Skip to content
Permalink
master
Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?

SF09/SF09 Table EA1a WSAp_A.ipynb

Go to file
 
 
Cannot retrieve contributors at this time
1749 lines (1749 sloc) 71.7 KB
Raw Blame
Open in GitHub Desktop
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "d14c9c1b",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "82d44305",
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(\"WSAp_A1.csv\") # reads in csv as pandas dataframe\n",
"step = df['Step'] # defining variables from dataframe\n",
"T_C = df['T(C)']\n",
"t_hr = df['time(hr)']\n",
"He_Matom = df['He']\n",
"f = df['f']\n",
"F = df['F']\n",
"stnd_dev = df['stnd dev']\n",
"r = df['r']"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "812ac75f",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Step</th>\n",
" <th>T(C)</th>\n",
" <th>t(hr)</th>\n",
" <th>He</th>\n",
" <th>f</th>\n",
" <th>F</th>\n",
" <th>stnd dev</th>\n",
" <th>r</th>\n",
" <th>time(hr)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>150</td>\n",
" <td>0.50</td>\n",
" <td>0.08</td>\n",
" <td>0.08</td>\n",
" <td>0.000199</td>\n",
" <td>0.06</td>\n",
" <td>300.0</td>\n",
" <td>0.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>150</td>\n",
" <td>1.25</td>\n",
" <td>0.04</td>\n",
" <td>0.12</td>\n",
" <td>0.000299</td>\n",
" <td>0.05</td>\n",
" <td>NaN</td>\n",
" <td>1.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>150</td>\n",
" <td>3.00</td>\n",
" <td>0.02</td>\n",
" <td>0.14</td>\n",
" <td>0.000349</td>\n",
" <td>0.04</td>\n",
" <td>NaN</td>\n",
" <td>4.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>175</td>\n",
" <td>1.00</td>\n",
" <td>0.04</td>\n",
" <td>0.18</td>\n",
" <td>0.000448</td>\n",
" <td>0.05</td>\n",
" <td>NaN</td>\n",
" <td>5.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>175</td>\n",
" <td>2.00</td>\n",
" <td>0.03</td>\n",
" <td>0.21</td>\n",
" <td>0.000523</td>\n",
" <td>0.05</td>\n",
" <td>NaN</td>\n",
" <td>7.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>200</td>\n",
" <td>1.00</td>\n",
" <td>0.02</td>\n",
" <td>0.23</td>\n",
" <td>0.000573</td>\n",
" <td>0.05</td>\n",
" <td>NaN</td>\n",
" <td>8.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>200</td>\n",
" <td>1.50</td>\n",
" <td>0.04</td>\n",
" <td>0.27</td>\n",
" <td>0.000672</td>\n",
" <td>0.05</td>\n",
" <td>NaN</td>\n",
" <td>10.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>225</td>\n",
" <td>1.00</td>\n",
" <td>0.05</td>\n",
" <td>0.32</td>\n",
" <td>0.000797</td>\n",
" <td>0.06</td>\n",
" <td>NaN</td>\n",
" <td>11.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>225</td>\n",
" <td>1.25</td>\n",
" <td>0.06</td>\n",
" <td>0.38</td>\n",
" <td>0.000946</td>\n",
" <td>0.05</td>\n",
" <td>NaN</td>\n",
" <td>12.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10</td>\n",
" <td>250</td>\n",
" <td>0.75</td>\n",
" <td>0.14</td>\n",
" <td>0.52</td>\n",
" <td>0.001295</td>\n",
" <td>0.07</td>\n",
" <td>NaN</td>\n",
" <td>13.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>11</td>\n",
" <td>250</td>\n",
" <td>1.25</td>\n",
" <td>0.16</td>\n",
" <td>0.68</td>\n",
" <td>0.001693</td>\n",
" <td>0.08</td>\n",
" <td>NaN</td>\n",
" <td>14.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>12</td>\n",
" <td>240</td>\n",
" <td>2.00</td>\n",
" <td>0.12</td>\n",
" <td>0.80</td>\n",
" <td>0.001992</td>\n",
" <td>0.06</td>\n",
" <td>NaN</td>\n",
" <td>16.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>13</td>\n",
" <td>230</td>\n",
" <td>3.00</td>\n",
" <td>0.08</td>\n",
" <td>0.88</td>\n",
" <td>0.002191</td>\n",
" <td>0.06</td>\n",
" <td>NaN</td>\n",
" <td>19.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>14</td>\n",
" <td>220</td>\n",
" <td>5.00</td>\n",
" <td>0.04</td>\n",
" <td>0.92</td>\n",
" <td>0.002290</td>\n",
" <td>0.06</td>\n",
" <td>NaN</td>\n",
" <td>24.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>15</td>\n",
" <td>235</td>\n",
" <td>4.00</td>\n",
" <td>0.13</td>\n",
" <td>1.05</td>\n",
" <td>0.002614</td>\n",
" <td>0.07</td>\n",
" <td>NaN</td>\n",
" <td>28.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>16</td>\n",
" <td>250</td>\n",
" <td>2.00</td>\n",
" <td>0.13</td>\n",
" <td>1.18</td>\n",
" <td>0.002938</td>\n",
" <td>0.08</td>\n",
" <td>NaN</td>\n",
" <td>30.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>17</td>\n",
" <td>250</td>\n",
" <td>2.50</td>\n",
" <td>0.16</td>\n",
" <td>1.34</td>\n",
" <td>0.003336</td>\n",
" <td>0.09</td>\n",
" <td>NaN</td>\n",
" <td>33.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>18</td>\n",
" <td>265</td>\n",
" <td>1.00</td>\n",
" <td>0.09</td>\n",
" <td>1.43</td>\n",
" <td>0.003560</td>\n",
" <td>0.06</td>\n",
" <td>NaN</td>\n",
" <td>34.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>19</td>\n",
" <td>265</td>\n",
" <td>1.50</td>\n",
" <td>0.12</td>\n",
" <td>1.55</td>\n",
" <td>0.003859</td>\n",
" <td>0.09</td>\n",
" <td>NaN</td>\n",
" <td>35.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>20</td>\n",
" <td>285</td>\n",
" <td>0.50</td>\n",
" <td>0.14</td>\n",
" <td>1.69</td>\n",
" <td>0.004208</td>\n",
" <td>0.07</td>\n",
" <td>NaN</td>\n",
" <td>36.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>21</td>\n",
" <td>285</td>\n",
" <td>1.25</td>\n",
" <td>0.29</td>\n",
" <td>1.98</td>\n",
" <td>0.004930</td>\n",
" <td>0.11</td>\n",
" <td>NaN</td>\n",
" <td>37.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>22</td>\n",
" <td>285</td>\n",
" <td>1.25</td>\n",
" <td>0.26</td>\n",
" <td>2.24</td>\n",
" <td>0.005577</td>\n",
" <td>0.10</td>\n",
" <td>NaN</td>\n",
" <td>38.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>23</td>\n",
" <td>300</td>\n",
" <td>0.50</td>\n",
" <td>0.20</td>\n",
" <td>2.44</td>\n",
" <td>0.006075</td>\n",
" <td>0.09</td>\n",
" <td>NaN</td>\n",
" <td>39.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>24</td>\n",
" <td>300</td>\n",
" <td>0.75</td>\n",
" <td>0.27</td>\n",
" <td>2.71</td>\n",
" <td>0.006747</td>\n",
" <td>0.09</td>\n",
" <td>NaN</td>\n",
" <td>39.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>25</td>\n",
" <td>300</td>\n",
" <td>1.00</td>\n",
" <td>0.30</td>\n",
" <td>3.01</td>\n",
" <td>0.007494</td>\n",
" <td>0.10</td>\n",
" <td>NaN</td>\n",
" <td>40.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>26</td>\n",
" <td>275</td>\n",
" <td>2.00</td>\n",
" <td>0.15</td>\n",
" <td>3.16</td>\n",
" <td>0.007867</td>\n",
" <td>0.08</td>\n",
" <td>NaN</td>\n",
" <td>42.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>27</td>\n",
" <td>275</td>\n",
" <td>2.00</td>\n",
" <td>0.15</td>\n",
" <td>3.31</td>\n",
" <td>0.008241</td>\n",
" <td>0.08</td>\n",
" <td>NaN</td>\n",
" <td>44.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>28</td>\n",
" <td>300</td>\n",
" <td>1.00</td>\n",
" <td>0.22</td>\n",
" <td>3.53</td>\n",
" <td>0.008789</td>\n",
" <td>0.11</td>\n",
" <td>NaN</td>\n",
" <td>45.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>29</td>\n",
" <td>300</td>\n",
" <td>1.25</td>\n",
" <td>0.34</td>\n",
" <td>3.87</td>\n",
" <td>0.009635</td>\n",
" <td>0.11</td>\n",
" <td>NaN</td>\n",
" <td>47.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>30</td>\n",
" <td>325</td>\n",
" <td>0.50</td>\n",
" <td>0.33</td>\n",
" <td>4.20</td>\n",
" <td>0.010457</td>\n",
" <td>0.10</td>\n",
" <td>NaN</td>\n",
" <td>47.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>31</td>\n",
" <td>325</td>\n",
" <td>0.50</td>\n",
" <td>0.37</td>\n",
" <td>4.57</td>\n",
" <td>0.011378</td>\n",
" <td>0.11</td>\n",
" <td>NaN</td>\n",
" <td>48.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>32</td>\n",
" <td>325</td>\n",
" <td>0.50</td>\n",
" <td>0.29</td>\n",
" <td>4.86</td>\n",
" <td>0.012100</td>\n",
" <td>0.11</td>\n",
" <td>NaN</td>\n",
" <td>48.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>33</td>\n",
" <td>340</td>\n",
" <td>0.50</td>\n",
" <td>0.54</td>\n",
" <td>5.40</td>\n",
" <td>0.013444</td>\n",
" <td>0.14</td>\n",
" <td>NaN</td>\n",
" <td>49.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>34</td>\n",
" <td>340</td>\n",
" <td>0.50</td>\n",
" <td>0.50</td>\n",
" <td>5.90</td>\n",
" <td>0.014689</td>\n",
" <td>0.11</td>\n",
" <td>NaN</td>\n",
" <td>49.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>35</td>\n",
" <td>340</td>\n",
" <td>0.75</td>\n",
" <td>0.63</td>\n",
" <td>6.53</td>\n",
" <td>0.016258</td>\n",
" <td>0.13</td>\n",
" <td>NaN</td>\n",
" <td>50.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>36</td>\n",
" <td>365</td>\n",
" <td>0.50</td>\n",
" <td>1.06</td>\n",
" <td>7.59</td>\n",
" <td>0.018897</td>\n",
" <td>0.18</td>\n",
" <td>NaN</td>\n",
" <td>50.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>37</td>\n",
" <td>365</td>\n",
" <td>0.50</td>\n",
" <td>0.96</td>\n",
" <td>8.55</td>\n",
" <td>0.021287</td>\n",
" <td>0.15</td>\n",
" <td>NaN</td>\n",
" <td>51.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>38</td>\n",
" <td>400</td>\n",
" <td>0.50</td>\n",
" <td>2.61</td>\n",
" <td>11.16</td>\n",
" <td>0.027785</td>\n",
" <td>0.30</td>\n",
" <td>NaN</td>\n",
" <td>51.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>39</td>\n",
" <td>425</td>\n",
" <td>0.50</td>\n",
" <td>5.09</td>\n",
" <td>16.25</td>\n",
" <td>0.040457</td>\n",
" <td>0.36</td>\n",
" <td>NaN</td>\n",
" <td>52.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>40</td>\n",
" <td>420</td>\n",
" <td>0.50</td>\n",
" <td>3.20</td>\n",
" <td>19.45</td>\n",
" <td>0.048424</td>\n",
" <td>0.34</td>\n",
" <td>NaN</td>\n",
" <td>52.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>41</td>\n",
" <td>480</td>\n",
" <td>2.00</td>\n",
" <td>31.27</td>\n",
" <td>50.72</td>\n",
" <td>0.126276</td>\n",
" <td>1.04</td>\n",
" <td>NaN</td>\n",
" <td>54.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>42</td>\n",
" <td>500</td>\n",
" <td>0.50</td>\n",
" <td>7.41</td>\n",
" <td>58.13</td>\n",
" <td>0.144724</td>\n",
" <td>0.53</td>\n",
" <td>NaN</td>\n",
" <td>55.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>43</td>\n",
" <td>500</td>\n",
" <td>0.50</td>\n",
" <td>6.52</td>\n",
" <td>64.65</td>\n",
" <td>0.160957</td>\n",
" <td>0.44</td>\n",
" <td>NaN</td>\n",
" <td>55.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>44</td>\n",
" <td>500</td>\n",
" <td>1.00</td>\n",
" <td>10.75</td>\n",
" <td>75.40</td>\n",
" <td>0.187721</td>\n",
" <td>0.60</td>\n",
" <td>NaN</td>\n",
" <td>56.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>45</td>\n",
" <td>500</td>\n",
" <td>2.00</td>\n",
" <td>17.24</td>\n",
" <td>92.64</td>\n",
" <td>0.230643</td>\n",
" <td>0.78</td>\n",
" <td>NaN</td>\n",
" <td>58.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>46</td>\n",
" <td>500</td>\n",
" <td>3.00</td>\n",
" <td>19.09</td>\n",
" <td>111.73</td>\n",
" <td>0.278171</td>\n",
" <td>0.81</td>\n",
" <td>NaN</td>\n",
" <td>61.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>47</td>\n",
" <td>700</td>\n",
" <td>0.20</td>\n",
" <td>88.83</td>\n",
" <td>200.56</td>\n",
" <td>0.499328</td>\n",
" <td>1.61</td>\n",
" <td>NaN</td>\n",
" <td>61.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>48</td>\n",
" <td>800</td>\n",
" <td>0.20</td>\n",
" <td>138.91</td>\n",
" <td>339.47</td>\n",
" <td>0.845168</td>\n",
" <td>11.55</td>\n",
" <td>NaN</td>\n",
" <td>62.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>49</td>\n",
" <td>800</td>\n",
" <td>0.20</td>\n",
" <td>38.16</td>\n",
" <td>377.63</td>\n",
" <td>0.940173</td>\n",
" <td>0.33</td>\n",
" <td>NaN</td>\n",
" <td>62.35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>50</td>\n",
" <td>750</td>\n",
" <td>0.40</td>\n",
" <td>13.82</td>\n",
" <td>391.45</td>\n",
" <td>0.974580</td>\n",
" <td>0.74</td>\n",
" <td>NaN</td>\n",
" <td>62.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>51</td>\n",
" <td>700</td>\n",
" <td>1.00</td>\n",
" <td>5.96</td>\n",
" <td>397.41</td>\n",
" <td>0.989419</td>\n",
" <td>0.43</td>\n",
" <td>NaN</td>\n",
" <td>63.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>52</td>\n",
" <td>650</td>\n",
" <td>1.00</td>\n",
" <td>1.29</td>\n",
" <td>398.70</td>\n",
" <td>0.992631</td>\n",
" <td>0.18</td>\n",
" <td>NaN</td>\n",
" <td>64.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>53</td>\n",
" <td>725</td>\n",
" <td>0.50</td>\n",
" <td>1.26</td>\n",
" <td>399.96</td>\n",
" <td>0.995768</td>\n",
" <td>0.21</td>\n",
" <td>NaN</td>\n",
" <td>65.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>54</td>\n",
" <td>725</td>\n",
" <td>0.70</td>\n",
" <td>0.89</td>\n",
" <td>400.85</td>\n",
" <td>0.997983</td>\n",
" <td>0.18</td>\n",
" <td>NaN</td>\n",
" <td>65.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>55</td>\n",
" <td>850</td>\n",
" <td>0.20</td>\n",
" <td>0.61</td>\n",
" <td>401.46</td>\n",
" <td>0.999502</td>\n",
" <td>0.14</td>\n",
" <td>NaN</td>\n",
" <td>66.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>56</td>\n",
" <td>950</td>\n",
" <td>0.10</td>\n",
" <td>0.16</td>\n",
" <td>401.62</td>\n",
" <td>0.999900</td>\n",
" <td>0.08</td>\n",
" <td>NaN</td>\n",
" <td>66.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>57</td>\n",
" <td>950</td>\n",
" <td>0.10</td>\n",
" <td>0.04</td>\n",
" <td>401.66</td>\n",
" <td>1.000000</td>\n",
" <td>0.05</td>\n",
" <td>NaN</td>\n",
" <td>66.35</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Step T(C) t(hr) He f F stnd dev r time(hr)\n",
"0 1 150 0.50 0.08 0.08 0.000199 0.06 300.0 0.50\n",
"1 2 150 1.25 0.04 0.12 0.000299 0.05 NaN 1.75\n",
"2 3 150 3.00 0.02 0.14 0.000349 0.04 NaN 4.75\n",
"3 4 175 1.00 0.04 0.18 0.000448 0.05 NaN 5.75\n",
"4 5 175 2.00 0.03 0.21 0.000523 0.05 NaN 7.75\n",
"5 6 200 1.00 0.02 0.23 0.000573 0.05 NaN 8.75\n",
"6 7 200 1.50 0.04 0.27 0.000672 0.05 NaN 10.25\n",
"7 8 225 1.00 0.05 0.32 0.000797 0.06 NaN 11.25\n",
"8 9 225 1.25 0.06 0.38 0.000946 0.05 NaN 12.50\n",
"9 10 250 0.75 0.14 0.52 0.001295 0.07 NaN 13.25\n",
"10 11 250 1.25 0.16 0.68 0.001693 0.08 NaN 14.50\n",
"11 12 240 2.00 0.12 0.80 0.001992 0.06 NaN 16.50\n",
"12 13 230 3.00 0.08 0.88 0.002191 0.06 NaN 19.50\n",
"13 14 220 5.00 0.04 0.92 0.002290 0.06 NaN 24.50\n",
"14 15 235 4.00 0.13 1.05 0.002614 0.07 NaN 28.50\n",
"15 16 250 2.00 0.13 1.18 0.002938 0.08 NaN 30.50\n",
"16 17 250 2.50 0.16 1.34 0.003336 0.09 NaN 33.00\n",
"17 18 265 1.00 0.09 1.43 0.003560 0.06 NaN 34.00\n",
"18 19 265 1.50 0.12 1.55 0.003859 0.09 NaN 35.50\n",
"19 20 285 0.50 0.14 1.69 0.004208 0.07 NaN 36.00\n",
"20 21 285 1.25 0.29 1.98 0.004930 0.11 NaN 37.25\n",
"21 22 285 1.25 0.26 2.24 0.005577 0.10 NaN 38.50\n",
"22 23 300 0.50 0.20 2.44 0.006075 0.09 NaN 39.00\n",
"23 24 300 0.75 0.27 2.71 0.006747 0.09 NaN 39.75\n",
"24 25 300 1.00 0.30 3.01 0.007494 0.10 NaN 40.75\n",
"25 26 275 2.00 0.15 3.16 0.007867 0.08 NaN 42.75\n",
"26 27 275 2.00 0.15 3.31 0.008241 0.08 NaN 44.75\n",
"27 28 300 1.00 0.22 3.53 0.008789 0.11 NaN 45.75\n",
"28 29 300 1.25 0.34 3.87 0.009635 0.11 NaN 47.00\n",
"29 30 325 0.50 0.33 4.20 0.010457 0.10 NaN 47.50\n",
"30 31 325 0.50 0.37 4.57 0.011378 0.11 NaN 48.00\n",
"31 32 325 0.50 0.29 4.86 0.012100 0.11 NaN 48.50\n",
"32 33 340 0.50 0.54 5.40 0.013444 0.14 NaN 49.00\n",
"33 34 340 0.50 0.50 5.90 0.014689 0.11 NaN 49.50\n",
"34 35 340 0.75 0.63 6.53 0.016258 0.13 NaN 50.25\n",
"35 36 365 0.50 1.06 7.59 0.018897 0.18 NaN 50.75\n",
"36 37 365 0.50 0.96 8.55 0.021287 0.15 NaN 51.25\n",
"37 38 400 0.50 2.61 11.16 0.027785 0.30 NaN 51.75\n",
"38 39 425 0.50 5.09 16.25 0.040457 0.36 NaN 52.25\n",
"39 40 420 0.50 3.20 19.45 0.048424 0.34 NaN 52.75\n",
"40 41 480 2.00 31.27 50.72 0.126276 1.04 NaN 54.75\n",
"41 42 500 0.50 7.41 58.13 0.144724 0.53 NaN 55.25\n",
"42 43 500 0.50 6.52 64.65 0.160957 0.44 NaN 55.75\n",
"43 44 500 1.00 10.75 75.40 0.187721 0.60 NaN 56.75\n",
"44 45 500 2.00 17.24 92.64 0.230643 0.78 NaN 58.75\n",
"45 46 500 3.00 19.09 111.73 0.278171 0.81 NaN 61.75\n",
"46 47 700 0.20 88.83 200.56 0.499328 1.61 NaN 61.95\n",
"47 48 800 0.20 138.91 339.47 0.845168 11.55 NaN 62.15\n",
"48 49 800 0.20 38.16 377.63 0.940173 0.33 NaN 62.35\n",
"49 50 750 0.40 13.82 391.45 0.974580 0.74 NaN 62.75\n",
"50 51 700 1.00 5.96 397.41 0.989419 0.43 NaN 63.75\n",
"51 52 650 1.00 1.29 398.70 0.992631 0.18 NaN 64.75\n",
"52 53 725 0.50 1.26 399.96 0.995768 0.21 NaN 65.25\n",
"53 54 725 0.70 0.89 400.85 0.997983 0.18 NaN 65.95\n",
"54 55 850 0.20 0.61 401.46 0.999502 0.14 NaN 66.15\n",
"55 56 950 0.10 0.16 401.62 0.999900 0.08 NaN 66.25\n",
"56 57 950 0.10 0.04 401.66 1.000000 0.05 NaN 66.35"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df # dataframe"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "ff7fc21e",
"metadata": {},
"outputs": [],
"source": [
"T = T_C + 273.15 # converts T(C) into temperature in kelvin\n",
"t = t_hr*3600 # converts t(hr) into time in seconds\n",
"He = He_Matom\n",
"sigma = stnd_dev # standard deviation\n",
"r = df.at[0, 'r']*(10**-4) # converts radius in microns to centimeters"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "de459138",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"401.66\n"
]
}
],
"source": [
"He_total = np.sum(He) # Calculates total He released\n",
"print(He_total) # Total He released during experiment, shoud equal last step in column 'f'"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "775e79a5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9999999999999999"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f = He/He_total # fractional release for each step\n",
"np.sum(f) # Total release should equal 1"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "877ad0f8",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Step</th>\n",
" <th>T(K)</th>\n",
" <th>t(sec)</th>\n",
" <th>He</th>\n",
" <th>f</th>\n",
" <th>F</th>\n",
" <th>sigma</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.0</td>\n",
" <td>423.15</td>\n",
" <td>1800.0</td>\n",
" <td>0.08</td>\n",
" <td>0.000199</td>\n",
" <td>0.000199</td>\n",
" <td>0.06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2.0</td>\n",
" <td>423.15</td>\n",
" <td>6300.0</td>\n",
" <td>0.04</td>\n",
" <td>0.000100</td>\n",
" <td>0.000299</td>\n",
" <td>0.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3.0</td>\n",
" <td>423.15</td>\n",
" <td>17100.0</td>\n",
" <td>0.02</td>\n",
" <td>0.000050</td>\n",
" <td>0.000349</td>\n",
" <td>0.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.0</td>\n",
" <td>448.15</td>\n",
" <td>20700.0</td>\n",
" <td>0.04</td>\n",
" <td>0.000100</td>\n",
" <td>0.000448</td>\n",
" <td>0.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5.0</td>\n",
" <td>448.15</td>\n",
" <td>27900.0</td>\n",
" <td>0.03</td>\n",
" <td>0.000075</td>\n",
" <td>0.000523</td>\n",
" <td>0.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6.0</td>\n",
" <td>473.15</td>\n",
" <td>31500.0</td>\n",
" <td>0.02</td>\n",
" <td>0.000050</td>\n",
" <td>0.000573</td>\n",
" <td>0.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7.0</td>\n",
" <td>473.15</td>\n",
" <td>36900.0</td>\n",
" <td>0.04</td>\n",
" <td>0.000100</td>\n",
" <td>0.000672</td>\n",
" <td>0.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8.0</td>\n",
" <td>498.15</td>\n",
" <td>40500.0</td>\n",
" <td>0.05</td>\n",
" <td>0.000124</td>\n",
" <td>0.000797</td>\n",
" <td>0.06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9.0</td>\n",
" <td>498.15</td>\n",
" <td>45000.0</td>\n",
" <td>0.06</td>\n",
" <td>0.000149</td>\n",
" <td>0.000946</td>\n",
" <td>0.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10.0</td>\n",
" <td>523.15</td>\n",
" <td>47700.0</td>\n",
" <td>0.14</td>\n",
" <td>0.000349</td>\n",
" <td>0.001295</td>\n",
" <td>0.07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>11.0</td>\n",
" <td>523.15</td>\n",
" <td>52200.0</td>\n",
" <td>0.16</td>\n",
" <td>0.000398</td>\n",
" <td>0.001693</td>\n",
" <td>0.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>12.0</td>\n",
" <td>513.15</td>\n",
" <td>59400.0</td>\n",
" <td>0.12</td>\n",
" <td>0.000299</td>\n",
" <td>0.001992</td>\n",
" <td>0.06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>13.0</td>\n",
" <td>503.15</td>\n",
" <td>70200.0</td>\n",
" <td>0.08</td>\n",
" <td>0.000199</td>\n",
" <td>0.002191</td>\n",
" <td>0.06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>14.0</td>\n",
" <td>493.15</td>\n",
" <td>88200.0</td>\n",
" <td>0.04</td>\n",
" <td>0.000100</td>\n",
" <td>0.002290</td>\n",
" <td>0.06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>15.0</td>\n",
" <td>508.15</td>\n",
" <td>102600.0</td>\n",
" <td>0.13</td>\n",
" <td>0.000324</td>\n",
" <td>0.002614</td>\n",
" <td>0.07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>16.0</td>\n",
" <td>523.15</td>\n",
" <td>109800.0</td>\n",
" <td>0.13</td>\n",
" <td>0.000324</td>\n",
" <td>0.002938</td>\n",
" <td>0.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>17.0</td>\n",
" <td>523.15</td>\n",
" <td>118800.0</td>\n",
" <td>0.16</td>\n",
" <td>0.000398</td>\n",
" <td>0.003336</td>\n",
" <td>0.09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>18.0</td>\n",
" <td>538.15</td>\n",
" <td>122400.0</td>\n",
" <td>0.09</td>\n",
" <td>0.000224</td>\n",
" <td>0.003560</td>\n",
" <td>0.06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>19.0</td>\n",
" <td>538.15</td>\n",
" <td>127800.0</td>\n",
" <td>0.12</td>\n",
" <td>0.000299</td>\n",
" <td>0.003859</td>\n",
" <td>0.09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>20.0</td>\n",
" <td>558.15</td>\n",
" <td>129600.0</td>\n",
" <td>0.14</td>\n",
" <td>0.000349</td>\n",
" <td>0.004208</td>\n",
" <td>0.07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>21.0</td>\n",
" <td>558.15</td>\n",
" <td>134100.0</td>\n",
" <td>0.29</td>\n",
" <td>0.000722</td>\n",
" <td>0.004930</td>\n",
" <td>0.11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>22.0</td>\n",
" <td>558.15</td>\n",
" <td>138600.0</td>\n",
" <td>0.26</td>\n",
" <td>0.000647</td>\n",
" <td>0.005577</td>\n",
" <td>0.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>23.0</td>\n",
" <td>573.15</td>\n",
" <td>140400.0</td>\n",
" <td>0.20</td>\n",
" <td>0.000498</td>\n",
" <td>0.006075</td>\n",
" <td>0.09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>24.0</td>\n",
" <td>573.15</td>\n",
" <td>143100.0</td>\n",
" <td>0.27</td>\n",
" <td>0.000672</td>\n",
" <td>0.006747</td>\n",
" <td>0.09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>25.0</td>\n",
" <td>573.15</td>\n",
" <td>146700.0</td>\n",
" <td>0.30</td>\n",
" <td>0.000747</td>\n",
" <td>0.007494</td>\n",
" <td>0.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>26.0</td>\n",
" <td>548.15</td>\n",
" <td>153900.0</td>\n",
" <td>0.15</td>\n",
" <td>0.000373</td>\n",
" <td>0.007867</td>\n",
" <td>0.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>27.0</td>\n",
" <td>548.15</td>\n",
" <td>161100.0</td>\n",
" <td>0.15</td>\n",
" <td>0.000373</td>\n",
" <td>0.008241</td>\n",
" <td>0.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>28.0</td>\n",
" <td>573.15</td>\n",
" <td>164700.0</td>\n",
" <td>0.22</td>\n",
" <td>0.000548</td>\n",
" <td>0.008789</td>\n",
" <td>0.11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>29.0</td>\n",
" <td>573.15</td>\n",
" <td>169200.0</td>\n",
" <td>0.34</td>\n",
" <td>0.000846</td>\n",
" <td>0.009635</td>\n",
" <td>0.11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>30.0</td>\n",
" <td>598.15</td>\n",
" <td>171000.0</td>\n",
" <td>0.33</td>\n",
" <td>0.000822</td>\n",
" <td>0.010457</td>\n",
" <td>0.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>31.0</td>\n",
" <td>598.15</td>\n",
" <td>172800.0</td>\n",
" <td>0.37</td>\n",
" <td>0.000921</td>\n",
" <td>0.011378</td>\n",
" <td>0.11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>32.0</td>\n",
" <td>598.15</td>\n",
" <td>174600.0</td>\n",
" <td>0.29</td>\n",
" <td>0.000722</td>\n",
" <td>0.012100</td>\n",
" <td>0.11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>33.0</td>\n",
" <td>613.15</td>\n",
" <td>176400.0</td>\n",
" <td>0.54</td>\n",
" <td>0.001344</td>\n",
" <td>0.013444</td>\n",
" <td>0.14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>34.0</td>\n",
" <td>613.15</td>\n",
" <td>178200.0</td>\n",
" <td>0.50</td>\n",
" <td>0.001245</td>\n",
" <td>0.014689</td>\n",
" <td>0.11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>35.0</td>\n",
" <td>613.15</td>\n",
" <td>180900.0</td>\n",
" <td>0.63</td>\n",
" <td>0.001568</td>\n",
" <td>0.016258</td>\n",
" <td>0.13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>36.0</td>\n",
" <td>638.15</td>\n",
" <td>182700.0</td>\n",
" <td>1.06</td>\n",
" <td>0.002639</td>\n",
" <td>0.018897</td>\n",
" <td>0.18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>37.0</td>\n",
" <td>638.15</td>\n",
" <td>184500.0</td>\n",
" <td>0.96</td>\n",
" <td>0.002390</td>\n",
" <td>0.021287</td>\n",
" <td>0.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>38.0</td>\n",
" <td>673.15</td>\n",
" <td>186300.0</td>\n",
" <td>2.61</td>\n",
" <td>0.006498</td>\n",
" <td>0.027785</td>\n",
" <td>0.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>39.0</td>\n",
" <td>698.15</td>\n",
" <td>188100.0</td>\n",
" <td>5.09</td>\n",
" <td>0.012672</td>\n",
" <td>0.040457</td>\n",
" <td>0.36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>40.0</td>\n",
" <td>693.15</td>\n",
" <td>189900.0</td>\n",
" <td>3.20</td>\n",
" <td>0.007967</td>\n",
" <td>0.048424</td>\n",
" <td>0.34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>41.0</td>\n",
" <td>753.15</td>\n",
" <td>197100.0</td>\n",
" <td>31.27</td>\n",
" <td>0.077852</td>\n",
" <td>0.126276</td>\n",
" <td>1.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>42.0</td>\n",
" <td>773.15</td>\n",
" <td>198900.0</td>\n",
" <td>7.41</td>\n",
" <td>0.018448</td>\n",
" <td>0.144724</td>\n",
" <td>0.53</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>43.0</td>\n",
" <td>773.15</td>\n",
" <td>200700.0</td>\n",
" <td>6.52</td>\n",
" <td>0.016233</td>\n",
" <td>0.160957</td>\n",
" <td>0.44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>44.0</td>\n",
" <td>773.15</td>\n",
" <td>204300.0</td>\n",
" <td>10.75</td>\n",
" <td>0.026764</td>\n",
" <td>0.187721</td>\n",
" <td>0.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>45.0</td>\n",
" <td>773.15</td>\n",
" <td>211500.0</td>\n",
" <td>17.24</td>\n",
" <td>0.042922</td>\n",
" <td>0.230643</td>\n",
" <td>0.78</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>46.0</td>\n",
" <td>773.15</td>\n",
" <td>222300.0</td>\n",
" <td>19.09</td>\n",
" <td>0.047528</td>\n",
" <td>0.278171</td>\n",
" <td>0.81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>47.0</td>\n",
" <td>973.15</td>\n",
" <td>223020.0</td>\n",
" <td>88.83</td>\n",
" <td>0.221157</td>\n",
" <td>0.499328</td>\n",
" <td>1.61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>48.0</td>\n",
" <td>1073.15</td>\n",
" <td>223740.0</td>\n",
" <td>138.91</td>\n",
" <td>0.345840</td>\n",
" <td>0.845168</td>\n",
" <td>11.55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>49.0</td>\n",
" <td>1073.15</td>\n",
" <td>224460.0</td>\n",
" <td>38.16</td>\n",
" <td>0.095006</td>\n",
" <td>0.940173</td>\n",
" <td>0.33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>50.0</td>\n",
" <td>1023.15</td>\n",
" <td>225900.0</td>\n",
" <td>13.82</td>\n",
" <td>0.034407</td>\n",
" <td>0.974580</td>\n",
" <td>0.74</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>51.0</td>\n",
" <td>973.15</td>\n",
" <td>229500.0</td>\n",
" <td>5.96</td>\n",
" <td>0.014838</td>\n",
" <td>0.989419</td>\n",
" <td>0.43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>52.0</td>\n",
" <td>923.15</td>\n",
" <td>233100.0</td>\n",
" <td>1.29</td>\n",
" <td>0.003212</td>\n",
" <td>0.992631</td>\n",
" <td>0.18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>53.0</td>\n",
" <td>998.15</td>\n",
" <td>234900.0</td>\n",
" <td>1.26</td>\n",
" <td>0.003137</td>\n",
" <td>0.995768</td>\n",
" <td>0.21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>54.0</td>\n",
" <td>998.15</td>\n",
" <td>237420.0</td>\n",
" <td>0.89</td>\n",
" <td>0.002216</td>\n",
" <td>0.997983</td>\n",
" <td>0.18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>55.0</td>\n",
" <td>1123.15</td>\n",
" <td>238140.0</td>\n",
" <td>0.61</td>\n",
" <td>0.001519</td>\n",
" <td>0.999502</td>\n",
" <td>0.14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>56.0</td>\n",
" <td>1223.15</td>\n",
" <td>238500.0</td>\n",
" <td>0.16</td>\n",
" <td>0.000398</td>\n",
" <td>0.999900</td>\n",
" <td>0.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>57.0</td>\n",
" <td>1223.15</td>\n",
" <td>238860.0</td>\n",
" <td>0.04</td>\n",
" <td>0.000100</td>\n",
" <td>1.000000</td>\n",
" <td>0.05</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Step T(K) t(sec) He f F sigma\n",
"0 1.0 423.15 1800.0 0.08 0.000199 0.000199 0.06\n",
"1 2.0 423.15 6300.0 0.04 0.000100 0.000299 0.05\n",
"2 3.0 423.15 17100.0 0.02 0.000050 0.000349 0.04\n",
"3 4.0 448.15 20700.0 0.04 0.000100 0.000448 0.05\n",
"4 5.0 448.15 27900.0 0.03 0.000075 0.000523 0.05\n",
"5 6.0 473.15 31500.0 0.02 0.000050 0.000573 0.05\n",
"6 7.0 473.15 36900.0 0.04 0.000100 0.000672 0.05\n",
"7 8.0 498.15 40500.0 0.05 0.000124 0.000797 0.06\n",
"8 9.0 498.15 45000.0 0.06 0.000149 0.000946 0.05\n",
"9 10.0 523.15 47700.0 0.14 0.000349 0.001295 0.07\n",
"10 11.0 523.15 52200.0 0.16 0.000398 0.001693 0.08\n",
"11 12.0 513.15 59400.0 0.12 0.000299 0.001992 0.06\n",
"12 13.0 503.15 70200.0 0.08 0.000199 0.002191 0.06\n",
"13 14.0 493.15 88200.0 0.04 0.000100 0.002290 0.06\n",
"14 15.0 508.15 102600.0 0.13 0.000324 0.002614 0.07\n",
"15 16.0 523.15 109800.0 0.13 0.000324 0.002938 0.08\n",
"16 17.0 523.15 118800.0 0.16 0.000398 0.003336 0.09\n",
"17 18.0 538.15 122400.0 0.09 0.000224 0.003560 0.06\n",
"18 19.0 538.15 127800.0 0.12 0.000299 0.003859 0.09\n",
"19 20.0 558.15 129600.0 0.14 0.000349 0.004208 0.07\n",
"20 21.0 558.15 134100.0 0.29 0.000722 0.004930 0.11\n",
"21 22.0 558.15 138600.0 0.26 0.000647 0.005577 0.10\n",
"22 23.0 573.15 140400.0 0.20 0.000498 0.006075 0.09\n",
"23 24.0 573.15 143100.0 0.27 0.000672 0.006747 0.09\n",
"24 25.0 573.15 146700.0 0.30 0.000747 0.007494 0.10\n",
"25 26.0 548.15 153900.0 0.15 0.000373 0.007867 0.08\n",
"26 27.0 548.15 161100.0 0.15 0.000373 0.008241 0.08\n",
"27 28.0 573.15 164700.0 0.22 0.000548 0.008789 0.11\n",
"28 29.0 573.15 169200.0 0.34 0.000846 0.009635 0.11\n",
"29 30.0 598.15 171000.0 0.33 0.000822 0.010457 0.10\n",
"30 31.0 598.15 172800.0 0.37 0.000921 0.011378 0.11\n",
"31 32.0 598.15 174600.0 0.29 0.000722 0.012100 0.11\n",
"32 33.0 613.15 176400.0 0.54 0.001344 0.013444 0.14\n",
"33 34.0 613.15 178200.0 0.50 0.001245 0.014689 0.11\n",
"34 35.0 613.15 180900.0 0.63 0.001568 0.016258 0.13\n",
"35 36.0 638.15 182700.0 1.06 0.002639 0.018897 0.18\n",
"36 37.0 638.15 184500.0 0.96 0.002390 0.021287 0.15\n",
"37 38.0 673.15 186300.0 2.61 0.006498 0.027785 0.30\n",
"38 39.0 698.15 188100.0 5.09 0.012672 0.040457 0.36\n",
"39 40.0 693.15 189900.0 3.20 0.007967 0.048424 0.34\n",
"40 41.0 753.15 197100.0 31.27 0.077852 0.126276 1.04\n",
"41 42.0 773.15 198900.0 7.41 0.018448 0.144724 0.53\n",
"42 43.0 773.15 200700.0 6.52 0.016233 0.160957 0.44\n",
"43 44.0 773.15 204300.0 10.75 0.026764 0.187721 0.60\n",
"44 45.0 773.15 211500.0 17.24 0.042922 0.230643 0.78\n",
"45 46.0 773.15 222300.0 19.09 0.047528 0.278171 0.81\n",
"46 47.0 973.15 223020.0 88.83 0.221157 0.499328 1.61\n",
"47 48.0 1073.15 223740.0 138.91 0.345840 0.845168 11.55\n",
"48 49.0 1073.15 224460.0 38.16 0.095006 0.940173 0.33\n",
"49 50.0 1023.15 225900.0 13.82 0.034407 0.974580 0.74\n",
"50 51.0 973.15 229500.0 5.96 0.014838 0.989419 0.43\n",
"51 52.0 923.15 233100.0 1.29 0.003212 0.992631 0.18\n",
"52 53.0 998.15 234900.0 1.26 0.003137 0.995768 0.21\n",
"53 54.0 998.15 237420.0 0.89 0.002216 0.997983 0.18\n",
"54 55.0 1123.15 238140.0 0.61 0.001519 0.999502 0.14\n",
"55 56.0 1223.15 238500.0 0.16 0.000398 0.999900 0.08\n",
"56 57.0 1223.15 238860.0 0.04 0.000100 1.000000 0.05"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame([step, T, t, He, f, F, sigma]) # Creates a new dataframe (df) from existing var\n",
"nd = df.T # Transposes df, converts rows to columns\n",
"nd.columns = ['Step', 'T(K)', 't(sec)', 'He', 'f', 'F', 'sigma'] # Renames columns for proper units\n",
"nd"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "3b029929",
"metadata": {},
"outputs": [],
"source": [
"Step = nd['Step'] # Setting variables from the new dataframe\n",
"T = nd['T(K)']\n",
"t = nd['t(sec)']\n",
"He = nd['He']\n",
"f = nd['f']\n",
"F = nd['F']\n",
"sigma = nd['sigma']"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "f4af5549",
"metadata": {},
"outputs": [],
"source": [
"def lower_test(R, i, t, F):\n",
" # set up a function to call for the less than values\n",
" # sum F \n",
" return ((R**2)/((np.pi**2)*(t[i+1]-t[i])))*(-((np.pi**2)/3)*(np.sum(F[i+1])-np.sum(F[i]))-((2*np.pi)*(np.sqrt(1-(np.pi/3)*np.sum(F[i+1]))-np.sqrt(1-(np.pi/3)*np.sum(F[i])))))\n",
" # uses equation 5b in Fechtig and Kalbitzer\n",
"def upper_test(R, i, t, F):\n",
" # set up a function to call for the greater than values\n",
" return ((R**2)/((np.pi**2)*(t[i+1]-t[i])))*np.log((1-np.sum(F[i]))/(1-np.sum(F[i+1]))) \n",
" # uses equation 5c in Fechtig and Kalbitzer"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "b991eec9",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:8: RuntimeWarning: divide by zero encountered in double_scalars\n",
" \n"
]
},
{
"data": {
"text/plain": [
"array([ nan, 8.65639352e-16, 2.34460005e-16, 1.73146542e-15,\n",
" 7.91391933e-16, 1.19054427e-15, 1.80392580e-15, 3.99158640e-15,\n",
" 4.54681702e-15, 2.27386297e-14, 2.07957427e-14, 1.20257804e-14,\n",
" 6.06825833e-15, 1.95071942e-15, 8.67478362e-15, 1.96443585e-14,\n",
" 2.18637142e-14, 3.38042109e-14, 3.23328887e-14, 1.23070264e-13,\n",
" 1.15555027e-13, 1.19191387e-13, 2.54314073e-13, 2.51985325e-13,\n",
" 2.33360108e-13, 6.29573519e-14, 6.60381773e-14, 2.04864318e-13,\n",
" 2.74175801e-13, 7.25993315e-13, 8.85211741e-13, 7.46514322e-13,\n",
" 1.51366217e-12, 1.54519141e-12, 1.42936382e-12, 4.10486631e-12,\n",
" 4.25797829e-12, 1.41888685e-11, 3.87885067e-11, 3.20224220e-11,\n",
" 1.60239831e-10, 2.44549814e-10, 2.46570360e-10, 2.36612631e-10,\n",
" 2.35549477e-10, 2.21420477e-10, 2.81050472e-08, 1.35770984e-07,\n",
" 1.31284940e-07, 5.42027435e-08, 2.22007048e-08, 9.16271928e-09,\n",
" 2.80943946e-08, 2.68265630e-08, 1.77149456e-07, 4.07674877e-07,\n",
" inf])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# set up an empty array\n",
"D = np.zeros((len(nd.iloc[:, 2])))*np.nan \n",
"\n",
"# loop through length of dataset\n",
"for x in range(len(nd.iloc[:, 2])-1):\n",
" # size minus one since it requires the values in the step after to exist \n",
" if np.sum(nd.iloc[x, 5]) < 0.85:\n",
" D[x+1] = lower_test(r, x, t, F)\n",
" elif np.sum(nd.iloc[x, 5]) >= 0.85:\n",
" D[x+1] = upper_test(r, x, t, F)\n",
"D"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "69c78f63",
"metadata": {},
"outputs": [],
"source": [
"# run a quick least squares fit\n",
"from scipy.optimize import curve_fit\n",
"\n",
"def linear(m, x, b):\n",
" # basic linear equation for fit\n",
" return m*x+b\n",
"# write a mask to exclude some data from the quick fit\n",
"mask = (~np.isnan(np.log(D))) & (~np.isinf(np.log(D)))\n",
"\n",
"#opt = optimized output, cov = covariance\n",
"opt, cov = curve_fit(linear, 10**4/T[mask], np.log(D[mask]/r**2))"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "7b9ad3a2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(-30.0, -5.0)"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEKCAYAAADuEgmxAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAsrUlEQVR4nO3dd5gUVdbH8e+BIYhZRMVAck1DlhEFI4KKLIIJ06yiqIgouqgoiFnZVTDhKlFZUVFBFMWwCpjDgg5IGHAxArK6r4q7KGKE8/5xe7AZeqB76J7q6fl9nqee6a6u7j6E4lB17z3H3B0REZFkVIs6ABERqTyUNEREJGlKGiIikjQlDRERSZqShoiIJE1JQ0REkpZ1ScPMbjCzf5vZ3NjWJeqYREQkyIs6gDLc5e63Rx2EiIisL+uuNEREJHtl65XGxWZ2FlAEXO7u/y19gJn1BnoDbLnllm323XffCg5RRKRymz179jfuXi+V91gUZUTMbAawS4KXBgMzgW8AB24G6rt7r419XkFBgRcVFaU9ThGRXGZms929IJX3RHKl4e6dkjnOzMYCz2U4HBERSVLWjWmYWf24pycAxVHFIiIi68vGMY2hZtaKcHtqCXBBpNGIiMg6WZc03P3MqGMQEZHEsu72lIiIZC8lDRERSZqShoiIJC03ksYvv0QdgYhIlZAbSWPhQrj3Xli7NupIRERyWm4kja22gn794PDD4cMPo45GRCRn5UbS2GsvePBBKC6GFi1g6FD47beooxIRyTm5kTQAevaERYvg2GPhqqugXTtYsCDqqEREckruJA2A+vXhqadg4kRYuhTatIEbbtBAuYhImuRW0gAwg1NOCVcdPXrAjTdCQQGoCq6IyGbLvaRRYscdYcIEmDoVVqyAAw8Mt61+/DHqyEREKq3cTRoljjsuTMnt1SsMkLdqBW+9FXVUIiKVUu4nDYDttoOxY2H69DC+cdhhYYruqlVRRyYiUqlUjaRRolOnMKOqXz+47z5o3hxmzIg6KhGRSqNqJQ0ICwGHD4c334RateCoo+C88+B//4s6MhGRrFf1kkaJgw+GuXNh4MCwMLBpU3j22XUvT5gAjRpBtWrh54QJEcUpIpJFqm7SAKhdG/76V5g1K8y26tYNzjiDyaO+oXfvsNTDPfzs3VuJQ0SkaieNEm3awHvvwU03weTJHHFRPl1XTyR0nA1Wr4bBg6MLUUQkGyhplKhZE669FubM4dO1jZjIaUzhBHbhy3WHLFsWYXwiIllASaO0Zs04vcE7XMEwjuElFpFPTx4EnAYNog5ORCRaShoJ3PSXPEbWuYIWzGcBzXmQc5hW7Vju7r806tBERCKlpJFAYSGMGQO/NtyLDrzGNTvcxxE13uL4a5rBiBFq9iQiVZaSRhkKC2HJEljj1bhlRV9qLF4I7dvDRRdBhw7w0UdlvlfTdUUkVylpJKthQ3jxRRg3DubPD82ebr8d1qxZ77AJE9B0XRHJWUoaqTCDc84JBRCPOQYGDAjNnoqL1x0yeHCYnhuv9HRdXYmISGWlpFEeu+4KU6bA44/DZ5/B/vuHNR6//FLmtNyS/RMmhLwTfyVyzjlKHCJSOShplJcZnHpqaPZ08slw/fVwwAF02Xl2wsNLputeein8+uv6r/36a9gvIpLtlDQ2V7168Oij8Mwz8PXXTP3qQG7PG0gtflp3SJ06MGRIeLxiReKPKWu/iEg2UdJIl27dYNEiqp3dk8t/u42Fea04mLdp2DBM3y0sjDpAEZHNp6SRTtttBw88ANOmseduP/GWHcqS7pdS2P33Zk916yZ+a1n7RUSyiZJGJhx1VJhRdfHFcM89odnTyy8DoZVHzZrrH16zZtgvIpLtlDQyZautQsJ4882QFTp1gvPPp7DrSsaNC8s+zMLPceN0+0pEKgcljUw75JDQ7OnKK0N2yM+ncNvnWLIkVCNZsiRxwtBaDhHJRkoaFWGLLeC220Kzp7p14bjjQqb45puEh2tVuYhkKyWNilRQAEVFcMMN8MQTkJ8PkyaFzBAnmVXlIiJRiCRpmFkPM1toZmvNrKDUa4PM7GMzW2xmx0QRX0bVrBkWAs6eHQY0Tj0VTjoJvtx0syc1gRKRqEV1pVEMnAi8Eb/TzPKB04CmQGdghJlVr/jwKkDz5vDPf4bbVi+8EK46HnwQvOxmT2oCJSJRiyRpuPsH7r44wUvdgcfd/Wd3/wz4GGhbsdFVoLy8MEA+fz40axaKUB17LHf1X0adOusfGr+qXEQkKtk2prEb8Hnc8+WxfRsws95mVmRmRV9//XWFBJcxe+8Nr78Of/sbvPUWJ1zTlFdPGUmjBmvXTcsdMyYcWnpGlWZZiUhFyljSMLMZZlacYOu+sbcl2OcJ9uHuY9y9wN0L6tWrl56go1StWlgMWFwMBx1E2wf78lnjI1n74ccsWRIOKV0dt2fPDfdplpWIZFLGkoa7d3L3Zgm2ZzbytuXAHnHPdwe+yFSMWalRI5g2De6/P6zvaNEC7riD/pes2aA67po1G1bM1SwrEcmkbLs9NRU4zcxqmVljYC/g3YhjqnhmcO65odlTp05wxRVM/fZg8lmY1NuXLs1wfCJSZUU15fYEM1sOtAOeN7OXANx9ITAJWAS8CFzk7mvK/qQct9tuoeT6o4/yBz7mfVozmFvI49eNvq16bs43E5EsYO4JhwwqlYKCAi8qKoo6jIzad4evuOG/l3AaE5lLS3oxjvfZv8zjc+CPVUQyzMxmu3vBpo/8XbbdnpIyXPu3nehZ83GOZwo783+8S1uGcPV6zZ5KNGwYQYAiUiUoaVQShYWh3uHchsfTlEU8teVZXM1fmWutacc7647Teg4RySQljUqksDBUxf3Wt+eUVePgpZfYY4fVvMUh3M2f2XePH9QlUEQySkmjMjv6aLb8rJhqF/XlUobzQV5zCuu/EnVUIpLDlDQqu623hnvvDSvKq1eHjh3DCr+VKzc4VKvHRWRzKWnkisMOCzWsBgwIfcqbNoXnn1/3snp0iEg6KGnkki22gKFDQ/Xc7baDrl3hzDNhxQr16BCRtFDSyEVt24Z+HdddB48/Dvn5HLB0csJD1aNDRFKhpJGratWCG28MnQJ3350n6MFkTmJn/rPeYerRISKpUNLIdS1bwqxZvH/qrfyR51lEPmfyEOBa0yEiKVPSqAry8mj9+FVMGzqPT2vtx0P05JXaf+SRv36uNR0ikhIljSqk24B9KPjhDRg+nA7VXueEa5rC6NGwdm3UoYlIJaGkUdVUrw6XXBKaPbVtC336hLUdn3wSdWQiUgkoaVRVjRvD9Omh2dOcOdC8Odx1V+jsJCJShqSThpkVmFl/MxtmZjeZ2SlmtkMmg5MMK2n2tGhRuNq47DI45BD44IOoIxORLLXJpGFmZ5vZHGAQsAWwGPgKOASYbmbjzUwTNyuz3XaDqVPD8vCPPoJWreAvf9mwl6yIVHl5SRyzJXCwu/+Y6EUza0Voy6plYpWZGZxxRmgv269fWCr+xBOhHnvr1lFHJyJZYpNXGu5+X1kJI/b6XHd/Ob1hSWR22gkmToSnnoL//AcOOACuuQZ+/jnqyEQkCyQ1pmFmtc3sZDMbbmZPmNlDZnalmTXNdIASkRNOCGMdZ54ZVgC2bg0zZ0YdlYhELJkxjRuAt4F2wCxgNDAJ+A241cymm1mLTAYpEdl+e/j73+Ef/4BVq6B9+zBYXrryoYhUGcmMabzn7jeU8dqdZrYToIHwXNa5c1jXMXBgmJb7zDNhqm6HDlFHJiIVLJkxjec38fpX7l6UvpAkK22zDYwYAa+9FgbNjzwSLrwQvvsu6shEpAJt1uI+MxuTrkCkkjj88NDs6fLLYcwYaNYs3L4SkSohmTGNHcrY6gJdKiBGyTZ16sDtt8M774R2s126QM+e8O23UUcmIhmWzJXG10ARMDtuK4ptO2UuNMl6Bx4YSpBccw08+ijk58OTT0YdlYhkUDJJ41PgCHdvHLc1cffGwP9lOD7JdrVqwc03w3vvwa67wsknQ48e8H/6qyGSi5JJGncD25fx2tD0hSKVWqtWMGtWKD8ydWq46njkEXCPOjIRSaNkV4TPK+O1v6U/JKm0atSAQYNg7lzYZ5+wMLBrV1i+POrIRCRNyjV7SrOmZKP22w/efBPuvjtM0W3aNMy00lWHSKVX3im3BWmNQnJP9epw6aWwYAG0aQMXXBDKr3/6adSRichmKG/S+CqtUUjuatIEXn45tJUtKgrNnu6+W82eRCqpciUNd++c7kAkh5lB796hAOIRR0D//nDooWr2JFIJpdK5T0UJZfPsvjs89xw8/DAsXqxmTyKVULKl0TsBIzIci1QFZvCnP4Wrjm7dQrOnAw8MM65EJOslU0akkLAe44R0famZ9TCzhWa21swK4vY3MrMfzWxubBuVru+ULLPzzqEz4OTJ8MUXavYkUkkkc6XxAHCyu3+dxu8tBk4E3kjw2ifu3iq29Unjd0o2OumkcNVxxhkwZAhf1N+f9tVnYQZ5edC3b9QBiki8ZJLGTcADZrZFur7U3T9w98Xp+jyp5HbYAcaP594uL7Dmv9/x5tr23M7l1FyzmpEjlThEskkyK8L/QrjaeDrj0QSNzex9M3vdzA4t6yAz621mRWZW9PXX6bwIkqj0e+FYmrKQsZzP5dzJfFpwGK8zcmTUkYlIiaQGwt39EVKsM2VmM8ysOMHWfSNv+xJo4O6tgcuAR81smzJiGuPuBe5eUK9evVRCkyz2PdtwIaPowCsYzuscwQjU7EkkWyQ95dbdX07lg929k7s3S7A9s5H3/OzuK2KPZwOfAHun8r2SG16jAy2Yz5305wJGQ7NmvHLlizRqBNWqQaNGMGFC1FGKVD2prNPoYWZbxx5fY2ZPmdn+6QzGzOqZWfXY4ybAXoTS7FIFrWZLLudODuZtVq7ZiiOHHcsNS89mO/+WpUvDekElDpGKlcqK8Gvd/XszOwQ4BhgPlOtus5mdYGbLgXbA82b2Uuylw4D5ZjYPmAz0cXe1g6siOnZMvH/Lju04IO99bmEwf+IRFpHP8Uxh9eqwzENEKk4qSaOkWNAfgZGx20w1y/Ol7j7F3Xd391ruvrO7HxPb/6S7N3X3lu6+v7s/W57Pl8ppxowNE0fHjmH/x5/X4lpu4QDe40vqM4UTmcgp/LRUzZ5EKlIqSePfZjYaOAV4wcxqpfh+kU2aMSNUUC/ZZswI+xs0CD/n0pq2vMvVDKE7z/BBtfxwj0pl10UqRCr/6J8CvAR0dvf/ATsAAzIRlEhpQ4ZAnTrh8W/U4K9cTbvac/mtyd6hLEm3bmr2JFIBkikjshWAu69296fc/aPY8y/dfVr8MSKZUlgY+jg1bBjKVzVsCJffvx/1/vUW3HVXKL/etCmMHaurDpEMMt/ECWZmLwNzgWeA2e7+Q2x/E6AD4QpkrLtPzmyoZSsoKPCioqKovl6ywSefwPnnw6uvhoGQsWOhceOooxLJamY2291TaqqXzIrwjsDLwAXAQjP7zsxWAI8AuwA9o0wYIgDsuWcYABk9Gt59F5o1g3vugbVro45MJKds8kqjMtCVhqzn88+hTx944QU4+GB44AHYZ5+ooxLJOhm50ij1BbuZWXszO6xkSy1EkQqwxx6h2dNDD4UKui1bwq23wm+/RR2ZSKWXyorw24C3gWsIs6YGAFdkKC6RzWMGZ54ZkkbXrjBoEBx0EMyfH3VkIpVaKlcaxwP7uHsXdz8utnXLUFwi6bHLLqHR0xNPhNtWbdrA9dfDL78AYYmH6lmJJC+VpPEpUCNTgYhk1Mknh6uO00+Hm26C/ffnxZvepXdvWLo0zNJVPSuRTUslaawG5prZaDO7p2TLVGAiaVe3bhjneP55WLmSo65vxw2rB1CbH9cdsno19OypxCFSllSSxlTgZuAdYHbcJlK5dOkCxcXcz/kM4Hbm04JD4zoPr1mjKw6RsmjKrVRZjRpB46Wvcj/nsSefch99GcitrGJrIKw6X7Ik0hBFMiojU27NbIGZzS9rK3+4ItEaMgTerfN7s6cLGUkxzTiKaQAsWxZxgCJZKC+JY7pmPAqRCBQWhp89e27J5Wvu5Al6MI5eTOMYxnEOd+92B7B9pDGKZJtkxjSWufvSsjYAM7MMxymSEYWFMH58qKA7k3a05n2GcDVn8RCzvs+HZ8rsTixSJSWTNF41s35m1iB+p5nVNLMjzWw80DMz4YlkXnwF3V+sNmMbDmHaLe+xRaOd4fjj4bTT4Ouvow5TJCskkzQ6E7r2PWZmX5jZIjP7DPgIOB24y90fzGCMIhlXWBgGvdeuDT+7DG4N770Ht9wCU6bw0575XFLvMaqZaxGgVGkpzZ4ysxrAjsCPsUZMWUGzpySTnh26iJ0H9aLt2lk8QzcuZCQr6+zKmDG/j4uIVEYZL1gIbEXo2LeDmanVq1QJ/Ubk027t21zGHRzFdBaRz6mrxzH46so/XV0kVclMud3WzK42swXATGA0MAlYamZPmFmHTAcpEqVly2At1bmLy2jBfObSinGcy5hlx2ghh1Q5yVwtTAY+Bw51933c/RB3L3D3PYBbge5mdm5GoxSJUIO4KSCf8AeO5BX6MJJ2NjM0e7r3XjV7kiojmc59R7n7w4nGMNx9trv/2d0fyEh0IllgyJAwJbeEU42H6/Rhxl3FcOih0K8fHH44fPhhdEGKVJBU+mlsUOHWzHZMbzgi2Sd+Sq5Z+DlmDJxwaYPQHfDBB6G4GFq0gKFD1exJcloyYxodzGw58IWZTTOzRnEvT8tYZCJZpPSU3HWzpsxCWdxFi0IhxKuugnbtYMGCCKMVyZxkrjSGAse4ez1gDDDdzA6KvaaV4CIA9evDk0/CpEmhMUebNnDDDeuaPYnkimSSRk13Xwjg7pMJHfzGm9kJgOYcipQwgx49wlXHKafAjTdCQUFYJCiSI5JJGr+a2S4lT2IJpCNwPbBXpgITqbR23BEeeQSefRa+/Tb0Jr/ySvjxx02/VyTLJZM0BgI7x+9w9+XA4YQptyKSSNeusHAhnHsuDBvGh3Vacoi9RV4e9O0bdXAi5ZPMlNsZ7j4vwf6V7j4kM2GJ5Ihtt6Vv3hg6MoM8fuMNDuOuNf14aOQqJQ6plFKZcntUJgMRyVVjxsArdKQ5C7iHS7iI+yimGZ+Onh51aCIpSyppmFlP4PIMxyKSk9asCT9XsyX9uZtDeZOfqM2La48Ot67+979I4xNJRTLrNK4GzgZOzHg0IjmoevX1n7/DwbRiLrfZwNABKl/NnqTySOZK43rgXHdfnelgRHJR794b7vuZ2izt81eYNQvq1QvNnk4/Xc2eJOslkzTOASarZIhI+YwYARde+PsVR/Xq4fmIEYRFgO+9BzfdFBYH5ufD449DCn1uRCpSMrOnHgWuAJ5O15ea2TAz+5eZzTezKWa2Xdxrg8zsYzNbbGbHpOs7RaI0YkQoSeUefo4YEfdizZpw7bUwZw40bhyuOI4/Hr74IqpwRcqU1EC4u78C9Enj904Hmrl7C+BDYBCAmeUDpwFNCW1mR5hZ9TI/RSSXNGsG77wDw4bBtGn8slc+A3YcpxazklWSnnLr7sXp+lJ3n+buJaVAZwK7xx53Bx5395/d/TPgY6Btur5XJOvl5cEVVzD1lvnMWt2CYSvO5UWOgaVL6NlTiUOil1LLVjP7o5ldaWbXlWxpiKEX8I/Y490IDZ9KLI/tSxRLbzMrMrOirzV4KDnmjOv34nBeoy/30Y5/UkwzLlhzH316q9mTRCuVxX2jgFOBfoTqtj2Ahhs5foaZFSfYuscdMxj4DSj5/1OiqrkJRwTdfUysg2BBvXr1kv1liFQKP/wQmj2NpC/NKOZtDuY+Lub51Ueo2ZNEKpUrjfbufhbwX3e/EWgH7FHWwe7eyd2bJdiegXULBrsChe7rpoosL/WZuwMaDZQqbRkN6cyLnM3fac4CaNkyjHuo2ZNEIJWkUVKic7WZ7Qr8CjQuz5eaWWfgKqBbqfUfU4HTzKyWmTUmVNF9tzzfIVKZVdvgzDTGczbNbBF07gxXXsmKvdtxzK4LqFYNDZRLhUklaTwXmxo7DJgDLAEeK+f33gtsTWjoNDd266uk7PokYBHwInCRu68p53eIVFoXXJB4f/c+9eGpp3jz4oms/Wwpz37Zhmv9Rr5Y+gu9eytxSOaZb2IRkZnVd/cvS+2rBdR295WZDC5ZBQUFXlRUFHUYImnVt28odrhmTVgQ2Lv37+s7GjWCVUu/YTiXUsijzKc5vRjHNw0LWLIkyqilMjGz2e5ekNJ7kkga/wC2B14j/O//rbjpsllBSUOqGoubMtKVZxlFH3bhPwxjAANXXw9bbBFdcFJplCdpJLMi/FjgCELSOAGYaWZPxaa8NihPoCKSPs9xHE1ZyDh6MZDboFUrePvtqMOSHJXsivCf3P1Fd780lpUuB/KAe81MA9UiEVvJdvRmLJ2YDr/8AoceCpdcAqtWRR2a5JhU1mlsZ2YHmNkBwLfuPsLduwGHZC48EUmkdLn1Eq9V7wQLFsDFF8O990Lz5jBjRsUGJzktmX4aNc3sQcJsqTHAWGCJmY0zs5ru/ktmQxSR0hKVW1+3f6ut4J574I03QjHEo46C88+HlVkxb0UquWSuNK4BagB7uHtrd28FNCDcnro2g7GJSBk2Wm69xCGHwNy5cNVVMG5cKLv+7LNRhCs5JJnZU8VA29JNmMxsK2CmuzfLYHxJ0ewpkU0oKoJevcKtqzPOgOHDYUe1yKnqMjJ7ClibqGufu6+ijLpQIpJlCgpC4rjxRtZMfIJvds7nVJtEo4auBYGSkmSShpvZ9ma2Q+kNUMlNkcqiZk0m7HkdbZjDp2sbMZFTuWvZiQzs+aUShyQtmaSxLTC7jG3rzIUmIunWpw/MW9OM9rzDAIbSmReZvyafN899UC1mJSnJLO5r5O5N3L1xgq1JRQQpIulRsmxjDXnczgBaMo8FNGfUz+fAscfC0qXRBihZL5kpt4028bqZ2e4bO0ZEstNH7M0RvMZF3AtvvRVazo4cCWt151kSS+b21DAze9LMzjKzpma2k5k1MLMjzexm4G1gvwzHKSIZ4lRjBBdBcTG0axcqJXboAB99FHVokoWSuT3Vg7AeYx/gPuBNQt+L84HFwJHuPj2TQYpIBWjUCF56KazpmDcPWrSAO+4IZXZFYpKtPbXI3Qe7+xHuvo+7t3L30939EXf/KdNBikh61K27if1mcM45PHXLIqbb0XDFFcyt057nbltYYTFKdkulCRNm1t7MzojdqjrLzM7KVGAikn7Dh4fKIvFq1gz7S0yYAD0u3ZWjf3ya03iM3X75lKMHtmbeyTfDr79WbMCSdVIpWPgwcDuhQOEBsS2llYQiEq3CwnD3qWHDcFHRsGF4Xlj4+zEXXFAyDm5M5DTyWcSTnETLJ68LiwRnz44qfMkCmywjsu5Asw+AfE/2DRVIZURE0ie+wVO87jzN0/X7wldfwYABcP31ULt2xQYnaZWpMiIlioFdUgtJRHLFMxwPCxfCWWfBrbeGZk/vvBN1WFLBUkkaOwKLzOwlM5tasmUqMBGJRrUy/lWoVg3YfvtwP+ull+DHH0Ml3UsvhR9+qNAYJTqpJI0bgOOBvwB3xG0ikkMuuGDj+/v2hbwuR7P1smLuo2/o3dG8Obz8csUFKZFJOmm4++uJtkwGJyIVb2O9Ovr2DQvG16yBVWzNxX4vh/E6X31bHTp1UrOnKiCZfhrfk7gEugHu7ttkIrBUaCBcpGLk5SVe67dltR9Zdfn1YTFg/fowahR07VrxAUpKMjIQ7u5bu/s2CbatsyFhiEjFKWtx+A9rt4ChQ+Gf/4TttoPjjoM//Qm++aZC45PMS2lxn4hUbSW3rMrc37ZtWMdx3XUwcWJoMfvEEyq7nkOUNEQkab17J7G/Vi248cbQKbBBAzjlFDjpJPjPfyokRsksJQ0RSdqmBsnz8sLiwLw86Du6JcycGdZ0vPBCuOp46CFddVRySa8Iz2YaCBeJVsmsqtJKEgqLF8O558Lbb0PnzjB6dLgKkUhlekW4iEhCY8ZsYv8++8Abb4Q1HW+8EZo9jRqlZk+VkJKGiGy2smZVrbe/WjXo1w+Ki/nXNm3hwgt5tXpH9q7+CX37VkiYkgZKGiKy2TY5qypO32GN2e/f0zmPsezPHOaubU6tkXdxUR81e6oMlDREZLMlNasqZtQoAOMBziOfRcygE3dxGX8afQgsWpTJMCUNlDREZLNtbFZVafFzb75gN7rzDKfzKHvxEbRuDUOGqNlTFtPsKRGpUGX166jHV3x1Sj+YNCmUXR83LiQRyZhKM3vKzIaZ2b/MbL6ZTTGz7WL7G5nZj2Y2N7aNiiI+Eal4X7NTWEU+ZUpYCHjAAXDNNfDzz1GHJnGiuj01HWjm7i2AD4FBca994u6tYlufaMITkUy58MKN75/ww/G0yFvE39ecCUOGsLJJ67BIULJCJEnD3ae5+2+xpzOB3aOIQ0Qq3sbGPyZMCIPnC5ZvTy/+zjG8yMovf8Dbt4fLLoPVq6MNXqIf0zCzZ4GJ7v6ImTUCFhKuPr4DrnH3N8t4X2+gN0CDBg3aLF26tIIiFpFMadQISp/KW/E9t9lA+voIluY14aMr76fTkA6RxJdryjOmkbGkYWYzSNxTfLC7PxM7ZjBQAJzo7m5mtYCt3H2FmbUBngaauvt3G/suDYSL5IZq1couTXUYr3M/57EXH/PRkRew15ShsI26M2yOrBoId/dO7t4swVaSMHoCXYFCj2Uud//Z3VfEHs8GPgH2zlSMIpJdNlaO6g0OpyXzGMYVNHllLDRtGgohSoWKavZUZ+AqoJu7r47bX8/MqsceNwH2Aj6NIkYRqXhDhkCdOmW//iN1uJJhtOefsO228Mc/wllnwYoVFRdkFRfV7Kl7ga2B6aWm1h4GzDezecBkoI+7fxtRjCJSwQoLQ5HDhg3Deo6yypPMrt6W2gtnM3zb61j76GOh7PqTT1ZssFVU5APh6aAxDZHcVDKbamOTpg6sPY/nd+lF3SVzQrOn++6DnXeuuCArsawa0xAR2VzJXHnM+qkluy6bxSD+ys9PPsfPf8iHhx9Ws6cMUdIQkaxWWAhLloTWG2W13/hlbR63MpCWzGX2D/uGcY6uXeHzzys01qpASUNEKo1NNftbzL4c6m8wqM5weO21MMNq9Gg1e0ojJQ0RqTQ2NbsKYC3VuXX1JbBgQahf1acPdOoEn3xSMUHmOCUNEak0So9xbFSTJjBjBowdC7NnQ/PmcPfdZbcZlKQoaYhIpRI/xlG3buJj1u03g/POg4ULoWNH6N8fDj0UPvigosLNOUoaIlJpDR8ONWqsv69GjbB/PbvvDlOnwiOPwOLFoV/HX/6iZk/loKQhIpVWYSH8/e+/365q2DBcWAweHOpYNWoU1noA4YDCwtBStnv3cNCBB8LcuRH+CiofJQ0RqdTib1cNGQLjx4dKue7hZ+/ecYkDwsK/SZPCCvIvvkjY7GnChJBwNkg8oqQhIrlj8OANV4+vXh32b+DEE8NVxxlnhGyz//4wc+a6VegbTTybobInJCUNEckZy5altp8ddgiXJi+8AN99B+3bs/rCy/FSmafMxJOiTCekiqCkISI5o6zFf5taFMixx4YZVhdcwPnf38l8WnAEr653SJmJJwUpXQllKSUNEckZiRb/1akT9m/SNtvAyJGcvvMrGM6rHMkILmRrQg+4TSaeJKR8JZSFlDREJGeUXvzXsGF4XliY/Gdsf2IHWjCfO+nPBYymmGZ05h906bL58ZX7SiiLKGmISE6Jn021ZElqCQPC8MZqtuRy7qQ977CKrfgHXej4UE/4dvPa+2zWlVCWUNIQEYkTf6toFgfRmve5hcF0/2FCaPb01FPl/uzCQujZ8/cS79Wrh+epJrZkZGqWlpKGiEic0reKfqEW13IL3eu/B/Xrh0ZPPXrA//1fyp89YUKYrFVS/mrNmvA83bOnMjlLS0lDRCROWbeQzhjWGt59NxwwdWq46njkkZSaPVXU7KlMfo+ShohInI0OpteoAVdfHUqP7L03nHkmHHccLF+e1GdX1OypTH6PkoaISCmbHEzfbz946y246y545ZXQ7Gns2E1edVTU7KlMfo+ShohIeVSvDn/+c2j21KZNGDQ46ij47LMy31JRs6cy+T1KGiIim2PPPUOzp9Gjw5hHs2Zwzz0JW8ymYx1JMjL5PeYpDOJkq4KCAi8qKoo6DBGp6j7/PLSXfeEFaN8eHngA9t036qjKZGaz3b0glffoSkNEJF322AOeew4eeih0B2zVCm69FX77LerI0kZJQ0QknczCrKpFi6BrVxg0CA46CObPjzqytFDSEBHJhF12gcmTw/b552Gw/Lrr1mv2VBkpaYiIZNJJJ4WrjtNPh5tvDsnj3Xcz/rUqIyIiUlnVrRvGOZ5/HlauhHbtYMCADZdtp4nKiIiI5IIuXUKzp/PPh9tvh5Yt4Y030v41KiMiIpIrttkGRo0KK8nXroXDD4eLLoLvv0/bV6iMiIhIrunQIcyo6t8fRo4MiwJfeiktH60yIiIiuWjLLeHOO+Htt8Pjzp3hnHPgv//drI9VGRERkVzWrh3MmRMq6D78cCi7/vTT5f44lRHZBJUREZGc8f770KtXKL9+6qnwt79BvXoZ+SqVERERqexax5o93XILTJkSyrA/+mhKzZ4yKZKkYWY3m9l8M5trZtPMbNe41waZ2cdmttjMjokiPhGRSNWoEebHvv8+/OEP4b5S9+7w739HHVlkVxrD3L2Fu7cCngOuAzCzfOA0oCnQGRhhZtUjilFEJFr5+WGQ/I47Qvn1pk3h/vsjveqIJGm4+3dxT7cESn4HugOPu/vP7v4Z8DHQtqLjExHJGtWrw2WXhem5rVuHhYFHH73RZk+ZlBfJtwJmNgQ4C1gJdIjt3g2YGXfY8ti+RO/vDfSOPV1lZovTGN6OwDdp/Lx0yubYILvjy+bYILvjU2zll/74ZsyAJk3S8Un7pPqGjCUNM5sB7JLgpcHu/oy7DwYGm9kg4GLgesASHJ/wOszdxwBj0hVvPDMrSnVGQUXJ5tggu+PL5tggu+NTbOWXzfGZWcrTTjOWNNy9U5KHPgo8T0gay4E94l7bHfgizaGJiEg5RTV7aq+4p92Af8UeTwVOM7NaZtYY2AvIfA1hERFJSlRjGrea2T7AWmAp0AfA3Rea2SRgEfAbcJG7r4kgvozc9kqTbI4Nsju+bI4Nsjs+xVZ+2RxfyrHlxIpwERGpGFoRLiIiSVPSEBGRpClpxDGz/ma20MyKzewxM6sdcTzjzOwrMyuO27eDmU03s49iP7fPsviGmdm/YmVippjZdtkSW9xrV5iZm9mO2RSbmfWLlc9ZaGZDo4itrPjMrJWZzYyV/ikys0gW3ZrZHmb2qpl9EPt9ujS2P/LzYiOxZcs5kTC+uNeTOy/cXVsY19kN+AzYIvZ8EnB2xDEdBuwPFMftGwoMjD0eCNyWZfEdDeTFHt8WVXyJYovt3wN4iTABY8dsiY2wwHUGUCv2fKcs+3OdBhwbe9wFeC2i2OoD+8cebw18CORnw3mxkdiy5ZxIGF/sedLnha401pcHbGFmeUAdIl4j4u5vAN+W2t0dGB97PB44viJjipcoPnef5u6/xZ7OJKy1qXBl/N4B3AVcSRmLRitCGbFdCNzq7j/HjvmqwgOLKSM+B7aJPd6WiM4Nd//S3efEHn8PfED4D1/k50VZsWXROVHW7x2kcF4oacS4+7+B24FlwJfASnefFm1UCe3s7l9C+EsA7BRxPBvTC/hH1EGUMLNuwL/dfV7UsSSwN3Comc0ys9fN7ICoAyrlz8AwM/uccJ4MijYcMLNGQGtgFll2XpSKLV5WnBPx8aV6XihpxMTugXYHGgO7Alua2Z+ijaryMrPBhLU2E6KOBcDM6gCDiVVUzkJ5wPbAQcAAYJKZJSqrE5ULgf7uvgfQH3ggymDMbCvgSeDPvn4B1MiVFVu2nBPx8cXiSem8UNL4XSfgM3f/2t1/BZ4C2kccUyL/Z2b1AWI/I7uNURYz6wl0BQo9dsM0C+xJ+A/BPDNbQrhFMMfMEtVHi8Jy4CkP3iUsfI1koL4MPQnnBMATRFh92sxqEP7Rm+DuJTFlxXlRRmxZc04kiC/l80JJ43fLgIPMrE7sf3gdCff8ss1UwglM7OczEcayATPrDFwFdHP31VHHU8LdF7j7Tu7eyN0bEf6R3t/d/xNxaCWeBo4EMLO9gZpkV+XWL4DDY4+PBD6KIojYufkA8IG73xn3UuTnRVmxZcs5kSi+cp0XUYziZ+sG3Eiog1UMPExsJkuE8TxGGF/5NfaHeS5QF3iZcNK+DOyQZfF9DHwOzI1to7IltlKvLyG62VOJft9qAo/E/u7NAY7Msj/XQ4DZwDzCffo2EcV2CGGwdn7c37Eu2XBebCS2bDknEsZX6phNnhcqIyIiIknT7SkREUmakoaIiCRNSUNERJKmpCEiIklT0hARkaQpaUiVsJHKshutjmpm9c3sYzObY2Zbl/HZUxN8bv1YRdG5se1bM/ss9nhGgs/YIlY+pHrs+Ytm9j8zey7Bsaeb2WAzO9vM7o3tq2Zm42O/TjOzGVFUepXcp6QhVcWDQOcE+wcCL7v7XoT5/QNLXogliacJC7PGA5NjK2qJO+ZEYFWCz+0MPOfurdy9FWHx2YDY804Jju9FWBFe0t54GHBmGb+WzsCLcTEYMAqoAZznYR79w0DfMt4vUm5KGlIleNlVbxNWR40lh8cIZayfdPfhhH/4x5a8MVbD5zLglgSf25nUCtMVEreK2d1fBr4vfVAsQbQiLAAsMZywuO0sd18b2zcVOD2F7xdJSl7UAYhEbL3qqGa2U+zxr4RaQeu4+32l3nszcAewXmmI2C2mfdx9UTIBmFlNoIm7L0ni8NbAPHf3WD3DMwjlbo7w38tv4+7/NbNaZlbX3VckE4dIMnSlIVIOZtYK+IO7T0nw8oFsWBJ7Y3YE/pfksaWvYOYADUlcQPArQsVmkbRR0pCqrrzVUdsBbWKVQd8C9jaz12KvHUvcmEMSfgSSbS18NKGLXol/AacAE82saalja8c+WyRtlDSkqitXdVR3H+nuu3qoDHoI8KG7HxF7uSNhUD0p7v5foLptoie9mW1LaBu63u0md38H6AM8b2YNYscasAuhAJ1I2mhMQ6oEM3sMOALY0cyWA9e7+wPArYSGR+cSyuP32MzvqQf85Kk3BppGSD4zYp/zJrAvsFUs3nMJfZ03mK4L4O7Pxb77RTM7lNAjYWb8OIdIOqjKrUgaxbo97u7ut6b4vtbAZe5e1jRbzOx+4H53n5nE5w0HpsZmYYmkjZKGSJYws17A+Li1GpvzWee7+9hNHymSGiUNERFJmgbCRUQkaUoaIiKSNCUNERFJmpKGiIgkTUlDRESS9v/Gg/qSYdPsNwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# load in green datapoints (WSAP_B) that was digitized to compare\n",
"gd = np.loadtxt('Default_Dataset.csv', delimiter=',')\n",
"\n",
"# plot\n",
"x_array = np.linspace(0, 24)\n",
"plt.plot(10**4/T, np.log(D/r**2), 'o', c='blue')\n",
"plt.plot(x_array, opt[0]*x_array + opt[1], c='red')\n",
"plt.xlabel('10^4/T (1/K)')\n",
"plt.ylabel('ln(D/a^2)(ln(s^-1))')\n",
"plt.xlim([7, 24])\n",
"plt.ylim([-30, -5])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "10e1f271",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}