Magnetogyric_ratio (γ)':"",
+ 'Magnetic_moment μ (μN)':"",
+ 'Nuclear_quad_mom (Q/millibarn)':'-40.8(8)',
+ 'Line_width_factor 1056 l (m4)':""})
+
+nmr_mod['calcium']['Isotope_43Ca'].update({'Natural abundance %': '0.135',
+ 'Spin (I)': '7/2',
+ 'Frequency relative to 1H = 100 (MHz)': '6.730029',
+ 'Receptivity_DP_relative_to_1H = 1.00': '0.00000868',
+ 'Receptivity_DC_relative_to_13C = 1.00': '0.0510',
+ 'Magnetogyric_ratio (γ)': '-1.803069',
+ 'Magnetic_moment μ (μN)': '-1.494067',
+ 'Nuclear_quad_mom (Q/millibarn)': '-4.08',
+ 'Line_width_factor 1056 l (m4)': '2.3'})
+
+nmr_mod['krypton']['Isotope_85Kr'].update({'Natural abundance %':"",
+ "Spin (I)":"",
+ 'Frequency relative to 1H = 100 (MHz)':"",
+ 'Receptivity_DP_relative_to_1H = 1.00':"",
+ 'Receptivity_DC_relative_to_13C = 1.00':"",
+ 'Magnetogyric_ratio (γ)':"",
+ 'Magnetic_moment μ (μN)':"",
+ 'Nuclear_quad_mom (Q/millibarn)':'507(3)',
+ 'Line_width_factor 1056 l (m4)':""})
+
+nmr_mod['iodine']['Isotope_129I'].update({'Natural abundance %':"",
+ "Spin (I)":"",
+ 'Frequency relative to 1H = 100 (MHz)':"",
+ 'Receptivity_DP_relative_to_1H = 1.00':"",
+ 'Receptivity_DC_relative_to_13C = 1.00':"",
+ 'Magnetogyric_ratio (γ)':"",
+ 'Magnetic_moment μ (μN)':"",
+ 'Nuclear_quad_mom (Q/millibarn)':'-604(10)',
+ 'Line_width_factor 1056 l (m4)':""})
+
+nmr_mod['lead']['Isotope_207Pb'].update({'Natural abundance %': '22.1',
+ 'Spin (I)': '1/2',
+ 'Frequency relative to 1H = 100 (MHz)': '20.920597',
+ 'Receptivity_DP_relative_to_1H = 1.00': '0.00201',
+ 'Receptivity_DC_relative_to_13C = 1.00': '11.5',
+ 'Magnetogyric_ratio (γ)': '5.58046',
+ 'Magnetic_moment μ (μN)': '1.00906',
+ 'Nuclear_quad_mom (Q/millibarn)': '-269(165)',
+ 'Line_width_factor 1056 l (m4)': '-'})
+
+nmr_mod['plutonium']['Isotope_239Pu'].update({'Natural abundance %': '0',
+ 'Spin (I)': '1/2',
+ 'Frequency relative to 1H = 100 (MHz)': '3.63',
+ 'Receptivity_DP_relative_to_1H = 1.00': '-',
+ 'Receptivity_DC_relative_to_13C = 1.00': '-',
+ 'Magnetogyric_ratio (γ)': '0.972',
+ 'Magnetic_moment μ (μN)': '',
+ 'Nuclear_quad_mom (Q/millibarn)': '-3345(13) [Mössbauer state]',
+ 'Line_width_factor 1056 l (m4)': ''})
+
+nmr_mod['plutonium']['Isotope_241Pu'].update({'Natural abundance %': '',
+ 'Spin (I)': '',
+ 'Frequency relative to 1H = 100 (MHz)': '',
+ 'Receptivity_DP_relative_to_1H = 1.00': '',
+ 'Receptivity_DC_relative_to_13C = 1.00': '',
+ 'Magnetogyric_ratio (γ)': '',
+ 'Magnetic_moment μ (μN)': '',
+ 'Nuclear_quad_mom (Q/millibarn)': '5600(200)',
+ 'Line_width_factor 1056 l (m4)': ''})
+
+nmr_mod['rhodium']['Isotope_100Rh'].update({'Natural abundance %': '',
+ 'Spin (I)': '',
+ 'Frequency relative to 1H = 100 (MHz)': '',
+ 'Receptivity_DP_relative_to_1H = 1.00': '',
+ 'Receptivity_DC_relative_to_13C = 1.00': '',
+ 'Magnetogyric_ratio (γ)': '',
+ 'Magnetic_moment μ (μN)': '',
+ 'Nuclear_quad_mom (Q/millibarn)': '153 (excited nuclear state)',
+ 'Line_width_factor 1056 l (m4)': ''})
+
+nmr_mod['rhodium']['Isotope_103Rh'].update({'Natural abundance %': '100',
+ 'Spin (I)': '1/2',
+ 'Frequency relative to 1H = 100 (MHz)': '3.172310',
+ 'Receptivity_DP_relative_to_1H = 1.00': '0.0000317',
+ 'Receptivity_DC_relative_to_13C = 1.00': '0.181',
+ 'Magnetogyric_ratio (γ)': '-0.8468',
+ 'Magnetic_moment μ (μN)': '-0.1531',
+ 'Nuclear_quad_mom (Q/millibarn)': '-',
+ 'Line_width_factor 1056 l (m4)': '-'})
+
+nmr_mod['tungsten']['Isotope_182W'].update({'Natural abundance %': '',
+ 'Spin (I)': '',
+ 'Frequency relative to 1H = 100 (MHz)': '',
+ 'Receptivity_DP_relative_to_1H = 1.00': '',
+ 'Receptivity_DC_relative_to_13C = 1.00': '',
+ 'Magnetogyric_ratio (γ)': '',
+ 'Magnetic_moment μ (μN)': '',
+ 'Nuclear_quad_mom (Q/millibarn)': '-2130(350) [Mössbauer state]',
+ 'Line_width_factor 1056 l (m4)': ''})
+
+nmr_mod['tungsten']['Isotope_183W'].update({'Natural abundance %': '14.3',
+ 'Spin (I)': '1/2',
+ 'Frequency relative to 1H = 100 (MHz)': '4.166398',
+ 'Receptivity_DP_relative_to_1H = 1.00': '0.0000107',
+ 'Receptivity_DC_relative_to_13C = 1.00': '0.0613',
+ 'Magnetogyric_ratio (γ)': '1.1282403',
+ 'Magnetic_moment μ (μN)': '0.20400919',
+ 'Nuclear_quad_mom (Q/millibarn)': '',
+ 'Line_width_factor 1056 l (m4)': '-'})
+
+nmr_mod['uranium']['Isotope_233U'].update({'Natural abundance %': '',
+ 'Spin (I)': '',
+ 'Frequency relative to 1H = 100 (MHz)': '',
+ 'Receptivity_DP_relative_to_1H = 1.00': '',
+ 'Receptivity_DC_relative_to_13C = 1.00': '',
+ 'Magnetogyric_ratio (γ)': '',
+ 'Magnetic_moment μ (μN)': '',
+ 'Nuclear_quad_mom (Q/millibarn)': '3663(8)',
+ 'Line_width_factor 1056 l (m4)': ''})
+
+nmr_mod['uranium']['Isotope_235U'].update({'Natural abundance %': '0.7200',
+ 'Spin (I)': '7/2',
+ 'Frequency relative to 1H = 100 (MHz)': '1.841000',
+ 'Receptivity_DP_relative_to_1H = 1.00': '-',
+ 'Receptivity_DC_relative_to_13C = 1.00': '-',
+ 'Magnetogyric_ratio (γ)': '-0.52',
+ 'Magnetic_moment μ (μN)': '-0.43',
+ 'Nuclear_quad_mom (Q/millibarn)': '4936(6)',
+ 'Line_width_factor 1056 l (m4)': '-'})
+
+# Lastly add the structured dict into df_iso dataframe
+
+df_iso['nmr_mod'] = nmr_mod.values()
+
+
+
+# Clean thermochemical data
+rem_keys= ['Units']
+for i in df_therm['thermo_chem']:
+ for key in i.copy(): # avoid dictionary changed size error during iteration using .copy method
+ for k in rem_keys:
+ if key==k:
+ del i[k]
+
+#dict keys whose values needs to be cleaned
+mod_key=['Melting point', 'Boiling point', 'Liquid range', 'Critical temperature',
+ 'Superconduction temperature', 'Enthalpy of fusion', 'Enthalpy of vaporisation', 'Enthalpy of atomisation']
+
+for i in range(len(df_therm)):
+ a=df_therm.loc[i,'thermo_chem']
+ for k in mod_key:
+ for a in df_therm.loc[i,'thermo_chem'].keys():
+ if a==k and i<118:
+ temp=df_therm.loc[i,'thermo_chem'][k]
+ df_therm.loc[i,'thermo_chem'][k]=re.sub(r"(\(.*?\))|(-)|( kJ mol-1)|( K)|(about )|(maybe about)", '', temp)
+ if df_therm.loc[i,'thermo_chem'][k]=="":
+ df_therm.loc[i,'thermo_chem'][k]=np.nan
+ else:
+ df_therm.loc[i,'thermo_chem'][k]=float(df_therm.loc[i,'thermo_chem'][k])
+
+
+# Seperate and store thermdata in another column "atomic_thermdata" of df_therm dataframe.
+df_therm['atomic_thermdata']=''
+df_therm['atomic_thermdata'] = df_therm['thermo_chem'].copy(deep=True)
+df_therm['atomic_thermdata'] = df_therm['thermo_chem'].apply(dict.copy)
+
+
+rem_keys= ['Melting point', 'Boiling point', 'Liquid range', 'Critical temperature',
+ 'Superconduction temperature', 'Enthalpy of fusion', 'Enthalpy of vaporisation', 'Enthalpy of atomisation']
+
+for dictionary in df_therm['atomic_thermdata']:
+ count=0
+ for key in dictionary.copy(): # avoid dictionary changed size error during iteration using .copy method
+ for k in rem_keys:
+ if key==k:
+ del dictionary[k]
+ count=count+1
+
+for i in range(len(df_therm)):
+ for k,v in df_therm.loc[i,'atomic_thermdata'].items():
+ for key,val in v.items():
+ temp=val
+ val=re.sub(r"(\*)|(no data)|(not known)", '', temp)
+ v[key]=val
+ if val=="":
+ v[key]=np.nan
+
+for i in range(len(df_therm)):
+ for k,v in df_therm.loc[i,'atomic_thermdata'].items():
+ for key in v.copy():
+ if k==v[key]:
+ del v[key]
+
+
+
+
+
+
+
+
+
+method_list
+
+
+
+
+
+count = 0
+for obj in objs:
+ obj.section_webele_metadata.source = 'Webelements'
+ obj.section_webele_metadata.atomic_spin_setting = 'NA'
+ obj.section_webele_metadata.atomic_method = 'NA'
+ obj.section_webele_metadata.atomic_basis_set = 'NA'
+ obj.density_of_solid = float(df_Webele.iloc[count,df_Webele.columns.get_loc("Density_of_solid")])
+ obj.youngs_mod = float(df_Webele.iloc[count,df_Webele.columns.get_loc("Youngs_mod")])
+ obj.rigidity_mod = float(df_Webele.iloc[count,df_Webele.columns.get_loc("Rigidity_mod")])
+ obj.bulk_mod = float(df_Webele.iloc[count,df_Webele.columns.get_loc("Bulk_mod")])
+ obj.poissons_ratio = float(df_Webele.iloc[count,df_Webele.columns.get_loc("Poissons_ratio")])
+ obj.mineral_hardness = float(df_Webele.iloc[count,df_Webele.columns.get_loc("Mineral_hardness")])
+ obj.brinell_hardness = float(df_Webele.iloc[count,df_Webele.columns.get_loc("Brinell_hardness")])
+ obj.vickers_hardness = float(df_Webele.iloc[count,df_Webele.columns.get_loc("Vickers_hardness")])
+ obj.electrical_resist = float(df_Webele.iloc[count,df_Webele.columns.get_loc("Electrical_resistivity")])
+ obj.thermal_conduct = float(df_Webele.iloc[count,df_Webele.columns.get_loc("Thermal_conductivity")])
+ obj.coeff_of_lte = float(df_Webele.iloc[count,df_Webele.columns.get_loc("Coeff_of_lte")])
+ obj.reflectivity = float(df_Webele.iloc[count,df_Webele.columns.get_loc("Reflectivity")])
+ obj.refract_index = float(df_Webele.iloc[count,df_Webele.columns.get_loc("Refractive_index")])
+ obj.vel_of_sound = float(df_Webele.iloc[count,df_Webele.columns.get_loc("Velocity_of_sound")])
+ obj.atomic_ebe = df_Webele.iloc[count,df_Webele.columns.get_loc("Electron_bin_En")]
+ obj.atomic_enc = df_Webele.iloc[count,df_Webele.columns.get_loc("Eff_Nuc_Char")]
+ obj.atomic_ie = df_Webele.iloc[count,df_Webele.columns.get_loc("Ionisation_En")]
+ obj.atomic_ea = float(df_Webele.iloc[count,df_Webele.columns.get_loc("Electron_aff")])
+ try:
+ obj.atomic_radius_calculated = float(df_atomic_sizes.iloc[count,df_atomic_sizes.columns.
+ get_loc("atom_radii")]
+ ['Atomic radius (calculated)'])
+ except ValueError:
+ obj.atomic_radius_calculated = np.nan
+
+ try:
+ obj.atomic_radius = float(df_atomic_sizes.iloc[count,df_atomic_sizes.columns.get_loc("atom_radii")]
+ ['Atomic radius (empirical)'])
+ except ValueError:
+ obj.atomic_radius = np.nan
+
+ try:
+ obj.covalent_rad = float(df_atomic_sizes.iloc[count,df_atomic_sizes.columns.get_loc("atom_radii")]
+ ['Covalent radius (2008 values)'])
+ except ValueError:
+ obj.covalent_rad = np.nan
+
+ try:
+ obj.covalent_rad_emp = float(df_atomic_sizes.iloc[count,df_atomic_sizes.columns.get_loc("atom_radii")]
+ ['Covalent radius (empirical)'])
+ except ValueError:
+ obj.covalent_rad_emp = np.nan
+
+ try:
+ obj.van_der_waals_rad =float(df_atomic_sizes.iloc[count,df_atomic_sizes.columns.get_loc("atom_radii")]
+ ['van der Waals radius'])
+ except ValueError:
+ obj.van_der_waals_rad = np.nan
+
+ obj.covalent_radius_mol = df_as_copy.iloc[count,df_as_copy.columns.get_loc("atom_radii")]
+ obj.atomic_orbital_radii = df_atomic_sizes.iloc[count,df_atomic_sizes.columns.get_loc("orbital_radii")]
+ obj.atomic_ionic_radii = df_atomic_sizes.iloc[count,df_atomic_sizes.columns.get_loc("ionic_radii")]
+ obj.paul_ionic_radii = df_atomic_sizes.iloc[count,df_atomic_sizes.columns.get_loc("paul_ionic_radii")]
+
+ obj.atomic_spacegroup = df_crys_struct.iloc[count,df_crys_struct.columns.get_loc("cryst_struct")]['Space group']
+ obj.atomic_spacegroupnum= float(df_crys_struct.iloc[count,df_crys_struct.columns.get_loc("cryst_struct")]
+ ['Space group number'])
+ obj.atomic_structure = df_crys_struct.iloc[count,df_crys_struct.columns.get_loc("cryst_struct")]['Structure']
+ obj.atomic_cellparam = df_crys_struct.iloc[count,df_crys_struct.columns.get_loc("atomic_cellparam")]
+
+ obj.atomic_en =df_en.iloc[count,df_en.columns.get_loc("electronegativity")]
+ obj.atomic_en_allen = float(df_en.iloc[count,df_en.columns.get_loc("electronegativity")]
+ ['Allen electronegativity'])
+ obj.atomic_en_paul = float(df_en.iloc[count,df_en.columns.get_loc("electronegativity")]
+ ['Pauling electronegativity'])
+ obj.atomic_en_allredroch = float(df_en.iloc[count,df_en.columns.get_loc("electronegativity")]
+ ['Allred Rochow electronegativity'])
+ obj.atomic_en_sanderson = float(df_en.iloc[count,df_en.columns.get_loc("electronegativity")]
+ ['Sanderson electronegativity'])
+
+ obj.atomic_nat_iso = df_iso.iloc[count,df_iso.columns.get_loc("nat_isotopes")]
+ obj.atomic_rad_iso = df_iso.iloc[count,df_iso.columns.get_loc("radiosotope_data")]
+ obj.atomic_nmr = df_iso.iloc[count,df_iso.columns.get_loc("nmr_mod")]
+
+ obj.boiling_point = float(df_therm.iloc[count,df_therm.columns.get_loc("thermo_chem")]
+ ['Boiling point'])
+ obj.supercond_temp = float(df_therm.iloc[count,df_therm.columns.get_loc("thermo_chem")]
+ ['Superconduction temperature'])
+ obj.melting_point = float(df_therm.iloc[count,df_therm.columns.get_loc("thermo_chem")]
+ ['Melting point'])
+ obj.liquid_range = float(df_therm.iloc[count,df_therm.columns.get_loc("thermo_chem")]
+ ['Liquid range'])
+ obj.critical_temperature = float(df_therm.iloc[count,df_therm.columns.get_loc("thermo_chem")]
+ ['Critical temperature'])
+ obj.atomic_hfu = float(df_therm.iloc[count,df_therm.columns.get_loc("thermo_chem")]
+ ['Enthalpy of fusion'])
+ obj.atomic_hvap = float(df_therm.iloc[count,df_therm.columns.get_loc("thermo_chem")]
+ ['Enthalpy of vaporisation'])
+ obj.atomic_hatm = float(df_therm.iloc[count,df_therm.columns.get_loc("thermo_chem")]
+ ['Enthalpy of atomisation'])
+ obj.atomic_thermdata = df_therm.iloc[count,df_therm.columns.get_loc("atomic_thermdata")]
+ count+=1
+
+atomic_element_symbol = df_Webele['Element_symbol'].values
+for count, symbol in enumerate(atomic_element_symbol):
+ globals()[symbol] = objs[count]
+
+
+df_Webele.rename_axis('',inplace=True)
+df_comb = df_Webele.copy(deep=True)
+df_comb['atom_radii']= df_atomic_sizes['atom_radii'].values
+df_comb['orbital_radii']= df_atomic_sizes['orbital_radii'].values
+df_comb['ionic_radii']= df_atomic_sizes['ionic_radii'].values
+df_comb['covalent_radius_mol']= df_as_copy['atom_radii'].values
+df_comb['paul_ionic_radii']= df_atomic_sizes['paul_ionic_radii'].values
+df_comb['nat_isotopes']= df_iso['nat_isotopes'].values
+df_comb['radiosotope_data']= df_iso['radiosotope_data'].values
+df_comb['nmr_mod']= df_iso['nmr_mod'].values
+df_comb['electronegativity']= df_en['electronegativity'].values
+df_comb['cryst_struct']= df_crys_struct['cryst_struct'].values
+df_comb['atomic_cellparam']= df_crys_struct['atomic_cellparam'].values
+df_comb['thermo_chem']= df_therm['thermo_chem'].values
+df_comb['atomic_thermdata']= df_therm['atomic_thermdata'].values
+
+
+
+def definition(abc):
+ '''
+ This function can be used to access quantity definitions for each element features accessible
+ '''
+ if hasattr(atomic_properties_webele,abc):
+ return print(getattr(atomic_properties_webele,abc).__doc__)
+ elif hasattr(metadata,abc):
+ return print(getattr(metadata,abc).__doc__)
+ else:
+ return print('Check the input feature name. Either it is wrong or requested feature does not exist from this webelements source')
+
+
+def symbol(abc):
+ '''
+This function utilty is, it can be called to access element properties based on its symbol in periodic table. Can be usefull if want to acess particular property of multiple elements at once
+ '''
+ return globals()[abc]
+
+
+
+
+
diff --git a/utils/webelements/webelements.ipynb b/utils/webelements/webelements.ipynb
new file mode 100644
index 0000000..017a898
--- /dev/null
+++ b/utils/webelements/webelements.ipynb
@@ -0,0 +1,1261 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#!/usr/bin/env python\n",
+ "# coding: utf-8\n",
+ "\n",
+ "'''\n",
+ "Module : atomic_properties_webele\n",
+ "\n",
+ "This module with help of atomic_properties_webele class from metainfo module instantiates objects with atomic element symbol as identifier to access atomic features available\n",
+ "'''\n",
+ "\n",
+ "import os\n",
+ "import re\n",
+ "import csv\n",
+ "import json\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "\n",
+ "from pathlib import Path"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def convert_element_name(value):\n",
+ " return {'element_name': value}\n",
+ "\n",
+ "def convert_element_symbol(value):\n",
+ " return {'element_symbol': value}\n",
+ "\n",
+ "def convert_density_of_solid(value):\n",
+ " value = value.replace('(estimated)','')\n",
+ " value = value.replace('(predicted)', '')\n",
+ " value = value.replace('(predicted, other prediction 5000)', '')\n",
+ " \n",
+ " return {'density_of_solid': float(value)}\n",
+ "\n",
+ "def convert_molar_vol(value):\n",
+ " value = value.replace('(rough estimate based upon density estimate)', '')\n",
+ " value = value.replace('(seems high?)', '')\n",
+ " return {'molar_vol': float(value)}\n",
+ "\n",
+ "def convert_youngs_mod(value):\n",
+ " return {'youngs_mod': float(value)}\n",
+ "\n",
+ "def convert_rigidity_mod(value):\n",
+ " return {'rigidity_mod': float(value)}\n",
+ "\n",
+ "def convert_bulk_mod(value):\n",
+ " value = value.replace('1.1 (liquid)', '1.1')\n",
+ " return {'bulk_mod': float(value)}\n",
+ "\n",
+ "def convert_poissons_ratio(value):\n",
+ " value = value.rstrip(' (no units)')\n",
+ " return {'poissons_ratio': float(value)}\n",
+ "\n",
+ "def convert_mineral_hardness(value):\n",
+ " value = value.replace('0.5 (graphite; diamond is 10.0) (no units)', '10.0')\n",
+ " value = value.replace(' (no units)', '')\n",
+ " return {'mineral_hardness': float(value)}\n",
+ "\n",
+ "def convert_brinell_hardness(value):\n",
+ " return {'brinell_hardness': float(value)}\n",
+ "\n",
+ "def convert_vickers_hardness(value):\n",
+ " return {'vickers_hardness': float(value)}\n",
+ "\n",
+ "def convert_electrical_resistivity(value):\n",
+ " value = value.replace('about ', '')\n",
+ " value = value.replace('> ', '')\n",
+ " value = value.replace(' - direction dependent', '')\n",
+ "\n",
+ " if value == 'high':\n",
+ " return {}\n",
+ "\n",
+ " if value.startswith(\"about\") or value.startswith(\">\"):\n",
+ " print(value)\n",
+ " value = 1\n",
+ " \n",
+ " return {'electrical_resistivity': float(value)}\n",
+ "\n",
+ "def convert_thermal_conductivity(value):\n",
+ " value = value.replace('66.6 (polycrystalline; 515 parallel to c axis, 745 perpendicular to c axis)', '66.6')\n",
+ " value = value.replace('0.00569 (gas)', '0.00569')\n",
+ " value = value.replace('(estimate)', '')\n",
+ " value = value.replace('0.0023 (gaseous state, estimate)', '0.0023')\n",
+ "\n",
+ " return {'thermal_conductivity': float(value)}\n",
+ "\n",
+ "def convert_coeff_of_lte(value):\n",
+ " return {'coeff_of_lte': float(value)}\n",
+ "\n",
+ "def convert_reflectivity(value):\n",
+ " return {'reflectivity': float(value)}\n",
+ "\n",
+ "def convert_refractive_index(value):\n",
+ " value = value.replace('1.000132 (gas; liquid 1.12) (no units)', '1.000132')\n",
+ " value = value.replace('1.000035 (gas; liquid 1.028) (no units)', '1.000035')\n",
+ " value = value.replace('1.000298 (gas; liquid 1.197) (no units)', '1.000298')\n",
+ " value = value.replace('1.000271 (gas; liquid 1.221) (no units)', '1.000271')\n",
+ " value = value.replace('2.417 (diamond) (no units)', '2.417')\n",
+ " value = value.replace(' (no units)', '')\n",
+ " \n",
+ " return {'refractive_index': float(value)}\n",
+ "\n",
+ "def convert_velocity_of_sound(value):\n",
+ " return {'velocity_of_sound': float(value)}\n",
+ "\n",
+ "def convert_electron_bin_en(value):\n",
+ " value = json.loads(value.replace(\"'\", '\"'))\n",
+ " value_dict = {}\n",
+ "\n",
+ " for (k,v) in value.items():\n",
+ " if v == '-':\n",
+ " value_dict[k] = None\n",
+ " continue\n",
+ "\n",
+ " value_dict[k] = float(v.split()[0])\n",
+ "\n",
+ " return {'electron_bin_en': value_dict}\n",
+ "\n",
+ "def convert_eff_nuc_char(value):\n",
+ " value = json.loads(value.replace(\"'\", '\"'))\n",
+ " value_dict = {}\n",
+ " for (k,v) in value.items():\n",
+ " \n",
+ " value_dict[k] = float(v) if v != '(no data)' else None\n",
+ "\n",
+ " return {'eff_nuc_char': value_dict}\n",
+ "\n",
+ "def convert_ionisation_en(value):\n",
+ " value = json.loads(value.replace(\"'\", '\"'))\n",
+ "\n",
+ " # inds = [int(v[:-2]) for v in value.keys()]\n",
+ " # assert len(inds) == max(inds)\n",
+ "\n",
+ " value_list = []\n",
+ " for s in value.values():\n",
+ " s = s.replace(' (calculated)', '')\n",
+ " s = s.replace(' (inferred)', '')\n",
+ " \n",
+ " value_list.append(float(s))\n",
+ "\n",
+ " return {'ionisation_en': value_list}\n",
+ "\n",
+ "def convert_electron_aff(value):\n",
+ " value = value.replace('is ', '')\n",
+ " value = value.replace('33 http://dx.doi.org/10.1088/0953-4075/34/14/102', '33')\n",
+ " value = value.replace('>= ', '')\n",
+ " \n",
+ " return {'electron_aff': float(value)}\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "FileNotFoundError",
+ "evalue": "[Errno 2] No such file or directory: '/Users/fekad/Work/ai-toolkit/packages/atomic-features-package/utils/webelements/utils/webelements/data/Webele.csv'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m/var/folders/5q/g7by8qk10j1d8j0d31ljzm9c0000gn/T/ipykernel_90292/1246956635.py\u001b[0m in \u001b[0;36m<cell line: 27>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcwd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m\"utils/webelements/data/Webele.csv\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnewline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mcsvfile\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 28\u001b[0m \u001b[0mreader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcsv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcsvfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdelimiter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m','\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[0mheader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/Users/fekad/Work/ai-toolkit/packages/atomic-features-package/utils/webelements/utils/webelements/data/Webele.csv'"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "map_transformer = {\n",
+ " '': convert_element_name,\n",
+ " 'Element_symbol': convert_element_symbol,\n",
+ " 'Density_of_solid': convert_density_of_solid,\n",
+ " 'Molar_vol': convert_molar_vol,\n",
+ " 'Youngs_mod': convert_youngs_mod,\n",
+ " 'Rigidity_mod': convert_rigidity_mod,\n",
+ " 'Bulk_mod': convert_bulk_mod,\n",
+ " 'Poissons_ratio': convert_poissons_ratio,\n",
+ " 'Mineral_hardness': convert_mineral_hardness,\n",
+ " 'Brinell_hardness': convert_brinell_hardness,\n",
+ " 'Vickers_hardness': convert_vickers_hardness,\n",
+ " 'Electrical_resistivity': convert_electrical_resistivity,\n",
+ " 'Thermal_conductivity': convert_thermal_conductivity,\n",
+ " 'Coeff_of_lte': convert_coeff_of_lte,\n",
+ " 'Reflectivity': convert_reflectivity,\n",
+ " 'Refractive_index': convert_refractive_index,\n",
+ " 'Velocity_of_sound': convert_velocity_of_sound,\n",
+ " 'Electron_bin_En': convert_electron_bin_en,\n",
+ " 'Eff_Nuc_Char': convert_eff_nuc_char,\n",
+ " 'Ionisation_En': convert_ionisation_en,\n",
+ " 'Electron_aff': convert_electron_aff\n",
+ "}\n",
+ "\n",
+ "data = {}\n",
+ "path = Path.cwd() / \"utils/webelements/data/Webele.csv\"\n",
+ "with open(path, newline='') as csvfile:\n",
+ " reader = csv.reader(csvfile, delimiter=',')\n",
+ " header = next(reader)\n",
+ " \n",
+ " for row in reader:\n",
+ " element = row[1]\n",
+ " props = {}\n",
+ " \n",
+ " for (prop, value) in zip(header[2:], row[2:]):\n",
+ " value = value.strip() \n",
+ " if value.startswith('(no data)'):\n",
+ " continue\n",
+ " \n",
+ " props.update(map_transformer[prop](value))\n",
+ " \n",
+ " data[element] = props\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "FileNotFoundError",
+ "evalue": "[Errno 2] No such file or directory: '/Users/fekad/Work/ai-toolkit/packages/atomic-features-package/utils/webelements/utils/webelements/data/atomic_sizes.csv'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m/var/folders/5q/g7by8qk10j1d8j0d31ljzm9c0000gn/T/ipykernel_90292/3647919540.py\u001b[0m in \u001b[0;36m<cell line: 64>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcwd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m\"utils/webelements/data/atomic_sizes.csv\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnewline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mcsvfile\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 65\u001b[0m \u001b[0mreader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcsv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcsvfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdelimiter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m','\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[0mheader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/Users/fekad/Work/ai-toolkit/packages/atomic-features-package/utils/webelements/utils/webelements/data/atomic_sizes.csv'"
+ ]
+ }
+ ],
+ "source": [
+ "def convert_atom_radii(value):\n",
+ " value = json.loads(value.replace(\"'\", '\"'))\n",
+ " key_transformer = { \n",
+ " 'Atomic radius (empirical)': 'atomic_radius_empirical',\n",
+ " 'Atomic radius (calculated)': 'atomic_radius_calculated',\n",
+ " 'Covalent radius (2008 values)': 'covalent_radius_2008_values',\n",
+ " 'Molecular single bond covalent radius': 'molecular_single_bond_covalent_radius',\n",
+ " 'Molecular double bond covalent radius': 'molecular_double_bond_covalent_radius',\n",
+ " 'Molecular triple bond covalent radius': 'molecular_triple_bond_covalent_radius',\n",
+ " 'Covalent radius (empirical)': 'covalent_radius_empirical',\n",
+ " 'van der Waals radius': 'van_der_waals_radius'\n",
+ " }\n",
+ "\n",
+ " value_dict = {}\n",
+ " for (k, v) in value.items():\n",
+ " k = key_transformer[k]\n",
+ " if v == '-':\n",
+ " value_dict[k] = None\n",
+ " continue\n",
+ "\n",
+ " v = v.strip('[] ')\n",
+ " value_dict[k] = float(v.split()[0])\n",
+ " \n",
+ " return {'atom_radii': value_dict}\n",
+ "\n",
+ "def convert_orbital_radii(value):\n",
+ " value = json.loads(value.replace(\"'\", '\"'))\n",
+ "\n",
+ " key_transformer = {\n",
+ " 's orbital': 's',\n",
+ " 'p orbital': 'p',\n",
+ " 'd orbital': 'd',\n",
+ " 'f orbital': 'f'\n",
+ " }\n",
+ " \n",
+ " value_dict = {}\n",
+ " for (k, v) in value.items():\n",
+ " k = key_transformer[k]\n",
+ " if v == '-':\n",
+ " value_dict[k] = None\n",
+ " continue\n",
+ "\n",
+ " value_dict[k] = float(v)\n",
+ " \n",
+ " return {'orbital_radii': value_dict}\n",
+ "\n",
+ "def convert_ionic_radii(value):\n",
+ " value = json.loads(value.replace(\"'\", '\"'))\n",
+ " return {'ionic_radii': value}\n",
+ "\n",
+ "def convert_paul_ionic_radii(value):\n",
+ " value = json.loads(value.replace(\"'\", '\"'))\n",
+ " return {'paul_ionic_radii': value}\n",
+ "\n",
+ "map_transformer = {\n",
+ " 'atom_radii': convert_atom_radii, \n",
+ " 'orbital_radii': convert_orbital_radii, \n",
+ " 'ionic_radii': convert_ionic_radii, \n",
+ " 'paul_ionic_radii': convert_paul_ionic_radii\n",
+ "}\n",
+ "\n",
+ "data = {}\n",
+ "path = Path.cwd() / \"utils/webelements/data/atomic_sizes.csv\"\n",
+ "with open(path, newline='') as csvfile:\n",
+ " reader = csv.reader(csvfile, delimiter=',')\n",
+ " header = next(reader)\n",
+ " \n",
+ " for row in reader:\n",
+ " element = row[0]\n",
+ " props = {}\n",
+ " \n",
+ " for (prop, value) in zip(header[1:], row[1:]):\n",
+ " props.update(map_transformer[prop](value))\n",
+ " \n",
+ " data[element] = props\n",
+ " break \n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def space_group(value):\n",
+ " return {'space_group_name': value}\n",
+ "\n",
+ "def space_group_number(value):\n",
+ " return {'space_group': float(value)}\n",
+ "\n",
+ "def structure(value):\n",
+ " return {'structure': value.split(' ',maxsplit=1)[0]}\n",
+ "\n",
+ "def cell_parameter_a(value):\n",
+ " return {'cell_parameter_a': float(value)}\n",
+ "\n",
+ "def cell_parameter_b(value):\n",
+ " return {'cell_parameter_b': float(value)}\n",
+ "\n",
+ "def cell_parameter_c(value):\n",
+ " return {'cell_parameter_c': float(value)}\n",
+ "\n",
+ "def cell_parameter_alpha(value):\n",
+ " return {'cell_parameter_alpha': float(value)}\n",
+ "\n",
+ "def cell_parameter_beta(value):\n",
+ " return {'cell_parameter_beta': float(value)}\n",
+ "\n",
+ "def cell_parameter_gamma(value):\n",
+ " return {'cell_parameter_gamma': float(value)}\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['', 'cryst_struct']\n"
+ ]
+ }
+ ],
+ "source": [
+ "data = {}\n",
+ "path = Path.cwd() / \"data/cryst_struct.csv\"\n",
+ "with open(path, newline='') as csvfile:\n",
+ " reader = csv.reader(csvfile, delimiter=',')\n",
+ " header = next(reader)\n",
+ " print(header)\n",
+ "\n",
+ " map_transformer = { \n",
+ " 'Space group': space_group,\n",
+ " 'Space group number': space_group_number,\n",
+ " 'Structure': structure,\n",
+ " 'Cell_parameter_a': cell_parameter_a,\n",
+ " 'Cell_parameter_b': cell_parameter_b,\n",
+ " 'Cell_parameter_c': cell_parameter_c,\n",
+ " 'Cell_parameter_α': cell_parameter_alpha,\n",
+ " 'Cell_parameter_β': cell_parameter_beta,\n",
+ " 'Cell_parameter_γ': cell_parameter_gamma,\n",
+ " }\n",
+ "\n",
+ "\n",
+ " for row in reader:\n",
+ " element = row[0]\n",
+ " d = json.loads(row[1].replace('\\'', '\"'))\n",
+ "\n",
+ " props = {}\n",
+ " for (prop, value) in d.items():\n",
+ " if value == '' or value == 'unknown':\n",
+ " continue\n",
+ " props.update(map_transformer[prop](value))\n",
+ " \n",
+ "\n",
+ " data[element] = props\n",
+ " # break \n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def pauling_electronegativity(value):\n",
+ " return {'pauling_electronegativity': float(value)}\n",
+ "\n",
+ "def sanderson_electronegativity(value):\n",
+ " return {'sanderson_electronegativity': float(value)}\n",
+ "\n",
+ "def allred_rochow_electronegativity(value):\n",
+ " return {'allred_rochow_electronegativity': float(value)}\n",
+ "\n",
+ "def mulliken_jaffe_electronegativity(value):\n",
+ " return {'mulliken-jaffe_electronegativity': float(value.split(' ',maxsplit=1)[0])}\n",
+ "\n",
+ "def allen_electronegativity(value):\n",
+ " return {'allen_electronegativity': float(value)}\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['', 'electronegativity']\n"
+ ]
+ }
+ ],
+ "source": [
+ "data = {}\n",
+ "path = Path.cwd() / \"data/electronegativity.csv\"\n",
+ "with open(path, newline='') as csvfile:\n",
+ " reader = csv.reader(csvfile, delimiter=',')\n",
+ " header = next(reader)\n",
+ " print(header)\n",
+ " map_transformer = { \n",
+ " 'Pauling electronegativity': pauling_electronegativity,\n",
+ " 'Sanderson electronegativity': sanderson_electronegativity,\n",
+ " 'Allred Rochow electronegativity': allred_rochow_electronegativity,\n",
+ " 'Mulliken-Jaffe electronegativity': mulliken_jaffe_electronegativity,\n",
+ " 'Allen electronegativity': allen_electronegativity,\n",
+ " }\n",
+ " for row in reader:\n",
+ " element = row[0]\n",
+ " d = json.loads(row[1].replace('\\'', '\"'))\n",
+ "\n",
+ " props = {}\n",
+ " for (prop, value) in d.items():\n",
+ " if value == '' or value == 'unknown' or value == '(no data)':\n",
+ " continue\n",
+ " props.update(map_transformer[prop](value))\n",
+ "\n",
+ " data[element] = props\n",
+ " # break \n",
+ "\n",
+ "# data\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'pauling_electronegativity': 2.2,\n",
+ " 'sanderson_electronegativity': 2.59,\n",
+ " 'allred_rochow_electronegativity': 2.2,\n",
+ " 'mulliken-jaffe_electronegativity': 2.25,\n",
+ " 'allen_electronegativity': 2.3}"
+ ]
+ },
+ "execution_count": 53,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data['hydrogen']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['', 'nat_isotopes', 'radiosotope_data', 'nmr_properties']\n"
+ ]
+ }
+ ],
+ "source": [
+ "data = {}\n",
+ "path = Path.cwd() / \"data/isotopes.csv\"\n",
+ "with open(path, newline='') as csvfile:\n",
+ " reader = csv.reader(csvfile, delimiter=',')\n",
+ " header = next(reader)\n",
+ " print(header)\n",
+ " for row in reader:\n",
+ " element = row[0]\n",
+ " props = json.loads(row[1].replace('\\'', '\"'))\n",
+ " \n",
+ " data[element] = props\n",
+ " # break \n",
+ "\n",
+ "# data\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['', 'thermo_chem']\n"
+ ]
+ }
+ ],
+ "source": [
+ "data = {}\n",
+ "path = Path.cwd() / \"data/thermo_chem.csv\"\n",
+ "with open(path, newline='') as csvfile:\n",
+ " reader = csv.reader(csvfile, delimiter=',')\n",
+ " header = next(reader)\n",
+ " print(header)\n",
+ " for row in reader:\n",
+ " element = row[0]\n",
+ " props = json.loads(row[1].replace('\\'', '\"'))\n",
+ " \n",
+ " data[element] = props\n",
+ " # break \n",
+ "\n",
+ "# data\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'Melting point': '3800 K',\n",
+ " 'Boiling point': '4300 K',\n",
+ " 'Liquid range': '500 K',\n",
+ " 'Critical temperature': '(no data) K',\n",
+ " 'Superconduction temperature': '(no data) K',\n",
+ " 'Enthalpy of fusion': '117 (per mol C atoms) kJ mol-1',\n",
+ " 'Enthalpy of vaporisation': '715 (sublimation) kJ mol-1',\n",
+ " 'Enthalpy of atomisation': '717 kJ mol-1',\n",
+ " 'Units': {'State': 'Units',\n",
+ " 'ΔfH°': 'kJ mol‑1',\n",
+ " 'ΔfG°': 'kJ mol‑1',\n",
+ " 'S°': 'J K‑1 mol‑1',\n",
+ " 'CpH': 'J K‑1 mol‑1',\n",
+ " 'H°298.15‑H°0': 'kJ mol‑1'},\n",
+ " 'Solid (graphite)': {'State': 'Solid (graphite)',\n",
+ " 'ΔfH°': '*0',\n",
+ " 'ΔfG°': '0',\n",
+ " 'S°': '*5.74 ± 0.10',\n",
+ " 'CpH': '8.53',\n",
+ " 'H°298.15‑H°0': '*1.050 ± 0.020'},\n",
+ " 'Solid (diamond)': {'State': 'Solid (diamond)',\n",
+ " 'ΔfH°': '1.897',\n",
+ " 'ΔfG°': '2.900',\n",
+ " 'S°': '2.38',\n",
+ " 'CpH': '6.115',\n",
+ " 'H°298.15‑H°0': '0.52'},\n",
+ " 'Gas': {'State': 'Gas',\n",
+ " 'ΔfH°': '*716.68 ± 0.45',\n",
+ " 'ΔfG°': '671.29',\n",
+ " 'S°': '*158.100 ± 0.003',\n",
+ " 'CpH': '20.838',\n",
+ " 'H°298.15‑H°0': '*6.536 ± 0.001'},\n",
+ " 'Gas (C2)': {'State': 'Gas (C2)',\n",
+ " 'ΔfH°': '831.9',\n",
+ " 'ΔfG°': '775.9',\n",
+ " 'S°': '199.4',\n",
+ " 'CpH': '43.2',\n",
+ " 'H°298.15‑H°0': 'no data'}}"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data['carbon']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "path_new = os.path.join(path, \"data\",\"webele\", \"cryst_struct.csv\")\n",
+ "path_new = os.path.join(path, \"data\",\"webele\", \"electronegativity.csv\")\n",
+ "path_new = os.path.join(path, \"data\",\"webele\", \"isotopes.csv\")\n",
+ "path_new = os.path.join(path, \"data\",\"webele\", \"thermo_chem.csv\")\n",
+ "\n",
+ "\n",
+ "\n",
+ "for i in range(len(df_crys_struct)):\n",
+ " a=df_crys_struct.loc[i,'cryst_struct']['Space group']\n",
+ " if a==\"\" or a==\"unknown\":\n",
+ " df_crys_struct.loc[i,'cryst_struct']['Space group']='-'\n",
+ " b=df_crys_struct.loc[i,'cryst_struct']['Space group number']\n",
+ " if b==\"\" or b==\"unknown\":\n",
+ " df_crys_struct.loc[i,'cryst_struct']['Space group number']=np.nan\n",
+ " c=df_crys_struct.loc[i,'cryst_struct']['Structure']\n",
+ " if c==\"\" or c==\"unknown\":\n",
+ " df_crys_struct.loc[i,'cryst_struct']['Structure']='-'\n",
+ "\n",
+ "\n",
+ "\n",
+ "df_crys_struct['atomic_cellparam']=''\n",
+ "df_crys_struct['atomic_cellparam'] = df_crys_struct['cryst_struct'].copy(deep=True)\n",
+ "df_crys_struct['atomic_cellparam'] = df_crys_struct['cryst_struct'].apply(dict.copy)\n",
+ "\n",
+ "\n",
+ "rem_keys= ['Space group','Space group number','Structure']\n",
+ "\n",
+ "for dictionary in df_crys_struct['atomic_cellparam']:\n",
+ " count=0\n",
+ " for key in dictionary.copy(): # avoid dictionary changed size error during iteration using .copy method\n",
+ " for k in rem_keys:\n",
+ " if key==k:\n",
+ " del dictionary[k]\n",
+ " count=count+1\n",
+ "\n",
+ "\n",
+ "\n",
+ "for i in df_crys_struct['atomic_cellparam']:\n",
+ " for k,v in i.items():\n",
+ " #print(v)\n",
+ " if v==\"\" or v==\"unknown\":\n",
+ " i[k]=np.nan\n",
+ "\n",
+ "\n",
+ "for i in df_en['electronegativity']:\n",
+ " for k,v in i.items():\n",
+ " if k=='Allen electronegativity':\n",
+ " if i[k]=='(no data)':\n",
+ " i[k]=np.nan\n",
+ " #print(k,i[k])\n",
+ " else:\n",
+ " i[k]==float(i[k])\n",
+ " #print(k,i[k])\n",
+ " if k=='Pauling electronegativity':\n",
+ " if i[k]=='(no data)':\n",
+ " i[k]=np.nan\n",
+ " #print(k,i[k])\n",
+ " else:\n",
+ " i[k]==float(i[k])\n",
+ " #print(k,i[k])\n",
+ " if k=='Allred Rochow electronegativity':\n",
+ " if i[k]=='(no data)':\n",
+ " i[k]=np.nan\n",
+ " #print(k,i[k])\n",
+ " else:\n",
+ " i[k]==float(i[k])\n",
+ " #print(k,i[k])\n",
+ " if k=='Sanderson electronegativity':\n",
+ " if i[k]=='(no data)':\n",
+ " i[k]=np.nan\n",
+ " #print(k,i[k])\n",
+ " else:\n",
+ " i[k]==float(i[k])\n",
+ " #print(k,i[k])\n",
+ " if k=='Mulliken-Jaffe electronegativity':\n",
+ " if i[k]=='(no data)':\n",
+ " i[k]=np.nan\n",
+ " #print(k,i[k])\n",
+ " else:\n",
+ " pass\n",
+ " #print(k,i[k])\n",
+ "\n",
+ "\n",
+ "# Clean isotopes data\n",
+ "\n",
+ "for i in df_iso['nat_isotopes']:\n",
+ " #print(i.values())\n",
+ " for k,v in i.items():\n",
+ " #pass\n",
+ " for key,value in v.items():\n",
+ " if value=='no data':\n",
+ " v[key]=np.nan\n",
+ "\n",
+ "for i in df_iso['radiosotope_data']:\n",
+ " #print(i.values())\n",
+ " for k,v in i.copy().items():\n",
+ " #pass\n",
+ " #print(k)\n",
+ " #print(v.values())\n",
+ " try:\n",
+ " for key,value in v.items():\n",
+ " #print(value)\n",
+ " if value=='no data':\n",
+ " v[key]=np.nan\n",
+ " except AttributeError:\n",
+ " del i[k]\n",
+ " #print(\"Hello\")\n",
+ "\n",
+ "\n",
+ "# Clean and structure nmr data\n",
+ "# Create new nmr_mod column to organize nmr data\n",
+ "\n",
+ "df_iso['nmr_mod']=\"\"\n",
+ "\n",
+ "# Create structured nmr_mod named python dict to store nmr data systematically\n",
+ "nmr_mod={}\n",
+ "for i in df_Webele.index:\n",
+ " nmr_mod.update({i:{}})\n",
+ "\n",
+ "# Store isotope names in a list and remove missing data values from df_iso['nmr_properties'] dataframe\n",
+ "\n",
+ "Keys= list(df_iso.loc[0,'nmr_properties'].keys())\n",
+ "for k in Keys:\n",
+ " for i in df_iso['nmr_properties']:\n",
+ " for ele in i[k]:\n",
+ " try:\n",
+ " while ele == 'no data':\n",
+ " i[k].remove(ele)\n",
+ " while ele == 'none':\n",
+ " i[k].remove(ele)\n",
+ " except ValueError:\n",
+ " pass\n",
+ "\n",
+ "#Create dictionary keys\n",
+ "for row,col in df_iso.iterrows():\n",
+ " for k in nmr_mod.keys():\n",
+ " if col[0]==k:\n",
+ " #for ele in range(len(col[3]['Isotope'])):\n",
+ " for nam in col[3]['Isotope']:\n",
+ " nmr_mod[k].update({'Isotope_{}'.format(nam):{}})\n",
+ " row=row+1\n",
+ "\n",
+ "\n",
+ "\n",
+ "#Create nested dictionary keys inside nmr_data dictionary\n",
+ "for row,col in df_iso.iterrows():\n",
+ " for k in nmr_mod.keys():\n",
+ " if col[0]==k:\n",
+ " for iso in nmr_mod[k].keys():\n",
+ " for nam in col[3]['Natural abundance /%']:\n",
+ " nmr_mod[k][iso].update({'Natural abundance %':\"\",\n",
+ " \"Spin (I)\":\"\",\n",
+ " 'Frequency relative to 1H = 100 (MHz)':\"\",\n",
+ " 'Receptivity_DP_relative_to_1H = 1.00':\"\",\n",
+ " 'Receptivity_DC_relative_to_13C = 1.00':\"\",\n",
+ " 'Magnetogyric_ratio (γ)':\"\",\n",
+ " 'Magnetic_moment μ (μN)':\"\",\n",
+ " 'Nuclear_quad_mom (Q/millibarn)':\"\",\n",
+ " 'Line_width_factor 1056 l (m4)':\"\"})\n",
+ " row=row+1\n",
+ "\n",
+ "# Finally fill data in the dictionary\n",
+ "for row,col in df_iso.iterrows():\n",
+ " for k in nmr_mod.keys():\n",
+ " if col[0]==k:\n",
+ " ind=0\n",
+ " for iso in nmr_mod[k].keys():\n",
+ " try:\n",
+ " nmr_mod[k][iso].update({'Natural abundance %':col[3]['Natural abundance /%'][ind]})\n",
+ " nmr_mod[k][iso].update({'Spin (I)':col[3]['Spin (I)'][ind]})\n",
+ " nmr_mod[k][iso].update({'Frequency relative to 1H = 100 (MHz)':col[3]['Frequency relative to 1H = 100 (MHz)'][ind]})\n",
+ " nmr_mod[k][iso].update({'Receptivity_DP_relative_to_1H = 1.00':col[3]['Receptivity, DP, relative to 1H = 1.00'][ind]})\n",
+ " nmr_mod[k][iso].update({'Receptivity_DC_relative_to_13C = 1.00':col[3]['Receptivity, DC, relative to 13C = 1.00'][ind]})\n",
+ " nmr_mod[k][iso].update({'Magnetogyric_ratio (γ)':col[3]['Magnetogyric ratio, γ (107 rad\\xa0T‑1\\xa0s-1)'][ind]})\n",
+ " nmr_mod[k][iso].update({'Magnetic_moment μ (μN)':col[3]['Magnetic moment, μ (μN)'][ind]})\n",
+ " nmr_mod[k][iso].update({'Nuclear_quad_mom (Q/millibarn)':col[3]['Nuclear quadrupole moment, Q/millibarn'][ind]})\n",
+ " nmr_mod[k][iso].update({'Line_width_factor 1056 l (m4)':col[3]['Line width factor, 1056 l (m4)'][ind]})\n",
+ " ind=ind+1\n",
+ " row=row+1\n",
+ " except IndexError:\n",
+ " pass\n",
+ "\n",
+ "\n",
+ "\n",
+ "# correcting erroneous data filled for some elements in nmr_mod dictionary manually.\n",
+ "\n",
+ "nmr_mod['carbon']['Isotope_11C'].update({'Natural abundance %':\"\",\n",
+ " \"Spin (I)\":\"\",\n",
+ " 'Frequency relative to 1H = 100 (MHz)':\"\",\n",
+ " 'Receptivity_DP_relative_to_1H = 1.00':\"\",\n",
+ " 'Receptivity_DC_relative_to_13C = 1.00':\"\",\n",
+ " 'Magnetogyric_ratio (γ)':\"\",\n",
+ " 'Magnetic_moment μ (μN)':\"\",\n",
+ " 'Nuclear_quad_mom (Q/millibarn)':'33.27(24)',\n",
+ " 'Line_width_factor 1056 l (m4)':\"\"})\n",
+ "\n",
+ "nmr_mod['carbon']['Isotope_13C'].update({'Natural abundance %':1.07,\n",
+ " \"Spin (I)\":\"1/2\",\n",
+ " 'Frequency relative to 1H = 100 (MHz)':25.145020,\n",
+ " 'Receptivity_DP_relative_to_1H = 1.00':0.000170,\n",
+ " 'Receptivity_DC_relative_to_13C = 1.00':1.00,\n",
+ " 'Magnetogyric_ratio (γ)':6.728284,\n",
+ " 'Magnetic_moment μ (μN)':1.216613,\n",
+ " 'Nuclear_quad_mom (Q/millibarn)':'-',\n",
+ " 'Line_width_factor 1056 l (m4)':\"-\"})\n",
+ "\n",
+ "nmr_mod['calcium']['Isotope_41Ca'].update({'Natural abundance %':\"\",\n",
+ " \"Spin (I)\":\"\",\n",
+ " 'Frequency relative to 1H = 100 (MHz)':\"\",\n",
+ " 'Receptivity_DP_relative_to_1H = 1.00':\"\",\n",
+ " 'Receptivity_DC_relative_to_13C = 1.00':\"\",\n",
+ " 'Magnetogyric_ratio (γ)':\"\",\n",
+ " 'Magnetic_moment μ (μN)':\"\",\n",
+ " 'Nuclear_quad_mom (Q/millibarn)':'-40.8(8)',\n",
+ " 'Line_width_factor 1056 l (m4)':\"\"})\n",
+ "\n",
+ "nmr_mod['calcium']['Isotope_43Ca'].update({'Natural abundance %': '0.135',\n",
+ " 'Spin (I)': '7/2',\n",
+ " 'Frequency relative to 1H = 100 (MHz)': '6.730029',\n",
+ " 'Receptivity_DP_relative_to_1H = 1.00': '0.00000868',\n",
+ " 'Receptivity_DC_relative_to_13C = 1.00': '0.0510',\n",
+ " 'Magnetogyric_ratio (γ)': '-1.803069',\n",
+ " 'Magnetic_moment μ (μN)': '-1.494067',\n",
+ " 'Nuclear_quad_mom (Q/millibarn)': '-4.08',\n",
+ " 'Line_width_factor 1056 l (m4)': '2.3'})\n",
+ "\n",
+ "nmr_mod['krypton']['Isotope_85Kr'].update({'Natural abundance %':\"\",\n",
+ " \"Spin (I)\":\"\",\n",
+ " 'Frequency relative to 1H = 100 (MHz)':\"\",\n",
+ " 'Receptivity_DP_relative_to_1H = 1.00':\"\",\n",
+ " 'Receptivity_DC_relative_to_13C = 1.00':\"\",\n",
+ " 'Magnetogyric_ratio (γ)':\"\",\n",
+ " 'Magnetic_moment μ (μN)':\"\",\n",
+ " 'Nuclear_quad_mom (Q/millibarn)':'507(3)',\n",
+ " 'Line_width_factor 1056 l (m4)':\"\"})\n",
+ "\n",
+ "nmr_mod['iodine']['Isotope_129I'].update({'Natural abundance %':\"\",\n",
+ " \"Spin (I)\":\"\",\n",
+ " 'Frequency relative to 1H = 100 (MHz)':\"\",\n",
+ " 'Receptivity_DP_relative_to_1H = 1.00':\"\",\n",
+ " 'Receptivity_DC_relative_to_13C = 1.00':\"\",\n",
+ " 'Magnetogyric_ratio (γ)':\"\",\n",
+ " 'Magnetic_moment μ (μN)':\"\",\n",
+ " 'Nuclear_quad_mom (Q/millibarn)':'-604(10)',\n",
+ " 'Line_width_factor 1056 l (m4)':\"\"})\n",
+ "\n",
+ "nmr_mod['lead']['Isotope_207Pb'].update({'Natural abundance %': '22.1',\n",
+ " 'Spin (I)': '1/2',\n",
+ " 'Frequency relative to 1H = 100 (MHz)': '20.920597',\n",
+ " 'Receptivity_DP_relative_to_1H = 1.00': '0.00201',\n",
+ " 'Receptivity_DC_relative_to_13C = 1.00': '11.5',\n",
+ " 'Magnetogyric_ratio (γ)': '5.58046',\n",
+ " 'Magnetic_moment μ (μN)': '1.00906',\n",
+ " 'Nuclear_quad_mom (Q/millibarn)': '-269(165)',\n",
+ " 'Line_width_factor 1056 l (m4)': '-'})\n",
+ "\n",
+ "nmr_mod['plutonium']['Isotope_239Pu'].update({'Natural abundance %': '0',\n",
+ " 'Spin (I)': '1/2',\n",
+ " 'Frequency relative to 1H = 100 (MHz)': '3.63',\n",
+ " 'Receptivity_DP_relative_to_1H = 1.00': '-',\n",
+ " 'Receptivity_DC_relative_to_13C = 1.00': '-',\n",
+ " 'Magnetogyric_ratio (γ)': '0.972',\n",
+ " 'Magnetic_moment μ (μN)': '',\n",
+ " 'Nuclear_quad_mom (Q/millibarn)': '-3345(13) [Mössbauer state]',\n",
+ " 'Line_width_factor 1056 l (m4)': ''})\n",
+ "\n",
+ "nmr_mod['plutonium']['Isotope_241Pu'].update({'Natural abundance %': '',\n",
+ " 'Spin (I)': '',\n",
+ " 'Frequency relative to 1H = 100 (MHz)': '',\n",
+ " 'Receptivity_DP_relative_to_1H = 1.00': '',\n",
+ " 'Receptivity_DC_relative_to_13C = 1.00': '',\n",
+ " 'Magnetogyric_ratio (γ)': '',\n",
+ " 'Magnetic_moment μ (μN)': '',\n",
+ " 'Nuclear_quad_mom (Q/millibarn)': '5600(200)',\n",
+ " 'Line_width_factor 1056 l (m4)': ''})\n",
+ "\n",
+ "nmr_mod['rhodium']['Isotope_100Rh'].update({'Natural abundance %': '',\n",
+ " 'Spin (I)': '',\n",
+ " 'Frequency relative to 1H = 100 (MHz)': '',\n",
+ " 'Receptivity_DP_relative_to_1H = 1.00': '',\n",
+ " 'Receptivity_DC_relative_to_13C = 1.00': '',\n",
+ " 'Magnetogyric_ratio (γ)': '',\n",
+ " 'Magnetic_moment μ (μN)': '',\n",
+ " 'Nuclear_quad_mom (Q/millibarn)': '153 (excited nuclear state)',\n",
+ " 'Line_width_factor 1056 l (m4)': ''})\n",
+ "\n",
+ "nmr_mod['rhodium']['Isotope_103Rh'].update({'Natural abundance %': '100',\n",
+ " 'Spin (I)': '1/2',\n",
+ " 'Frequency relative to 1H = 100 (MHz)': '3.172310',\n",
+ " 'Receptivity_DP_relative_to_1H = 1.00': '0.0000317',\n",
+ " 'Receptivity_DC_relative_to_13C = 1.00': '0.181',\n",
+ " 'Magnetogyric_ratio (γ)': '-0.8468',\n",
+ " 'Magnetic_moment μ (μN)': '-0.1531',\n",
+ " 'Nuclear_quad_mom (Q/millibarn)': '-',\n",
+ " 'Line_width_factor 1056 l (m4)': '-'})\n",
+ "\n",
+ "nmr_mod['tungsten']['Isotope_182W'].update({'Natural abundance %': '',\n",
+ " 'Spin (I)': '',\n",
+ " 'Frequency relative to 1H = 100 (MHz)': '',\n",
+ " 'Receptivity_DP_relative_to_1H = 1.00': '',\n",
+ " 'Receptivity_DC_relative_to_13C = 1.00': '',\n",
+ " 'Magnetogyric_ratio (γ)': '',\n",
+ " 'Magnetic_moment μ (μN)': '',\n",
+ " 'Nuclear_quad_mom (Q/millibarn)': '-2130(350) [Mössbauer state]',\n",
+ " 'Line_width_factor 1056 l (m4)': ''})\n",
+ "\n",
+ "nmr_mod['tungsten']['Isotope_183W'].update({'Natural abundance %': '14.3',\n",
+ " 'Spin (I)': '1/2',\n",
+ " 'Frequency relative to 1H = 100 (MHz)': '4.166398',\n",
+ " 'Receptivity_DP_relative_to_1H = 1.00': '0.0000107',\n",
+ " 'Receptivity_DC_relative_to_13C = 1.00': '0.0613',\n",
+ " 'Magnetogyric_ratio (γ)': '1.1282403',\n",
+ " 'Magnetic_moment μ (μN)': '0.20400919',\n",
+ " 'Nuclear_quad_mom (Q/millibarn)': '',\n",
+ " 'Line_width_factor 1056 l (m4)': '-'})\n",
+ "\n",
+ "nmr_mod['uranium']['Isotope_233U'].update({'Natural abundance %': '',\n",
+ " 'Spin (I)': '',\n",
+ " 'Frequency relative to 1H = 100 (MHz)': '',\n",
+ " 'Receptivity_DP_relative_to_1H = 1.00': '',\n",
+ " 'Receptivity_DC_relative_to_13C = 1.00': '',\n",
+ " 'Magnetogyric_ratio (γ)': '',\n",
+ " 'Magnetic_moment μ (μN)': '',\n",
+ " 'Nuclear_quad_mom (Q/millibarn)': '3663(8)',\n",
+ " 'Line_width_factor 1056 l (m4)': ''})\n",
+ "\n",
+ "nmr_mod['uranium']['Isotope_235U'].update({'Natural abundance %': '0.7200',\n",
+ " 'Spin (I)': '7/2',\n",
+ " 'Frequency relative to 1H = 100 (MHz)': '1.841000',\n",
+ " 'Receptivity_DP_relative_to_1H = 1.00': '-',\n",
+ " 'Receptivity_DC_relative_to_13C = 1.00': '-',\n",
+ " 'Magnetogyric_ratio (γ)': '-0.52',\n",
+ " 'Magnetic_moment μ (μN)': '-0.43',\n",
+ " 'Nuclear_quad_mom (Q/millibarn)': '4936(6)',\n",
+ " 'Line_width_factor 1056 l (m4)': '-'})\n",
+ "\n",
+ "# Lastly add the structured dict into df_iso dataframe\n",
+ "\n",
+ "df_iso['nmr_mod'] = nmr_mod.values()\n",
+ "\n",
+ "\n",
+ "\n",
+ "# Clean thermochemical data\n",
+ "rem_keys= ['Units']\n",
+ "for i in df_therm['thermo_chem']:\n",
+ " for key in i.copy(): # avoid dictionary changed size error during iteration using .copy method\n",
+ " for k in rem_keys:\n",
+ " if key==k:\n",
+ " del i[k]\n",
+ "\n",
+ "#dict keys whose values needs to be cleaned\n",
+ "mod_key=['Melting point', 'Boiling point', 'Liquid range', 'Critical temperature',\n",
+ " 'Superconduction temperature', 'Enthalpy of fusion', 'Enthalpy of vaporisation', 'Enthalpy of atomisation']\n",
+ "\n",
+ "for i in range(len(df_therm)):\n",
+ " a=df_therm.loc[i,'thermo_chem']\n",
+ " for k in mod_key:\n",
+ " for a in df_therm.loc[i,'thermo_chem'].keys():\n",
+ " if a==k and i<118:\n",
+ " temp=df_therm.loc[i,'thermo_chem'][k]\n",
+ " df_therm.loc[i,'thermo_chem'][k]=re.sub(r\"(\\(.*?\\))|(-)|( kJ mol-1)|( K)|(about )|(maybe about)\", '', temp)\n",
+ " if df_therm.loc[i,'thermo_chem'][k]==\"\":\n",
+ " df_therm.loc[i,'thermo_chem'][k]=np.nan\n",
+ " else:\n",
+ " df_therm.loc[i,'thermo_chem'][k]=float(df_therm.loc[i,'thermo_chem'][k])\n",
+ "\n",
+ "\n",
+ "# Seperate and store thermdata in another column \"atomic_thermdata\" of df_therm dataframe.\n",
+ "df_therm['atomic_thermdata']=''\n",
+ "df_therm['atomic_thermdata'] = df_therm['thermo_chem'].copy(deep=True)\n",
+ "df_therm['atomic_thermdata'] = df_therm['thermo_chem'].apply(dict.copy)\n",
+ "\n",
+ "\n",
+ "rem_keys= ['Melting point', 'Boiling point', 'Liquid range', 'Critical temperature',\n",
+ " 'Superconduction temperature', 'Enthalpy of fusion', 'Enthalpy of vaporisation', 'Enthalpy of atomisation']\n",
+ "\n",
+ "for dictionary in df_therm['atomic_thermdata']:\n",
+ " count=0\n",
+ " for key in dictionary.copy(): # avoid dictionary changed size error during iteration using .copy method\n",
+ " for k in rem_keys:\n",
+ " if key==k:\n",
+ " del dictionary[k]\n",
+ " count=count+1\n",
+ "\n",
+ "for i in range(len(df_therm)):\n",
+ " for k,v in df_therm.loc[i,'atomic_thermdata'].items():\n",
+ " for key,val in v.items():\n",
+ " temp=val\n",
+ " val=re.sub(r\"(\\*)|(no data)|(not known)\", '', temp)\n",
+ " v[key]=val\n",
+ " if val==\"\":\n",
+ " v[key]=np.nan\n",
+ "\n",
+ "for i in range(len(df_therm)):\n",
+ " for k,v in df_therm.loc[i,'atomic_thermdata'].items():\n",
+ " for key in v.copy():\n",
+ " if k==v[key]:\n",
+ " del v[key]\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "method_list\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "count = 0\n",
+ "for obj in objs:\n",
+ " obj.section_webele_metadata.source = 'Webelements'\n",
+ " obj.section_webele_metadata.atomic_spin_setting = 'NA'\n",
+ " obj.section_webele_metadata.atomic_method = 'NA'\n",
+ " obj.section_webele_metadata.atomic_basis_set = 'NA'\n",
+ " obj.density_of_solid = float(df_Webele.iloc[count,df_Webele.columns.get_loc(\"Density_of_solid\")])\n",
+ " obj.youngs_mod = float(df_Webele.iloc[count,df_Webele.columns.get_loc(\"Youngs_mod\")])\n",
+ " obj.rigidity_mod = float(df_Webele.iloc[count,df_Webele.columns.get_loc(\"Rigidity_mod\")])\n",
+ " obj.bulk_mod = float(df_Webele.iloc[count,df_Webele.columns.get_loc(\"Bulk_mod\")])\n",
+ " obj.poissons_ratio = float(df_Webele.iloc[count,df_Webele.columns.get_loc(\"Poissons_ratio\")])\n",
+ " obj.mineral_hardness = float(df_Webele.iloc[count,df_Webele.columns.get_loc(\"Mineral_hardness\")])\n",
+ " obj.brinell_hardness = float(df_Webele.iloc[count,df_Webele.columns.get_loc(\"Brinell_hardness\")])\n",
+ " obj.vickers_hardness = float(df_Webele.iloc[count,df_Webele.columns.get_loc(\"Vickers_hardness\")])\n",
+ " obj.electrical_resist = float(df_Webele.iloc[count,df_Webele.columns.get_loc(\"Electrical_resistivity\")])\n",
+ " obj.thermal_conduct = float(df_Webele.iloc[count,df_Webele.columns.get_loc(\"Thermal_conductivity\")])\n",
+ " obj.coeff_of_lte = float(df_Webele.iloc[count,df_Webele.columns.get_loc(\"Coeff_of_lte\")])\n",
+ " obj.reflectivity = float(df_Webele.iloc[count,df_Webele.columns.get_loc(\"Reflectivity\")])\n",
+ " obj.refract_index = float(df_Webele.iloc[count,df_Webele.columns.get_loc(\"Refractive_index\")])\n",
+ " obj.vel_of_sound = float(df_Webele.iloc[count,df_Webele.columns.get_loc(\"Velocity_of_sound\")])\n",
+ " obj.atomic_ebe = df_Webele.iloc[count,df_Webele.columns.get_loc(\"Electron_bin_En\")]\n",
+ " obj.atomic_enc = df_Webele.iloc[count,df_Webele.columns.get_loc(\"Eff_Nuc_Char\")]\n",
+ " obj.atomic_ie = df_Webele.iloc[count,df_Webele.columns.get_loc(\"Ionisation_En\")]\n",
+ " obj.atomic_ea = float(df_Webele.iloc[count,df_Webele.columns.get_loc(\"Electron_aff\")])\n",
+ " try:\n",
+ " obj.atomic_radius_calculated = float(df_atomic_sizes.iloc[count,df_atomic_sizes.columns.\n",
+ " get_loc(\"atom_radii\")]\n",
+ " ['Atomic radius (calculated)'])\n",
+ " except ValueError:\n",
+ " obj.atomic_radius_calculated = np.nan\n",
+ "\n",
+ " try:\n",
+ " obj.atomic_radius = float(df_atomic_sizes.iloc[count,df_atomic_sizes.columns.get_loc(\"atom_radii\")]\n",
+ " ['Atomic radius (empirical)'])\n",
+ " except ValueError:\n",
+ " obj.atomic_radius = np.nan\n",
+ "\n",
+ " try:\n",
+ " obj.covalent_rad = float(df_atomic_sizes.iloc[count,df_atomic_sizes.columns.get_loc(\"atom_radii\")]\n",
+ " ['Covalent radius (2008 values)'])\n",
+ " except ValueError:\n",
+ " obj.covalent_rad = np.nan\n",
+ "\n",
+ " try:\n",
+ " obj.covalent_rad_emp = float(df_atomic_sizes.iloc[count,df_atomic_sizes.columns.get_loc(\"atom_radii\")]\n",
+ " ['Covalent radius (empirical)'])\n",
+ " except ValueError:\n",
+ " obj.covalent_rad_emp = np.nan\n",
+ "\n",
+ " try:\n",
+ " obj.van_der_waals_rad =float(df_atomic_sizes.iloc[count,df_atomic_sizes.columns.get_loc(\"atom_radii\")]\n",
+ " ['van der Waals radius'])\n",
+ " except ValueError:\n",
+ " obj.van_der_waals_rad = np.nan\n",
+ "\n",
+ " obj.covalent_radius_mol = df_as_copy.iloc[count,df_as_copy.columns.get_loc(\"atom_radii\")]\n",
+ " obj.atomic_orbital_radii = df_atomic_sizes.iloc[count,df_atomic_sizes.columns.get_loc(\"orbital_radii\")]\n",
+ " obj.atomic_ionic_radii = df_atomic_sizes.iloc[count,df_atomic_sizes.columns.get_loc(\"ionic_radii\")]\n",
+ " obj.paul_ionic_radii = df_atomic_sizes.iloc[count,df_atomic_sizes.columns.get_loc(\"paul_ionic_radii\")]\n",
+ "\n",
+ " obj.atomic_spacegroup = df_crys_struct.iloc[count,df_crys_struct.columns.get_loc(\"cryst_struct\")]['Space group']\n",
+ " obj.atomic_spacegroupnum= float(df_crys_struct.iloc[count,df_crys_struct.columns.get_loc(\"cryst_struct\")]\n",
+ " ['Space group number'])\n",
+ " obj.atomic_structure = df_crys_struct.iloc[count,df_crys_struct.columns.get_loc(\"cryst_struct\")]['Structure']\n",
+ " obj.atomic_cellparam = df_crys_struct.iloc[count,df_crys_struct.columns.get_loc(\"atomic_cellparam\")]\n",
+ "\n",
+ " obj.atomic_en =df_en.iloc[count,df_en.columns.get_loc(\"electronegativity\")]\n",
+ " obj.atomic_en_allen = float(df_en.iloc[count,df_en.columns.get_loc(\"electronegativity\")]\n",
+ " ['Allen electronegativity'])\n",
+ " obj.atomic_en_paul = float(df_en.iloc[count,df_en.columns.get_loc(\"electronegativity\")]\n",
+ " ['Pauling electronegativity'])\n",
+ " obj.atomic_en_allredroch = float(df_en.iloc[count,df_en.columns.get_loc(\"electronegativity\")]\n",
+ " ['Allred Rochow electronegativity'])\n",
+ " obj.atomic_en_sanderson = float(df_en.iloc[count,df_en.columns.get_loc(\"electronegativity\")]\n",
+ " ['Sanderson electronegativity'])\n",
+ "\n",
+ " obj.atomic_nat_iso = df_iso.iloc[count,df_iso.columns.get_loc(\"nat_isotopes\")]\n",
+ " obj.atomic_rad_iso = df_iso.iloc[count,df_iso.columns.get_loc(\"radiosotope_data\")]\n",
+ " obj.atomic_nmr = df_iso.iloc[count,df_iso.columns.get_loc(\"nmr_mod\")]\n",
+ "\n",
+ " obj.boiling_point = float(df_therm.iloc[count,df_therm.columns.get_loc(\"thermo_chem\")]\n",
+ " ['Boiling point'])\n",
+ " obj.supercond_temp = float(df_therm.iloc[count,df_therm.columns.get_loc(\"thermo_chem\")]\n",
+ " ['Superconduction temperature'])\n",
+ " obj.melting_point = float(df_therm.iloc[count,df_therm.columns.get_loc(\"thermo_chem\")]\n",
+ " ['Melting point'])\n",
+ " obj.liquid_range = float(df_therm.iloc[count,df_therm.columns.get_loc(\"thermo_chem\")]\n",
+ " ['Liquid range'])\n",
+ " obj.critical_temperature = float(df_therm.iloc[count,df_therm.columns.get_loc(\"thermo_chem\")]\n",
+ " ['Critical temperature'])\n",
+ " obj.atomic_hfu = float(df_therm.iloc[count,df_therm.columns.get_loc(\"thermo_chem\")]\n",
+ " ['Enthalpy of fusion'])\n",
+ " obj.atomic_hvap = float(df_therm.iloc[count,df_therm.columns.get_loc(\"thermo_chem\")]\n",
+ " ['Enthalpy of vaporisation'])\n",
+ " obj.atomic_hatm = float(df_therm.iloc[count,df_therm.columns.get_loc(\"thermo_chem\")]\n",
+ " ['Enthalpy of atomisation'])\n",
+ " obj.atomic_thermdata = df_therm.iloc[count,df_therm.columns.get_loc(\"atomic_thermdata\")]\n",
+ " count+=1\n",
+ "\n",
+ "atomic_element_symbol = df_Webele['Element_symbol'].values\n",
+ "for count, symbol in enumerate(atomic_element_symbol):\n",
+ " globals()[symbol] = objs[count]\n",
+ "\n",
+ "\n",
+ "df_Webele.rename_axis('',inplace=True)\n",
+ "df_comb = df_Webele.copy(deep=True)\n",
+ "df_comb['atom_radii']= df_atomic_sizes['atom_radii'].values\n",
+ "df_comb['orbital_radii']= df_atomic_sizes['orbital_radii'].values\n",
+ "df_comb['ionic_radii']= df_atomic_sizes['ionic_radii'].values\n",
+ "df_comb['covalent_radius_mol']= df_as_copy['atom_radii'].values\n",
+ "df_comb['paul_ionic_radii']= df_atomic_sizes['paul_ionic_radii'].values\n",
+ "df_comb['nat_isotopes']= df_iso['nat_isotopes'].values\n",
+ "df_comb['radiosotope_data']= df_iso['radiosotope_data'].values\n",
+ "df_comb['nmr_mod']= df_iso['nmr_mod'].values\n",
+ "df_comb['electronegativity']= df_en['electronegativity'].values\n",
+ "df_comb['cryst_struct']= df_crys_struct['cryst_struct'].values\n",
+ "df_comb['atomic_cellparam']= df_crys_struct['atomic_cellparam'].values\n",
+ "df_comb['thermo_chem']= df_therm['thermo_chem'].values\n",
+ "df_comb['atomic_thermdata']= df_therm['atomic_thermdata'].values\n",
+ "\n",
+ "\n",
+ "\n",
+ "def definition(abc):\n",
+ " '''\n",
+ " This function can be used to access quantity definitions for each element features accessible\n",
+ " '''\n",
+ " if hasattr(atomic_properties_webele,abc):\n",
+ " return print(getattr(atomic_properties_webele,abc).__doc__)\n",
+ " elif hasattr(metadata,abc):\n",
+ " return print(getattr(metadata,abc).__doc__)\n",
+ " else:\n",
+ " return print('Check the input feature name. Either it is wrong or requested feature does not exist from this webelements source')\n",
+ "\n",
+ "\n",
+ "def symbol(abc):\n",
+ " '''\n",
+ "This function utilty is, it can be called to access element properties based on its symbol in periodic table. Can be usefull if want to acess particular property of multiple elements at once\n",
+ " '''\n",
+ " return globals()[abc]\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": ".venv",
+ "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": "3.9.16"
+ },
+ "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/utils/webelements/webelements_scrape.ipynb b/utils/webelements/webelements_scrape.ipynb
new file mode 100644
index 0000000..229811b
--- /dev/null
+++ b/utils/webelements/webelements_scrape.ipynb
@@ -0,0 +1,227 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import requests\n",
+ "from bs4 import BeautifulSoup\n",
+ "\n",
+ "URL = \"https://www.webelements.com/hydrogen/physics.html\"\n",
+ "page = requests.get(URL)\n",
+ "\n",
+ "soup = BeautifulSoup(page.content, \"html.parser\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<main>\n",
+ "<div class=\"pure-g\">\n",
+ "<div class=\"pure-u-1 pure-u-sm-1-1\">\n",
+ "<h1 class=\"s\">Hydrogen - <sub><small>1</small></sub>H: physical properties</h1>\n",
+ "<div class=\"pure-menu pure-menu-horizontal pure-menu-scrollable\">\n",
+ "<ul class=\"pure-menu-list z_menu\">\n",
+ "<li class=\"pure-menu-item s\"><a class=\"pure-menu-link\" href=\"../hydrogen/\">▸▸ H Essentials</a></li>\n",
+ "<li class=\"pure-menu-item s\"><a class=\"pure-menu-link\" href=\"../hydrogen/physics.html\">Physical properties</a></li>\n",
+ "<li class=\"pure-menu-item s\"><a class=\"pure-menu-link\" href=\"../hydrogen/atoms.html\">Electron shell data</a></li>\n",
+ "<li class=\"pure-menu-item s\"><a class=\"pure-menu-link\" href=\"../hydrogen/atom_sizes.html\">Atom sizes</a></li>\n",
+ "<li class=\"pure-menu-item s\"><a class=\"pure-menu-link\" href=\"../hydrogen/electronegativity.html\">Electronegativity</a></li>\n",
+ "<li class=\"pure-menu-item s\"><a class=\"pure-menu-link\" href=\"../hydrogen/isotopes.html\">Isotopes and NMR</a></li>\n",
+ "<li class=\"pure-menu-item s\"><a class=\"pure-menu-link\" href=\"../hydrogen/crystal_structure.html\">Crystal structure</a></li>\n",
+ "<li class=\"pure-menu-item s\"><a class=\"pure-menu-link\" href=\"../hydrogen/thermochemistry.html\">Thermochemistry</a></li>\n",
+ "<li class=\"pure-menu-item s\"><a class=\"pure-menu-link\" href=\"../hydrogen/history.html\">History</a></li>\n",
+ "<li class=\"pure-menu-item s\"><a class=\"pure-menu-link\" href=\"../hydrogen/uses.html\">Uses</a></li>\n",
+ "<li class=\"pure-menu-item s\"><a class=\"pure-menu-link\" href=\"../hydrogen/geology.html\">Geology</a></li>\n",
+ "<li class=\"pure-menu-item s\"><a class=\"pure-menu-link\" href=\"../hydrogen/biology.html\">Biology</a></li>\n",
+ "<li class=\"pure-menu-item s\"><a class=\"pure-menu-link\" href=\"../hydrogen/compounds.html\">Binary compounds</a></li>\n",
+ "<li class=\"pure-menu-item s\"><a class=\"pure-menu-link\" href=\"../hydrogen/compound_properties.html\">Compound properties</a></li>\n",
+ "<li class=\"pure-menu-item s\"><a class=\"pure-menu-link\" href=\"../hydrogen/chemistry.html\">Element reactions</a></li>\n",
+ "<li class=\"pure-menu-item s\"><a class=\"pure-menu-link\" href=\"../hydrogen/contents.html\">List all H properties</a></li>\n",
+ "</ul>\n",
+ "</div>\n",
+ "</div>\n",
+ "</div>\n",
+ "<p class=\"p_first\">Under ambient conditions, hydrogen is a colourless highly flammable diatomic gas with the molecular formula H<sub>2</sub>. Hydrogen gas has no small or taste. It is possible to make liquid hydrogen at atmospheric pressure by cooling it to below 20.28 K (-252.87 °C). Liquid hydrogen can be stored in insulated containers under pressure. Solid hydrogen may be made by cooling below the melting point of 14.01 K (-259.14 °C) and has a low density 88 kg m<sup>-3</sup> (0.088 g cm<sup>-3</sup>). Hydrogen is not an electrical conductor. It may be that a liquid metallic hydrogen is present in the highly compressed interiors of Jupiter and Saturn where pressures are in the millions of atmospheres. </p>\n",
+ "<h2 class=\"s\">Density properties</h2>\n",
+ "<ul class=\"spark_table_list\">\n",
+ "<li><a href=\"../periodicity/density/\" title=\"view definition and images showing periodicity for density of elemental solids\"><img alt=\"density of the chemical elements displayed on a miniature periodic table\" src=\"../_media/periodicity/tables/sparktables2/density.png\">Density of solid</img></a>: 88 kg m<sup>‑3</sup></li>\n",
+ "<li><a href=\"../periodicity/molar_volume/\" title=\"view definition and images showing periodicity for molar volumes of the elements\"><img alt=\"molar volume of the chemical elements displayed on a miniature periodic table\" src=\"../_media/periodicity/tables/sparktables2/molar_volume.png\"/>Molar volume</a>: 11.42 cm<sup>3</sup></li>\n",
+ "</ul>\n",
+ "<h2 class=\"s\">Elastic properties</h2>\n",
+ "<ul class=\"spark_table_list\">\n",
+ "<li><a href=\"../periodicity/youngs_modulus/\" title=\"view definition and images showing periodicity of the Young's modulus for the elements\"><img alt=\"Young's modulus of the chemical elements displayed on a miniature periodic table\" src=\"../_media/periodicity/tables/sparktables2/youngs_modulus.png\"/>Young's modulus</a>: (no data) GPa</li>\n",
+ "<li><a href=\"../periodicity/rigidity_modulus/\" title=\"view definition and images showing periodicity of the rigidity modulus for the elements\"><img alt=\"Rigidity modulus of the chemical elements displayed on a miniature periodic table\" src=\"../_media/periodicity/tables/sparktables2/rigidity_modulus.png\"/>Rigidity modulus</a>: (no data) GPa</li>\n",
+ "<li><a href=\"../periodicity/bulk_modulus/\" title=\"view definition and images showing periodicity of bulk modulus for the elements\"><img alt=\"bulk modulus of the chemical elements displayed on a miniature periodic table\" src=\"../_media/periodicity/tables/sparktables2/bulk_modulus.png\"/>Bulk modulus</a>: (no data) GPa</li>\n",
+ "<li><a href=\"../periodicity/poissons_ratio/\" title=\"view definition and images showing periodicity of the Poisson ratio for the elements\"><img alt=\"Poisson's ratio of the chemical elements displayed on a miniature periodic table\" src=\"../_media/periodicity/tables/sparktables2/poissons_ratio.png\"/>Poisson's ratio</a>: (no data) (no units)</li>\n",
+ "</ul>\n",
+ "<h2 class=\"s\">Hardnesses</h2>\n",
+ "<ul class=\"spark_table_list\">\n",
+ "<li><a href=\"../periodicity/hardness_mineral/\" title=\"view definition and images showing periodicity of mineral hardness for the elements\"><img alt=\"mineral hardness of the chemical elements displayed on a miniature periodic table\" src=\"../_media/periodicity/tables/sparktables2/hardness_mineral.png\"/>Mineral hardness</a>: (no data) (no units)</li>\n",
+ "<li><a href=\"../periodicity/hardness_brinell/\" title=\"view definition and images showing periodicity of Brinell hardness for the elements\"><img alt=\"Brinell hardness of the chemical elements displayed on a miniature periodic table\" src=\"../_media/periodicity/tables/sparktables2/hardness_brinell.png\"/>Brinell hardness</a>: (no data) MN m<sup>-2</sup></li>\n",
+ "<li><a href=\"../periodicity/hardness_vickers/\" title=\"view definition and images showing periodicity of Vickers hardness for the elements\"><img alt=\"Vickers hardness of the chemical elements displayed on a miniature periodic table\" src=\"../_media/periodicity/tables/sparktables2/hardness_vickers.png\"/>Vickers hardness</a>: (no data) MN m<sup>-2</sup></li>\n",
+ "</ul>\n",
+ "<div><figure><img alt=\"Heatscape representing the density of the elements\" class=\"pure-img\" src=\"../_media/periodicity/tables/periodic_table_heatscape_white/density_heat_w.png\"/><figcaption>Heatscape picture representing the density of the periodic table elements.</figcaption></figure></div>\n",
+ "<div><figure><img alt=\"Heatscape representing the molar volume of the elements\" class=\"pure-img\" src=\"../_media/periodicity/tables/periodic_table_heatscape_white/molar_volume_heat_w.png\"/><figcaption>Heatscape representing the molar volume of the periodic table elements.</figcaption></figure></div>\n",
+ "<div class=\"pure-g\">\n",
+ "<div class=\"pure-u-1 pure-u-sm-1-1\">\n",
+ "<script async=\"\" src=\"//pagead2.googlesyndication.com/pagead/js/adsbygoogle.js\"></script>\n",
+ "<!-- elements_responsive_centre -->\n",
+ "<ins class=\"adsbygoogle\" data-ad-client=\"ca-pub-9334432533380510\" data-ad-format=\"auto\" data-ad-slot=\"4479092381\" style=\"display:block\"></ins>\n",
+ "<script>\n",
+ "(adsbygoogle = window.adsbygoogle || []).push({});\n",
+ "</script>\n",
+ "</div>\n",
+ "</div>\n",
+ "<h2 class=\"s\">Electrical properties</h2>\n",
+ "<ul class=\"spark_table_list\">\n",
+ "<li><a href=\"../periodicity/elec_resist/\" title=\"view definition and images showing periodicity of electrical resistivity for the elements\"><img alt=\"electrical resistivity of the chemical elements displayed on a miniature periodic table\" src=\"../_media/periodicity/tables/sparktables2/elec_resist.png\"/>Electrical resistivity</a>: (no data) × 10<sup>‑8</sup> Ω m; or mΩ cm</li>\n",
+ "</ul>\n",
+ "<h2 class=\"s\">Heat and conduction</h2>\n",
+ "<ul class=\"spark_table_list\">\n",
+ "<li><a href=\"../periodicity/thermal_conduct/\" title=\"show definition and view pictures showing periodicity of thermal conductivity in the periodic table\"><img alt=\"Thermal conductivity of the chemical elements on a miniature periodic table spark table\" src=\"../_media/periodicity/tables/sparktables2/thermal_conduct.png\">Thermal conductivity</img></a>: 0.1805 W m<sup>‑1</sup> K<sup>‑1</sup></li>\n",
+ "<li><a href=\"../periodicity/coeff_thermal_exp/\" title=\"show definition and view pictures showing periodicity of the coefficient of linear expansion in the periodic table\"><img alt=\"coefficient of linear expansion of the chemical elements displayed on a miniature periodic table\" src=\"../_media/periodicity/tables/sparktables2/coeff_thermal_exp.png\"/>Coefficient of linear thermal expansion</a>: (no data) × 10<sup>‑6</sup> K<sup>‑1</sup></li>\n",
+ "</ul>\n",
+ "<h2 class=\"s\">Optical properties</h2>\n",
+ "<ul class=\"spark_table_list\">\n",
+ "<li><a href=\"../periodicity/reflectivity/\" title=\"view definition and images showing periodicity of reflectivity for the elements\"><img alt=\"reflectivity of the chemical elements displayed on a miniature periodic table\" src=\"../_media/periodicity/tables/sparktables2/reflectivity.png\"/>Reflectivity</a>: (no data) %</li>\n",
+ "<li><a href=\"../periodicity/refractive_index/\" title=\"view definition and images showing periodicity of refractive index for the elements\"><img alt=\"refractive index of the chemical elements displayed on a miniature periodic table\" src=\"../_media/periodicity/tables/sparktables2/refractive_index.png\"/>Refractive index</a>: 1.000132 (gas; liquid 1.12) (no units)</li>\n",
+ "</ul>\n",
+ "<h2 class=\"s\">Acoustic properties</h2>\n",
+ "<ul class=\"spark_table_list\">\n",
+ "<li><a href=\"../periodicity/velocity_sound/\" title=\"view definition and images showing periodicity for velocity of sound in the elements\"><img alt=\"velocity of sound of the chemical elements displayed on a miniature periodic table\" src=\"../_media/periodicity/tables/sparktables2/velocity_sound.png\"/>Velocity of sound</a>: 1270 m s<sup>‑1</sup></li>\n",
+ "</ul>\n",
+ "</main>"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tag = soup.main\n",
+ "tag"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "<h2 class=\"s\">Electrical properties</h2>\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[<h2 class=\"s\">Density properties</h2>,\n",
+ " <h2 class=\"s\">Elastic properties</h2>,\n",
+ " <h2 class=\"s\">Hardnesses</h2>,\n",
+ " <h2 class=\"s\">Electrical properties</h2>,\n",
+ " <h2 class=\"s\">Heat and conduction</h2>,\n",
+ " <h2 class=\"s\">Optical properties</h2>,\n",
+ " <h2 class=\"s\">Acoustic properties</h2>]"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cats = soup.find_all(\"h2\", class_=\"s\")\n",
+ "\n",
+ "cats"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<h2 class=\"s\">Density properties</h2>"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "a =cats[0]\n",
+ "a"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<ul class=\"spark_table_list\">\n",
+ "<li><a href=\"../periodicity/density/\" title=\"view definition and images showing periodicity for density of elemental solids\"><img alt=\"density of the chemical elements displayed on a miniature periodic table\" src=\"../_media/periodicity/tables/sparktables2/density.png\">Density of solid</img></a>: 88 kg m<sup>‑3</sup></li>\n",
+ "<li><a href=\"../periodicity/molar_volume/\" title=\"view definition and images showing periodicity for molar volumes of the elements\"><img alt=\"molar volume of the chemical elements displayed on a miniature periodic table\" src=\"../_media/periodicity/tables/sparktables2/molar_volume.png\"/>Molar volume</a>: 11.42 cm<sup>3</sup></li>\n",
+ "</ul>"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "a.next_sibling.next_sibling"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "a."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": ".venv",
+ "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": "3.9.16"
+ },
+ "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
--
GitLab