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openmod tutorial day evaluation Frankfurt 2017
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| Timestamp | I have attended the stream | I have attended the model Introduction in the afternoon | Please give us some feedback about the overall workshop [The amount of information covered at the workshop was reasonable for allotted time] | Please give us some feedback about the overall workshop [The overall atmosphere of the workshop was welcoming] | Please give us some feedback about the overall workshop [I learned skills that I will be able to use in my research/work] | Please give us some feedback about the overall workshop [The material presented matched the workshop description] | Please give us some feedback about the overall workshop [I would recommend this workshop to a friend/colleague] | Please give us some feedback about the overall workshop [The workshop was worth my time] | How did you perceive the pace of the workshop? | How was the balance of lecture to hands-on work? | Please select how you felt the instructors performed as a team [Instructors gave clear answers to your questions] | Please select how you felt the instructors performed as a team [Instructors were considerate] | Please select how you felt the instructors performed as a team [Instructors were good communicators] | Please select how you felt the instructors performed as a team [Instructors were enthusiastic] | Please select how you felt the helpers performed as a team [Helpers gave clear answers to your questions] | Please select how you felt the helpers performed as a team [Helpers were considerate] | Please select how you felt the helpers performed as a team [Helpers were good communicators] | Please select how you felt the helpers performed as a team [Helpers were enthusiastic] | Were there enough helpers at the workshop? | Do you have specific comments about the instructors or helpers? | How much of the information presented at this workshop was new to you? | Any comments on what was the best thing about this workshop? | Any comments on what needed the most improvement at this workshop? | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2017/04/19 9:44:34 PM GMT+2 | General Scientific Computing | Yes | Agree | Agree | Agree | Agree | Agree | Agree | Slightly slow | Balanced (lecture/hands-on) | Often | Often | Sometimes | Often | Often | Often | Often | Often | Yes | About half of it | Would have liked to attend the Advanced tutorial too (space was limited though) | |||
| 2017/04/20 9:06:40 AM GMT+2 | Advanced Topics | Yes | Disagree | Strongly Agree | Agree | Agree | Agree | Strongly Agree | Slightly fast | Too much hands on | All of the time | Often | Often | Often | Column 6 | All of the time | Column 6 | Column 6 | Yes | All of it | Coding samples should already be filled in jupyter notebook, so that there is no need to copy. | |||
| 2017/04/23 3:25:12 PM GMT+2 | Advanced Topics | Yes | Agree | Strongly Agree | Strongly Agree | Agree | Strongly Agree | Strongly Agree | Slightly fast | Balanced (lecture/hands-on) | All of the time | All of the time | All of the time | All of the time | Often | All of the time | All of the time | Often | Yes | Most of it | More time, better preparation / complete Installation | |||
| 2017/04/23 3:49:32 PM GMT+2 | Advanced Topics | Yes | Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Just right | Balanced (lecture/hands-on) | All of the time | All of the time | All of the time | All of the time | All of the time | All of the time | All of the time | All of the time | Yes | Most of it | ||||
| 2017/04/23 5:18:42 PM GMT+2 | Advanced Topics | Yes | Agree | Strongly Agree | Agree | Agree | Strongly Agree | Strongly Agree | Just right | Slightly too much hands on | All of the time | All of the time | All of the time | All of the time | All of the time | All of the time | All of the time | All of the time | Yes | just to thank them for their time, particularly in regards to preparation | About half of it | git clone is really the best way of distributing files, better than zip files, as overwritten files can be recovered and new files stored (yes I know I could also create a local git repo to do the same thing) / Jupyter notebooks are great, I hadn't used them before | the Jupyter notebooks should come with Creative Commons CC BY[-SA] licenses so that code and text can be extracted and worked into the openmod wiki and, if BY-SA, Wikipedia also / you should also have a feedback box on this form for any further comments / ps thanks for organizing all this | |
| 2017/04/23 5:28:06 PM GMT+2 | Advanced Topics | Yes | Disagree | Agree | Agree | Agree | Agree | Neither Agree nor Disagree | Slightly fast | Slightly too much lecture | Often | All of the time | Often | All of the time | Often | All of the time | Often | Often | Yes | Some of it | The visualisation | Please split the GIS and the database tutorial, that was too much for the allocated time. | ||
| 2017/04/23 8:12:20 PM GMT+2 | Advanced Topics | Yes | Neither Agree nor Disagree | Agree | Agree | Agree | Strongly Agree | Slightly fast | Balanced (lecture/hands-on) | Often | Often | Often | All of the time | Often | Often | Often | Often | No | Most of it | |||||
| 2017/04/24 9:01:44 AM GMT+2 | Advanced Topics | Yes | Agree | Strongly Agree | Agree | Agree | Strongly Agree | Agree | Slightly slow | Balanced (lecture/hands-on) | All of the time | All of the time | All of the time | All of the time | All of the time | All of the time | All of the time | All of the time | Yes | Thank you!! | Some of it | The whole atmosphere and the visualization part. | Preparation by participants was not satisfactory and hindered reasonable pace. | |
| 2017/04/24 9:21:49 AM GMT+2 | General Scientific Computing | Yes | Agree | Strongly Agree | Strongly Agree | Agree | Strongly Agree | Slightly fast | Balanced (lecture/hands-on) | Often | Often | Often | Often | All of the time | All of the time | All of the time | All of the time | Yes | Great job! | Some of it | The informal atmosphere which enables deeper exchange than classic conferences. It would be great if this could be kept in future and the workshops do not become to formal! | The amount of coffee ;-) | ||
| 2017/04/24 9:31:57 AM GMT+2 | Advanced Topics | Yes | Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Just right | Balanced (lecture/hands-on) | All of the time | All of the time | All of the time | All of the time | Column 6 | Column 6 | Column 6 | Column 6 | Yes | About half of it | ||||
| 2017/04/24 11:53:41 AM GMT+2 | Advanced Topics | Yes | Neither Agree nor Disagree | Strongly Agree | Agree | Strongly Agree | Strongly Agree | Strongly Agree | Slightly fast | Balanced (lecture/hands-on) | All of the time | All of the time | All of the time | All of the time | All of the time | All of the time | All of the time | All of the time | Yes | About half of it | It was a useful condensed package of ready-to-use tools for improving my daily work of start exploring new, relevant functions. Awesome! | I have struggled to pre install some of the requested softwares and files before the tutorial, so more detailed information on that would have been useful to me. | ||
| 2017/04/24 3:14:53 PM GMT+2 | Advanced Topics | Yes | Neither Agree nor Disagree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Just right | Slightly too much lecture | All of the time | All of the time | All of the time | All of the time | Often | All of the time | All of the time | All of the time | Yes | About half of it | Everything was just right, it seems to have been very well planned and well thought out | No improvement necessary, keep up whatever you're doing, you guys are great | ||
| 2017/04/25 11:29:39 AM GMT+2 | Advanced Topics | Yes | Agree | Strongly Agree | Strongly Agree | Agree | Strongly Agree | Strongly Agree | Slightly fast | Balanced (lecture/hands-on) | All of the time | All of the time | All of the time | All of the time | All of the time | All of the time | All of the time | All of the time | Yes | very good work. Maybe next time the handouts should be sent ahead of the wokshop so participants can get familiar with the topic. | Some of it | the open and welcoming atmosphere | Keep the tutorial day. | |
| 2017/04/25 12:00:56 PM GMT+2 | Advanced Topics | Yes | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Just right | Balanced (lecture/hands-on) | All of the time | All of the time | All of the time | All of the time | All of the time | All of the time | All of the time | All of the time | Yes | They did a grat work. It was very useful. | About half of it | Insides to a many interesing topics, that touch the every day work. New inspiration to use different tools, packages, | For preparing the material, program set up etc. on the personal computer a reminder about a week in advance would be great to have time to organise it. | |
| 2017/04/25 12:30:09 PM GMT+2 | General Scientific Computing | Yes | Neither Agree nor Disagree | Agree | Agree | Agree | Strongly Agree | Strongly Agree | Slightly fast | Balanced (lecture/hands-on) | All of the time | All of the time | Often | Often | Often | All of the time | Often | Often | Yes | They managed to be clear and friendly | About half of it | Good collaborative atmosphere: this always motivates people. Reasonable work for the time available | A few hours' tutorial is a good start but obviously not enough to get a thorough good introduction to the topic. It would be good to have a longer tutorial with more hands-on exercises. Since I guess the "teaching capacity" is limited, it could be an idea to have a short "real tutorial" plus a number of open spaces where people work together and only a smaller number of "supervisors" are at times present. | |
| 2017/04/25 5:39:42 PM GMT+2 | General Scientific Computing | Yes | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Just right | Balanced (lecture/hands-on) | All of the time | All of the time | All of the time | All of the time | All of the time | All of the time | Often | Often | Yes | About half of it | ||||
| 2017/04/26 10:41:16 AM GMT+2 | General Scientific Computing | Yes | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Just right | Balanced (lecture/hands-on) | All of the time | All of the time | All of the time | All of the time | All of the time | All of the time | All of the time | All of the time | Yes | This is an excellent setup for the tutorial. You had enough helpers so that it didn't slow the tutorial down and whenever I had a question, there was always a helper available. | Most of it | I thought this workshop was excellent. I learned a lot and can apply it all directly into my work. | I enjoyed the tutorial so much that I wish that it was 2-3 days long! | |
| 2017/04/26 12:03:44 PM GMT+2 | Advanced Topics | Yes | Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Just right | Balanced (lecture/hands-on) | Often | Often | Often | Often | Often | Often | Often | Often | Yes | About half of it | For me personally, the tutorial day and the personal chats with any of the participants, especially in the breaks, helping me to answer my question and/or solve problems. | More time for the tutorial workshop, especially the GIS session was quite stressful and should be split into (a) PostgreSQL / PostGIS Introduction and (b) QGIS itself. Moreover, in the graphing tutorial we did not use Inkscape - I believe we were somehow quite short in time... | ||
| 2017/04/26 12:22:24 PM GMT+2 | General Scientific Computing | Yes | Agree | Agree | Agree | Agree | Agree | Agree | Just right | Balanced (lecture/hands-on) | All of the time | All of the time | All of the time | All of the time | All of the time | All of the time | All of the time | All of the time | Yes | No, they were all fine | About half of it | The content were really targeted for the need of participants without a great knowledge of coding of getting aware of energy models functioning | Any in particular, I was just a bit disappointed that I could not follow the introduction to pyomo in the advanced topics workshop because it was in parallel with the general scientific computing workshop. Maybe it was a too advanced topic for the current workshop, but since pyomo is used by all the models presented at Openmod, I would have liked to know a bit about it even being new to coding. | |
| 2017/04/28 8:48:42 AM GMT+2 | General Scientific Computing | Yes | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Just right | Balanced (lecture/hands-on) | Often | Often | Often | Often | All of the time | All of the time | All of the time | All of the time | Yes | python instructor was a bit confusing but it was already the afternoon, might have been my atention | Most of it | |||
| 2017/05/09 8:36:17 AM GMT+2 | Advanced Topics | Yes | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Strongly Agree | Neither Agree nor Disagree | Just right | Balanced (lecture/hands-on) | All of the time | All of the time | All of the time | All of the time | All of the time | All of the time | All of the time | All of the time | Yes | Some of it | Stefan Pfenningers presentation on plotting fast was impressive | It should be emphasized that people should have everything installed. It cost 45 min out of 90 minutes in the Pyomo session. |
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# openmod tutorial day evaluation\n", | |
| "\n", | |
| "The following shows all data from the first openmod tutorial day at the openmod workshop in Frankfurt 2017. The data is generally split between the two streams that were offered." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "import numpy as np\n", | |
| "import pandas as pd\n", | |
| "%matplotlib inline\n", | |
| "import seaborn as sns\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "sns.set(rc={\"figure.figsize\": (14, 7)})\n", | |
| "sns.set_context(\"notebook\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "data = pd.read_csv(\"./openmod tutorial day evaluation.csv\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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AANzIynFHQECAtmzZombNmskYo7CwMDk4cJkEAABWynLY\nsG7dOuXOnVuLFi3S8ePH1b17d/3yyy8qUaKElfUBAABYPu7gppAAANxfWb6M4uuvv1Z0dLRcXV1V\nunRpLVy4UFFRUVbWBgAAIIlxBwAAD5sshw3JyclycnKyvb7x/wAAAFZi3AEAwMMly5dR1KtXT23b\nttUrr7wiOzs7fffdd6pbt66VtQEAAEhi3AEAwMMmy2FDz549tXLlSm3ZskWOjo5q06aN6tWrZ2Vt\nAAAAkhh3AADwsMly2CBJ//znP/XPf/7TqloAAABuiXEHAAAPjyzfswEAAAAAACAjhA0AAAAAAMBS\nhA0AAAAAAMBShA0AAAAAAMBS93SDyLuVmpqqjz/+WDExMXJwcFBERISKFi2anSUAAAAAAID7LFvP\nbFi9erUkae7cueratasiIiKyc/UAAAAAACAbZOuZDfXq1VOdOnUkSX/88Yfy58+fnasHAAAAAADZ\nIFvDBklydHRUaGiovv/+e40bN+6203p6usrR0SGbKgOQmXYzuuV0CenMfPszy5bVqtccy5ZlBecy\nOV3Bzby83CxZzoO2r/898s2cLgEAAOCRk+1hgySNGDFCPXr0UIsWLbR8+XK5urpmOF18fGI2Vwbg\nYRIXl5DTJTxWHtX9/bBul1XhDwAAwP2QrfdsWLx4sSIjIyVJuXPnlp2dnRwcOHMBAAAAAIBHSbae\n2RAYGKg+ffrozTffVEpKivr27SsXF5fsLAEAAAAAANxn2Ro2uLq6/h979x0QxbW2AfxZqiIiKNiu\nxoKKJbFGxUYRKwpG7FxRYqLRiCUqQrAh1ogVjBpjol5bomLU2GIvUbFdFTXEXrCCIF0W2H2/P/jY\nC6GIui4iz+8fEWZn3jnnzJl3352ZxZIl2rvHmoiIiIiIiIjePzq9jYKIiIiIiIiIPnwsNhARERER\nERGRVrHYQERERERERERaxWIDEREREREREWkViw1EREREREREpFUsNhARERERERGRVrHYQERERERE\nRERaxWIDEREREREREWkViw1EREREREREpFUsNhARERERERGRVrHYQERERERERERaxWIDERERERER\nEWkViw1EREREREREpFUsNhARERERERGRVrHYQERERERERERaxWIDEREREREREWkViw1ERERULEVH\nR8Pe3h63b98u7FCIiIg+OCw2EBERUbGTlpaGqVOnokSJEoUdChER0QeJxQYiIiIqdr777jv0798f\n5cuXL+xQiIiIPkgGhR0AERERkS5t27YNZcuWRbt27bBy5cpXLm9hYQIDA30dREZERLriPnFDYYeQ\njVG9wo4gJyur0m/1ehYbiIiIqFgJCQmBQqHA6dOnER4eDh8fHyxfvhxWVla5Lv/iRbKOIyQiIip8\nUVEJr1wmv4IEiw1ERERUrGzY8L9Pszw8PODv759noYGIiIjeDJ/ZQERERERERERaxSsbiIiIqNha\nt25dYYdARET0QeKVDURERERERESkVSw2EBEREREREZFWsdhARERERERERFql02c2pKWlwc/PD48e\nPUJqaipGjBgBJycnXYZARERERERERO+YTosNO3fuhLm5OQIDA/HixQv07NmTxQYiIiIiIiKiD4xO\niw1dunRB586dNf/X19fX5eaJiIiIiIiISAd0WmwoVaoUACAxMRGjR4/G2LFj813ewsIEBgYsSFDe\nPFePKewQcljz+ZLCDqHYsLIqXdghFCsfant775pc2CHkwHmEiIiIijqdFhsA4MmTJxg5ciTc3d3h\n4uKS77IvXiTrKCoi7YmKSijsEIoNtrVusb11pyBt/aEWf4iIiOjDoNNiw/PnzzFkyBBMnToVrVq1\n0uWmiYiIiIiIiEhHdPrVlytWrEB8fDyWLVsGDw8PeHh4ICUlRZchEBEREREREdE7ptMrGyZPnozJ\nk9+/e2OJiIiIiIiISHt0emUDEREREREREX34WGwgIiIiIiIiIq1isYGIiIiIiIiItIrFBiIiIiIi\nIiLSKp0+IJKIiIiIiIhyGhO4s7BDyGGJt2thh0BFGK9sICIiIiIiIiKtYrGBiIiIiIiIiLSKxQYi\nIiIiIiIi0ioWG4iIiIiIiIhIq1hsICIiIiIiIiKtYrGBiIiIiIiIiLSKxQYiIiIiIiIi0ioWG4iI\niIiIiIhIq1hsICIiIiIiIiKtYrGBiIiIiIiIiLSKxQYiIiIiIiIi0ioWG4iIiIiIiIhIq1hsICIi\nIiIiIiKtYrGBiIiIiIiIiLSKxQYiIiIiIiIi0ioWG4iIiIiIiIhIqwwKOwAiIiIiXUpLS4Ofnx8e\nPXqE1NRUjBgxAk5OToUdFhER0QeFxQYiIiIqVnbu3Alzc3MEBgbixYsX6NmzJ4sNREREWsZiAxER\nERUrXbp0QefOnTX/19fXL8RoiIiIPkwsNhAREVGxUqpUKQBAYmIiRo8ejbFjx+a7vIWFCQwMWJAg\non0IPYQAACAASURBVOLHe9fkwg4hmzWfLynsEIoVK6vSb/V6FhuIiIio2Hny5AlGjhwJd3d3uLi4\n5LvsixfJOoqKiIjyExWVUNghFCsFae/8ChIsNhAREVGx8vz5cwwZMgRTp05Fq1atCjscIiKiD1Kh\nfPXl5cuX4eHhURibJiIiomJuxYoViI+Px7Jly+Dh4QEPDw+kpKQUdlhEREQfFJ1f2fDjjz9i586d\nKFmypK43TURERITJkydj8uT36z5kIiKiD43Or2z46KOPEBwcrOvNEhEREREREZGO6PzKhs6dO+Ph\nw4cFWpZPf6ai6G2f2prJfeIGraxHm4zqFXYE2Wmrralg2N66w7YmIiKiou69fkAkn/5MRRGfkqs7\nbGvdYnvrzts+/ZmIiIiosBXKAyKJiIiIiIiI6MPFYgMRERERERERaVWhFBuqVKmCzZs3F8amiYiI\niIiIiOgd45UNRERERERERKRVLDYQERERERERkVax2EBEREREREREWsViAxERERERERFpFYsNRERE\nRERERKRVLDYQERERERERkVax2EBEREREREREWsViAxERERERERFpFYsNRERERERERKRVLDYQERER\nERERkVax2EBEREREREREWsViAxERERERERFpFYsNRERERERERKRVLDYQERERERERkVax2EBERERE\nREREWsViAxERERERERFpFYsNRERERERERKRVLDYQERERERERkVax2EBEREREREREWsViAxERERER\nERFpFYsNRERERERERKRVLDYQERERERERkVax2EBEREREREREWsViAxERERERERFpFYsNRERERERE\nRKRVLDYQERERERERkVax2EBEREREREREWmWgy42p1Wr4+/vj+vXrMDIywsyZM1GtWjVdhkBERETF\nHPMRIiKid0+nVzYcPHgQqamp+PXXXzF+/HjMnTtXl5snIiIiYj5CRESkAzotNly4cAHt2rUDADRu\n3BhXr17V5eaJiIiImI8QERHpgEJERFcbmzRpEjp16gR7e3sAgIODAw4ePAgDA53ezUFERETFGPMR\nIiKid0+nVzaYmpoiKSlJ83+1Ws0TOxEREekU8xEiIqJ3T6fFhqZNm+L48eMAgEuXLqFOnTq63DwR\nERER8xEiIiId0OltFJlPf75x4wZEBLNnz4a1tbWuNk9ERETEfISIiEgHdFpsICIiIiIiIqIPn05v\noyAiIiIiIiKiDx+LDURERERERESkVUW22LBy5Up4enpiyJAh+OKLLzTfkX39+nWcO3funWzT19dX\n80Cp/AQHB6Nz587w8PCAu7s7hgwZgr/++gsAsG3bNhw6dOidxFdUnDlzBq1atYKHhwcGDhyI/v37\nY8+ePQgPD8fSpUsLO7wCO3PmDD799FM8efJE87v58+dj27Zteb5m5cqVCAsLg1KpxJYtWwBkjJdN\nmzZpJaaVK1eibdu2UCqVWlmfLhS346Uo9tG79j7P55mGDx+O4cOHv5NYqGh7n8dvcZtfXxfzEeYj\nWRW346Uo9tG79j7P55mKWj5SJL/n6datWzh8+DA2bdoEhUKB8PBw+Pj4YOfOndi/fz8sLS3RvHnz\nQo3R09MTAwYMAADcvn0bI0eOxI4dO+Dm5laocb0vbG1tsWjRIgBAUlISPDw8MGvWLHh5eRVyZK/H\n0NAQ3377LVavXg2FQvHK5YcNGwYAePjwIbZs2YI+ffpoNZ7ff/8dzs7O2L17d5Eaa8XpeCmqffSu\nFIX5/MmTJ0hOTkZaWhoiIiJQtWrVQo2H3h9FYfwWp/n1TTAfYT6SVXE6XopqH70rRWE+L4r5SJEs\nNpQtWxaPHz/G1q1bYWdnh3r16mHr1q149uwZfvvtNxgaGqJBgwbw8/ND9erVYWRkBH9/f3h7eyMx\nMREqlQpjxoxBq1at4OLighYtWuD69etQKBRYtmwZTE1NMX36dFy9ehWWlpZ49OgRli9frtn++PHj\n4eLiAgcHB9y+fRvfffcdVq5cmWe81tbWaNCgAS5cuIALFy7A0tISnTt3xtixYyEiSEtLw/Tp02Fj\nY4MFCxbg6tWrSEpKgrW1NebMmYOYmBhMmDABqampqFGjBkJDQ3HgwAF0795ds3/Tp0/HpEmT8OLF\nCwDA5MmTYWNjg71792LNmjXQ09NDs2bNMGHChHfeP6+rVKlS6NevHwICAlCxYkUsWrQIvr6+ePDg\nAZRKJb744gs4Oztj37592LBhg+Z1S5YsgYWFRa59tXTpUsTGxiI2NhY//PADVq1ahXPnzkFE4Onp\nia5du+L69euYOXMmAMDc3ByzZ89G6dKlXyt2W1tbqNVqbNiwAQMHDsz2t3Xr1mHXrl1QKBRwdnbG\noEGD4OvrC2dnZ+zfvx+3bt3SfHJy6NAh7Nu3D7GxsRgzZgzat2+fa98FBwfj4sWLSE5OxqxZs7I9\nPf3MmTP46KOP0L9/f3h7e2tOHGFhYZg+fTpKlSqFcuXKwdjYGF5eXhgxYgTMzc1hZ2cHOzu7XNti\nwYIFOdotq06dOqFp06a4e/cuypUrh+DgYKjVavj5+SEiIgIqlQqff/45nJ2d4eHhAQsLC8THx+On\nn36Cvr5+rm36IR8vufXRu+yfoqAozOdbt26Fk5MTSpQogY0bN8LHxwcAsGXLFmzYsAFlypSBoaEh\nnJ2dAQAhISFQq9UYPXo0YmNjc4yphISEXMcfFT1FYfxm9SHPr9rAfIT5SFYf8vHCfCSnojCfF8l8\nRIqoq1eviq+vr9jb20vnzp1l3759IiISFBQkGzduFBERR0dHuXbtmoiIzJ07V9asWSMiIk+fPhVH\nR0dRqVTi6OgoFy5cEBGRcePGya5du+TAgQMyZswYERGJjo6WZs2aSUREhPj4+MixY8fk9OnTMnr0\naM16//jjj2yxZY0h06xZs2TXrl2avx05ckS+/vprefnypVy5ckXOnz8vCQkJsnLlShERUalU0qVL\nF3n69KnMmjVL1q9fLyIif/75pzg6OubYv3nz5smGDRtEROTu3bvSv39/efHihXTt2lWSk5NFRGTC\nhAny559/aqX930ZoaKiMHTs22+8OHDggnTt3lrFjx0pCQoI4ODhIdHS0REdHy86dO0VEZPny5Zp9\nmTJliuzYsSPfvlq9erWIiBw9elSzvZSUFHF1dZW4uDjp06eP3Lx5U0RENm/eLAsXLnyj/YiJiREn\nJye5e/euBAYGSkhIiNy8eVP69+8v6enpolKpxMPDQ27fvq0ZQxEREdKnTx8RyRgvfn5+mnV++eWX\nefZdUFCQzJgxI9d4xo8fL0eOHBERkf79+8ulS5dEROSzzz6TGzduiIjIwoULxcfHRyIiIqRly5ai\nVCpFRHJti7zaLau6devK48ePRUSkX79+cvHiRVm3bp3MmjVLREQSEhKkY8eOEh0dLQMHDpT9+/fn\niLs4HS+59dG77J+i4n2ez1UqlXTq1ElevHghCQkJYmdnJy9fvpTo6Gjp1KmTJCcnS3p6uri7u0tI\nSIiEhITI8OHDRUTyHFO5jT8qut7n8Vuc5tc3wXyE+UhWxel4YT6Su/d5Pi+q+UiRvLLh/v37MDU1\nxZw5cwAAV65cwbBhw9CyZcscy9aoUQNAxqVQLi4uAIAKFSrA1NQUMTExAID69esDACpVqgSlUolH\njx6hcePGADKqXDVr1sy2zpYtW2LWrFmIjo7GyZMnMW7cuFfG/PjxY3Tq1Al37twBANjZ2eHevXv4\n+uuvYWBggBEjRsDY2BgxMTEYN24cTExMNJfJ3L59Gz179gQAfPrpp7nu340bNxAaGoq9e/cCAOLj\n4/HgwQPExMRoLpVLSkpCRETEK2MtDI8fP4arqytu3rwJU1NTTJkyBVOmTEFiYiJcXV0BAOXKlYOP\njw9KlSqFO3fuoHHjxpp/gZx9lbVtrl27Bg8PDwBAeno6Hj9+jNu3b2P69OkAgLS0NM3yr8vCwgJ+\nfn7w9fVF06ZNNdt8/PgxPD09AQBxcXF48OBBnuto0KABAMDS0hIpKSn59l1uccbFxeH48eOIiYnB\nunXrkJiYiPXr16NRo0aIjIxE7dq1AQDNmjXDnj17AABVqlSBkZERAOTaFnm1m5mZWbZ9r1SpEoD/\nHT+3b99G69atAQCmpqawtrbON/bcfIjHS1599C77pyh43+fzEydOICkpCePHjwcAqNVq/P7776hd\nuzasra1RsmRJAECTJk1yxJnXmMpt/FHR9L6P39x8iPOrNjEfYT6S1Yd4vDAfyd37Pp8X1XykSBYb\nrl+/jk2bNmHFihUwNjZGjRo1ULp0aejr60OhUECtVmuW1dPLeAamtbU1zp8/j/r16+PZs2eIj4+H\nubk5AOS4t6127drYsWMHgIwD8t69e9n+rlAo4OLiglmzZqFNmzYwNDTMN94bN27g1q1baNy4MU6f\nPg0g4/Kl8uXL4+eff8bFixexcOFCeHp64smTJ1i8eDFiYmJw4MABiAjq1KmDixcvol69erh06VK2\ndWfuX82aNeHq6goXFxdER0djy5YtqFKlCipVqoSff/4ZhoaG2LZtG+rVq/earf3uJSYmYsuWLfj3\nv/8NAIiMjMS1a9fw/fffQ6lUwt7eHp06dUJQUBCOHj0KAPj8888hIvn2VWa/1qxZEy1btsSMGTOg\nVquxbNkyVKlSBTVq1MB3332HypUr48KFC4iKinrjfWjfvj0OHDiA3377Dd7e3qhZsyZq1aqFVatW\nQaFQYM2aNahTpw727dsHIKPfso7Tf47BvPru4MGDmj7PaufOnejVq5fmcqqXL1/CyckJMTExqFix\nIm7duoVatWrh8uXLmtdkXU9ubWFoaJhru2WV232hmcdax44dkZiYiBs3bmheV5D7SD/U4yWvPipR\nosQ765+i4H2fz7du3YqZM2fCwcEBAHDhwgXMnDkTP/30E+7cuYOUlBQYGRkhLCxMkzhkxpnXmLp7\n926O8UdF0/s+fv/pQ51ftYX5CPORrD7U44X5SO7e9/m8qOYjRbLY0KlTJ9y+fRt9+vSBiYkJRAQT\nJ05E6dKl8fHHH2PevHnZ7h0DgK+++gp+fn74448/kJKSgoCAABgY5L77Dg4OOH78OPr37w9LS0uU\nKFEiR4e7ubnBwcFBM2j+ac2aNdizZw/09PRgYGCAoKCgbNurW7cuvvnmG6xduxZ6enoYOXIkbGxs\nsGzZMvTt2xdGRkaoWrUqIiMjMXToUEycOBF79+5F+fLlc417+PDhmDRpEjZv3ozExER4eXmhbNmy\n8PT0hIeHB1QqFf71r3+9N/dQhYaGwsPDA3p6elCpVBg1ahTKlCmDM2fOwMrKClFRUfjss89gYmKC\nIUOGwNTUFE2bNkXPnj1hYmICMzMzREZGws3N7ZV91b59e5w9exbu7u5ITk5Ghw4dYGpqCn9/f/j4\n+EClUgEAZs2a9Vb7NGnSJISGhgLI6N9WrVphwIABSE1NRcOGDVGhQgXNsuXKlUNaWhoCAwNRokSJ\nHOt63b7bsmUL5s2bp/l/yZIl0alTJ2zevBnTpk2Dn58fTExMYGhomC2OTLm1RfXq1XNtt1fp27cv\npkyZggEDBkCpVMLLywvlypXL9zXF4XjJq48sLS112j/vm/d5Po+Ojsbly5c1D48DMj7tUSqVuHfv\nHoYOHQp3d3eYm5tDqVTCwMAA6enpmmXzGlO5jT8qmt7n8ZupOMyvb4P5CPORrIrD8cJ8JHfv83xe\npPMRrd+Y8QG4deuW7Nq1S0REYmJipHXr1pp7lTI9ffpUBg0apJN4jh49KpcvXxYRkZMnT4qHh4dO\ntlsUFKSvirv169dLdHS0iGTcgxccHFzIEb1bRe14KW79o2vvaj5PS0uTZcuWaf7v7u4uZ8+effuA\nibJgPlJ0MB95teJ2vitqx0tx6x9dK675SJG8suFdq1SpEubPn4+1a9dCpVJhwoQJmnuVAOCPP/7A\n0qVL37ryXFBVqlSBn58f9PX1oVarMWnSJJ1styh4VV9RxqcWQ4YMgYmJCUqXLo25c+cWdkjvVFE7\nXopb/+jau5rPDQwM8PLlS/Ts2ROGhoZo2LBhjntyid4W85Gig/nIqxW3811RO16KW//oWnHNRxQi\nIoUdBBERERERERF9OHI+2YWIiIiIiIiI6C2w2EBEREREREREWsViAxERERERERFpVbEtNkyfPh09\nevSAs7MzPv74Y/To0QM9evRASEgIgoODERwcXNghvpWwsDAEBga+1mu+/fZbODk5YdeuXZrfPXv2\nDEOHDs33dYmJiXBzc0P37t1x9+7dN4r3dXl4eGh+7tGjh062qWuvOw6DgoJw/vz5HL/fvHmzpk99\nfX2xbds2rcX4Jl5nv96HeD8kNjY2uf7ew8MDZ86cKfB6goKC4ODggNWrV2srNAD/i68gYyTreM86\nxguqffv2ePjwYbbfRUREwM/PDwBw5cqV9/5hXkQfog8lP8s8fz18+BDt27cv8OveJH/ThsJu29xy\nUCB7vpfXOUyXcjt35OV9iFcX3uV+LlmyBIcOHXpn66d3r9h+G8W0adMAAA8fPsSgQYOyfZ9pUTmR\n5efWrVuIjo5+rdf89ttvCAsLy/Zk1AoVKuDHH3/M93Xh4eEwMjLS6ZvCs2fPan7O67vFi5tz586h\nZcuWOX7/3//+Fy1atCiEiOhDtWPHDqxevRo1atQotBiyjndtjfHHjx8jIiICAPDJJ5/gk08+eet1\nEtHr+dDzs1d5k/ztQ5BbDgpkz/eo+BkzZkxhh0BvqdgWG14lLCwM/fv3x7Nnz+Dm5oZRo0ZBpVJh\n3rx5OHv2LFQqFdzc3ODp6Zntdenp6fD398fNmzfx/Plz2NjYYOHChXj+/DlGjhyJmjVr4tatW6hf\nvz6aNGmC3377DXFxcfj+++9hbW2NS5cuYdasWVAqlbCwsEBAQACqVasGDw8PeHl5oWXLlpoT8OHD\nh+Hr6wtTU1Ncu3YNz549w8iRI9GxY0cEBQUhOTkZy5cvx4gRIzTxqdVqzJ49G6dPn4ZCoYCrqyuG\nDRuG4cOHQ0TQp08f/PzzzyhXrhwAvHJbDg4O8PPzw/PnzzF8+HAsW7Ys1/WfOXMGgYGBUKvVqF27\nNqpUqYLHjx/j3r17iImJwYgRI3D69GlcvnwZdevWxaJFi6BSqXJty/nz5wMA+vTpgy1btsDGxgbX\nr1/Hy5cvMXnyZFy/fh0KhQJffPEFPvvsM2zbtg0nTpxAXFwcIiIi0KZNG/j7+2frNxHB/PnzcfDg\nQejr66Nfv34YPHgw7t69i6lTpyI2NhYmJiaYNGkSqlatiu7du+Po0aMwNDTEjRs3MGHCBOzcuRPb\nt2/H2rVroVar0aBBA0ybNg3GxsawtbXFxx9/jKioKGzduhWrV6/G3r17oVKp0LZtW3h7e0OhUGDV\nqlXYvHkzLCwsYGZmhoYNG+YYm+vXr8eOHTvw8uVLGBoaYsGCBQgLC8PVq1cxefJkLF26VFNlPnXq\nFA4fPozQ0FBYWVkBAI4ePYqNGzciOjoaw4cPR79+/ZCUlISAgADcvHkTKpUKQ4cORffu3bON63bt\n2uHAgQMwNTVF//790b59ewwbNgy7du3ChQsXMGXKlAL3PQCoVCp88803qFKlCr755hv4+fnh5s2b\nAAB3d3f07ds3z3jz6+ujR48iOjoaUVFRcHR0hK+vLxQKhWZfXFxcsHjxYlhbW2P8+PEwNTXF9OnT\ncfHiRSxfvhzLli3Lddylp6dj3LhxeP78OQBg5MiRcHJyytY3Hh4eKFOmDG7evInFixcjKioKQUFB\nSE9PR5UqVTBjxgxYWFhg7969WL16NVJSUpCamorZs2ejadOmWL16NX777Tfo6emhYcOGCAgIyPOY\nPXPmDH744QeUKFECt2/fho2NDebPnw8jIyMsWrQIp0+fRlxcHMqXL49FixbB0tISADBlyhSEhYXB\nwsICs2fPRuXKlbPtw8qVK3Mdm5mmTp2qOf4XLFiAp0+fYvHixVCr1ahatSoCAgJgaWmJ9u3bo2HD\nhggPD8fGjRs1cwqAfOP7pyNHjuRY/59//qkZ75nzU+YYr1evHqZOnYqnT59CoVBg/PjxaN26NWJj\nY+Ht7Y2nT5/C2toaSqUyx7ZmzpyJhw8fYvr06ejSpQuWLl2KdevWwcPDA/Xr18eFCxegVCoxYcIE\n/Oc//8Ht27fh6ekJT0/PVx5DRKQdb5qfAcCKFSuwc+dO6Ovro02bNvD29sa8efNQoUIFDBkyBAAw\natQouLq6okmTJrnOJcHBwbh06RKePHmCgQMHolatWli0aBFSUlIQHx+Pb7/9Fh06dHjlfty4cQMz\nZsxAcnIyYmJiMGzYMHTr1i1b/lahQgUcPXoUsbGxiIyMRP/+/fHo0SOEhobC3Nwcq1atgrGxMUJC\nQrB69WooFAo0aNAAU6ZMgZGRUa7nVV9fXxgbG+PKlStISkrCiBEj8Nlnn7122/7z3D516tRXzoGv\nm4POnDkTwP/yPSDjHHTp0iUAGcWnatWqISwsDHPmzEFKSgosLCwwffp0VK1aVbPdP/74A3v37sXi\nxYtx9+5ddOnSBSdPnoSlpSW++OILjBkzBqVLl86R7zVs2BC+vr6IjY3F/fv34e3trVnn3bt38dVX\nX2HevHkoUaIEpk6divT0dBgbG2POnDmoXr16nvHml+vXrVsX58+fh1KphJ+fH9q2bavZ5tWrVzF9\n+nRs2bIFycnJaNGiBTZs2IBGjRph6tSpaNWqFaytrXOMqwEDBuD06dOaK2bKlCmDBQsWoGzZspp1\nDx8+HAMGDIC9vT0WLlyIv/76C6tWrUJkZCSGDBmCXbt25ZnjZoqNjcWkSZNw584dGBkZwdfXF61a\ntcr1PJ6ZJ3Tr1g0nT56EgYEBvv76a/z888+4f/8+fHx84OzsDF9fX7Ro0QItWrSAl5cXateujfDw\ncJQrVw5LliyBubk59uzZg6CgIJiYmKBevXpQqVT82s73iRRzERER4ujomO13QUFB0rNnT1EqlRId\nHS2NGjWShIQE2bhxo8yePVtERJRKpQwcOFDOnTuX7bVnz54Vf39/ERFRqVQycOBA2bdvn0RERIiN\njY1cu3ZNVCqVdOjQQebPny8iIsHBwTJr1ixRKpXi6Ogoly9fFhGRPXv2iJubm4iIDBw4UEJDQ3PE\n7OPjIyNHjhS1Wi1///23tGjRQkREQkJCxMfHJ8f+rl+/Xr7++mtJT0+X5ORk6dWrlxw5ckREROrU\nqZNv++S1rdDQUBk4cGC+6w8NDZVmzZpJfHy8po3d3NwkLS1Nzpw5I3Xr1pWbN29KWlqadOzYUcLD\nw/Nsy3/Gmvnzd999JzNmzBARkejoaGnfvr2Eh4dLSEiI2NvbS0JCgiQnJ4udnZ38/fff2fZzz549\n0r9/f1EqlZKYmCiurq4SGRkpvXr1kj/++ENERC5evCgODg6iVCpl+PDhcvjwYRERWbhwofz4449y\n48YNGTBggKSkpIiIyPz58+X777/XxJjZf8eOHZNRo0ZJenq6qFQqGTdunGzfvl3CwsKkS5cukpiY\nKElJSdK9e3cJCgrKFmdCQoIMHjxYXr58KSIiixcvloCAgBxjJCsfHx8JCQnR/PzVV1+JWq2W69ev\nS8uWLUVEJDAwUNauXavZRrdu3eTBgwfZ1jN27Fg5fPiwJCYmSrt27eTLL78UEZGJEyfK0aNHX6vv\nlyxZIr6+vjJr1iwRETlz5owMHTpURESePn0q3t7e+cabX1+3bt1aoqKiRKlUSr9+/TT9lykwMFDW\nrVsnIiLdu3eX7t27i4jIkiVLZMOGDXmOu23btml+/9dff8ncuXNztPXAgQM1fRYdHS2urq4SGxsr\nIiKbNm0SPz8/UalUMmjQIImOjhYRkS1btshXX30l6enp0rJlS0lNTRWVSiW+vr7y9OnTfNu1cePG\n8uTJE1GpVNKrVy85dOiQ3Lt3T7y8vESlUomIiLe3t/z0008ikjEOd+zYISL/O1azjp28xuY/OTo6\nSkREhDx//lzatm0rERERIiLy448/yqhRozTLZI67rF4VX+YYCQoKynf9Wcd71jE+duxYOXjwoIiI\nPHv2TJycnCQhIUGmT58uCxcuFJGMebpOnTqa9WbKOpdl/XngwIGasRocHCwdOnSQ5ORkefjwoXz6\n6aciUrBjiIgKTtv52dGjR6VPnz6SnJwsaWlpMnz4cFm/fr1cu3ZNevbsKSIZx26bNm1EqVTmOZcE\nBQVp5gYRkVGjRsmtW7dEROTUqVOac0rmvJTbfoiIzJw5U06dOiUiIg8ePJDGjRuLSPb8LSQkRBwc\nHCQhIUEePnwoderUkePHj4tIxrx04MAB+fvvv6VDhw4SExMjIiL+/v4yd+7cfM+rn3/+uaSmpsqT\nJ0+kVatWEhkZ+dpt+89ze0HmwNfNQf/5+zp16sjevXtFRGTu3Lkyd+5cUSqV4uLiIo8ePRIRkePH\nj8vgwYOzrSMhIUHatm0rarVaNm7cKK1atZLdu3fLy5cvxdHRUdRqdZ75no+PT7Z82tHRUc6dOyfO\nzs6aMebr6yt79uwREZFt27bJb7/9lm+8+eX6vr6+IpKRZ2SOxUxqtVrs7OwkPj5ejh07Jq1atZKV\nK1eKiEjHjh0lISEhz3E1cOBAzTZXrlwpJ06cyNZGGzdu1OQ1AwYMEEdHR0lPT5etW7fKvHnzXpnj\nivxv7ImI/P3339K3b99X5glr1qzRtOGAAQM07wt69OghItmPo8z3USIiXl5e8p///Eeio6OlTZs2\n8vTpU1GpVDJy5Mhc3/9Q4eGVDXlo164djIyMULZsWVhYWCAuLg6nT59GeHg4QkNDAQDJycm4fv06\nPv30U83rmjdvDnNzc2zYsAF37tzBvXv3kJycDACwtLRE/fr1AQAVK1ZEq1atAACVK1fGw4cPce/e\nvWyfZnft2hVTp05FQkJCvrG2adMGCoUCderUQWxsbL7LnjlzBj179oS+vj5KliwJFxcXnD59zKvR\nfAAAIABJREFUGg4ODgVql1dtK6/1t2/fHjVq1EDp0qWzrcvAwACVK1eGlZUVatWqBSDj1o24uDi0\nbNkyz7bMTWhoKGbPng0AKFu2LJycnHD27FmYmpqiSZMmMDU1BQBUrVoVcXFx2V577tw5dO3aFUZG\nRjAyMsKOHTuQlJSEBw8eoFOnTgCAxo0bo0yZMrhz5w5cXV2xe/duODo6Yu/evVi3bh0OHDiA+/fv\naz6RT0tL0/Q3ADRq1AgAcPr0aYSFhcHNzQ0AkJKSgsqVK+P58+ewt7dHqVKlAABdunSBWq3OFqep\nqSkWLFiA3bt34969ezhx4gTq1av3qm7LxsnJCQqFArVr18aLFy8AZFwBkZKSgpCQEAAZY/vmzZvZ\nPhmwt7fH6dOnoaenBxcXF+zZswdpaWk4f/48AgIC4O3tXeC+/+WXX5CQkKC5D6927dq4e/cuvvji\nC9jZ2WHixIn5xptfXzs5OWk+JXd2dkZoaKimDzP3Y82aNbC1tUWtWrVw584dREdH4/jx4wgKCkLl\nypVzHXdNmjTBwoUL8ezZMzg4OGDkyJG5tm/m8Xv58mU8efIEgwYNApDxiU6ZMmWgp6eH77//HocP\nH8bdu3dx9uxZ6OnpQV9fH02aNEHv3r3h5OSEzz//HBUqVMj3mKpduzYqVqwIALC2tkZcXByqVasG\nHx8fbNmyBXfv3sWlS5fw0UcfAQBKlCgBV1dXABnPOlm8eHG22PMam3kJCwtDw4YNNVer9OvXDytX\nrtT8PXPMZ5VffK+7/tycOnUKd+7cQVBQEICMq3IiIiJw9uxZLFiwAEDGPJ11bBeEnZ0dgIz5ulGj\nRihZsiT+9a9/IT4+XrPdVx1DRPT23jQ/Cw0NRbdu3VCyZEkAQK9evbB9+3b8+9//RmpqKu7fv4+L\nFy+iffv2MDIyynMuAZDtqsPAwEAcOXIE+/btw+XLl5GUlFSg/fD19cWJEyfwww8/4MaNG3nmN02b\nNoWpqakmh8nMHTPnn3PnzsHR0REWFhYAMubJb7/9FsOGDcvzvOrm5gZDQ0NUrFgRTZs2xYULF167\nbWvVqpXt3F6QOfBtc1AAmqtGatWqhfPnz+PevXuIiIjIdhVvYmJitteYmpqiRo0auH79OkJDQzF4\n8GCcO3cOpUqVgq2tLZKTk/PM9wDkuMp0zJgx+OSTTzTjy97eHgEBAThx4gTat28PR0fHfOPNL9fP\nzCHr1asHKysrXL9+XXNLn0KhQOvWrXHmzBn897//1eyHo6MjKlWqBFNT0zzHlZOTE7y8vNChQwc4\nOTmhTZs22fbJwcEBI0aM0LSdjY0Nrl27huPHj2ue65Rfjgtk5NKZVx/b2Njg119/xZEjR/I9j2c9\nt5YvX17zviDz3JpVuXLlNNusXbs24uLicP78eTRp0gQVKlQAAHz22Wc4ePBgjtdS4WGxIQ8GBv9r\nGoVCARGBSqWCt7e3ZjKKiYnRvDHMdOjQIQQFBWHQoEFwc3PDixcvICIAkOM+NH19/Wz//+cbSwCa\n7Wb+DGSc8LLKvIQp66XOefnnNrKuvyBeta381l+iRIlsfzM0NNT8nLW9M+XXlrn559+ybjvrZV6Z\n/ZmVgYFBtn16+PAhypQpk+s2VCoVnJycMHfuXJw7dw6VKlVChQoVoFKp0LVrV0yePBkAkJSUlK1t\nM/dfpVJh8ODB+PzzzwEA8fHx0NfXx6+//potLgMDA6Smpmbb/pMnT+Dh4YGBAwfCzs4OlpaWCA8P\nz7NNcpM57rLur1qtRmBgIBo0aAAAeP78eY79t7Ozw+rVq6Gvr49WrVrhzp072Lp1K+rUqQNjY+PX\n6vsmTZqgfv36mDlzJoKCgmBhYYHdu3fj5MmTOHbsGHr27Indu3fnGW9+fZ31uFKr1TmOsyZNmsDX\n1xenTp1CixYtUK5cOezbtw/p6emoXLlynuOuevXq2Lt3L06cOIEjR47g559/xp49e6Cnl/05u1n7\nuWnTplixYgUAQKlUIikpCUlJSejduzdcXV3RvHlz2NjYYMOGDQCAZcuW4dKlSzh+/Di+/PJLzJ8/\nP992zW1cX716FePHj4enpyc6d+4MPT09TXtljVVEchx3eY3NvOQWW9b5KWt8mfKL73XXn9dr1q5d\nC3NzcwBAZGQkypUrl+O4z2+/cvOq+aogxxARvb03zc9yy7Ey5xNXV1fs2bMHFy9exLBhwzTL5zaX\nHDx4MNs5zd3dHS1btkTLli3RqlUrTJgwoUD7MXbsWJiZmcHR0RHOzs55PuQ269zzz/3Pbb8y58mC\nnFczX5+5ztdp20uXLmVrh4LMgW+bg2aNMTM+tVqNKlWqaJ7toVKpNLc7ZuXg4ICTJ0/izp078Pf3\nx6BBg6CnpwdHR8dcz0H55TCTJk3C999/j6NHj8LBwQFdunRBkyZNcOTIEaxZswZHjx7V3AKSW7z5\nbSuvvsm6H6dPn8bVq1exatUqzRv6zAJHXuPK09MTjo6OOHLkCAIDAxEWFpatQFOpUiWo1Wrs378f\nTZs2haWlJUJDQ3Ht2jU0adIE165dyzfHzdzXrLna7du3X3kef9W5Navcch49Pb1c25TeH8X22yje\nhK2tLTZv3oy0tDQkJSXB3d1dcx9WptOnT6Nr167o1asXzMzMcObMmQJPpDVr1kRsbCzCwsIAAHv2\n7NF8ymphYYFbt24BQIEqdvr6+rkm5ba2tti+fTtUKhVevnyJ33//PdeHCr4pba4/v7bMbf9sbW2x\ndetWABknw0OHDhX4oXHNmzfH/v37kZaWhpcvX+LLL7/E8+fPUaVKFezfvx8AcOnSJTx//hy1a9eG\nkZER2rVrh9mzZ2s+KW7ZsiUOHDiA6OhoiAj8/f2xdu3aXNso88qJ9PR0jBw5En/88YfmvraEhAQo\nlUocOHAgx2uvXLmCatWqwdPTE5988gkOHjyYrU1yG2t5/f6fMW3atAlARkLl6uqKJ0+eZFumbNmy\nKFGiBI4cOYJmzZrB1tYWy5Yt05zgXqfv69ati6FDh+LmzZs4fPgwDh06BG9vbzg4OGDy5MkwMTHJ\nsf1/xptXX584cULThrt379ZUzTMZGBigYcOGWLduHVq0aAFbW1usWLEC9vb2APIed+vXr0dwcDC6\ndu2KadOmISYmJsenJ1k1atQIly5d0nxDy7JlyzBv3jzcu3cPCoUCw4cP14wZlUqFmJgYODs7o06d\nOhgzZgzatGmD69evv/Yxde7cObRo0QIDBgxA9erVcfToUU3/Jycna64mCQkJQevWrXO0a25jM799\nvHz5subJ3L/++usrj/f84nud9Wcd11l/trW1xcaNGwFkPGjNxcUFL1++RKtWrTTJaFhYGB48eJBj\ne3nNmwVRkGOIiN6NguRntra22L17N1JSUpCeno6QkBDY2toCgOZqvfv376NZs2aa5XObS7KKjY3F\nvXv3MGbMGNjZ2eHQoUMFzvlOnjyJ0aNHo0OHDjh+/DiAjDfKrzsPtWjRAocPH9Zcbbp582a0bNky\n3/Pq3r17ISJ49OgRwsLCNPucm4K0beZyr5oD3yRHfFV71KxZU/MJN5Bxbsut4GNvb49ffvkFtWrV\ngoWFBQwNDXHkyBG0bt0apqameeZ7uWnYsCH8/f0REBCA5ORkjB07FleuXEH//v0xZswY/PXXX/nG\nm1eun/l/ICPfi4+PR506dbK9vk2bNvjzzz+hp6eH0qVLo169evjPf/6juTokr3HVp08fJCUlaZ4z\nlFuMdnZ2WL58uSY3WrduHRo1agR9ff0C5biffvqppqB1+/ZtDB069I3yhNfRtGlTXLlyBZGRkRAR\n7Nmzp0AfvpLu8MqG19C/f3/cv38fPXv2RHp6Otzc3HIcMH369MGECROwe/duGBoaomnTpgX+ipzM\nB7vNmDEDL1++RJkyZbBo0SIAwJdffglfX1+EhITkeChdbho2bIilS5di/vz52Sbdfv364d69e+jR\nowfS0tLg4uKCjh07vkYr5C+v9b/O1+plyq8tnZyc0KNHj2zfgDFy5Ej4+/vDxcUFKpUKw4cPR4MG\nDXD9+vVXbqtjx464evUq3NzcoFarMWjQINSoUQOBgYHw9/dHcHAwDA0NERwcrLlCpUePHti5cyc6\nd+4MIOMNtJeXFwYPHgy1Wo169eppPiHJqn379vj777/Rt29fqFQqtGvXDj179oRCocDgwYPRu3dv\nmJmZ5Xr5eps2bbBp0yY4OztDRNC8eXPNw5/atWuHadOm4bvvvkPTpk01r2ndujUWLlyY7TaGf/Ly\n8oK/vz+6d++u+RQjt0vb7ezscOzYMc2lh7Nnz9a8SX/dvjcyMoK/vz98fX3x+++/Y//+/ejWrRuM\njY3h6uqa71cp5dfXZcuWxdChQ/HixQu4urqiXbt2OV5vb2+Pc+fOwdraGlZWVoiOjtacqPMad0OH\nDsW4cePg4uICfX19eHt7w8zMLM8YraysMHv2bIwdOxZqtRoVKlRAYGAgzMzMUK9ePXTt2hUKhQJt\n27bFhQsXULZsWfTr1w+9e/dGyZIlUaNGDfTq1QuGhoav1a7Ozs7w8vKCi4sLAODjjz/WHDdmZmY4\nePAglixZggoVKmDOnDnZXpvX2MyLpaUlAgIC4OXlhbS0NFSuXBmzZs3Kc/lXxfc668863rOO8cmT\nJ2Pq1Kma9c+bNw+mpqYYPXo0fH190a1bN9SsWTPX2xusra2RkJAAb29v9O7dO9/9+KeCHkNEpH0F\nyc8cHR0RHh6OXr16IT09HW3btsXAgQMBZHyqa2FhgSZNmmjeqOQ1l2Rlbm6O3r17o1u3bjAwMICt\nrS1SUlLyveUz06hRo+Du7g5jY2PUrVsX//rXv/Dw4cNs+VvNmjVfuZ66deviq6++goeHB9LS0tCg\nQQNMnz4dxsbGeZ5XU1JS0KtXL6SmpiIgIEBzC8brtO0/z0EFmQPfJAfNLd/LysjICEuWLNE8cNHU\n1BTfffddjuWsra0hIpoPJlq0aIGbN29qroDJL9/LTfPmzdGyZUssXrwYw4cP11ztYGhomOMh5P+M\nN69cH8j4CubM8+6iRYtyXIVnamqKihUram6tsLW1xa1btzQPpMxrXI0bNw6+vr4wMDCAiYmJ5sqL\nrDK/0rpZs2YwMTFBWlqa5gOlguS4o0ePxuTJk+Hq6goDAwPMmzfvjfKE11G2bFlMnjwZQ4YMgZGR\nEapUqZJvbka6p5D8rksnIipCtm3bhrNnz/IpxERERLnIfLp/5rN56P2R9ZvnqGBevHiBdevWwcvL\nC3p6epg5c6bmmz3o/cArG4iIiIiIiKhIMTc3R3x8PLp37w59fX00aNBA8xBLej/wygYiIiIiIiIi\n0io+IJKIiIiIiIiItIrFBiIiIiIiIiLSKhYbiIiIiIiIiEirWGwgIiIiIiIiIq1isYGIiIiIiIiI\ntIrFBiIiIiIiIiLSKhYbiIiIiIiIiEirWGwgIiIiIiIiIq1isYGIiIiIiIiItIrFBiIiIiIiIiLS\nKhYbiIiIiIiIiEirWGwgIiIiIiIiIq1isYGIiIiIiIiItIrFBiIiIiIiIiLSKhYbiIiIiIiIiEir\nWGwgIiIiIiIiIq1isYGIiIiIiIiItIrFBiIiIiIiIiLSKhYbiIiIiIiIiEirWGwgIiIiIiIiIq0y\nKOwA8hMVlVDYIRAREb2XrKxKF3YIxQbzESIiotzll4/wygYiIiIiIiIi0ioWG4iIiIiIiIhIq1hs\nICIiIiIiIiKtYrGBiIiIiIiIiLSKxQYiIiIiIiIi0ioWG4iIiIiIiIhIq1hsICIiIiIiIiKtYrGB\niIiIiIiIiLTKQJcbS0tLg6+vLx49egQ9PT3MmDED1tbWb7XOMYE7tRRdhiXerq9c5tGjh1i+PAiR\nkZEoUaIEjI2NMWLEaNSs+Xb78jpcXTtj584/sv3ur7+u4scfl0NEICKwtW2DAQMG5vr60NBTePbs\nKXr0cHut7R47dgQNGnwMhUKB1atXYcIEXxw/fhQ//rgMrq5uuHjxAmbPDnzlepRKJX78cTn++usq\nFAoFSpYsCW9vP1SoUPG14nlTt2/fQkJCPBo3bopp077F5MkBMDQ01Mm2iYiocG3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Wcnv/zyi2rWrGnqPC9c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\nTIdiJxsYhmEYhmEYhmEYhulQ/wNraTebLI6CIAAAAABJRU5ErkJggg==\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x10e7cf978>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "fig = plt.figure(figsize=(18, 16))\n", | |
| "\n", | |
| "questions = [\n", | |
| " \"Please give us some feedback about the overall workshop [The amount of information covered at the workshop was reasonable for allotted time]\",\n", | |
| " \"Please give us some feedback about the overall workshop [The overall atmosphere of the workshop was welcoming]\",\n", | |
| " \"Please give us some feedback about the overall workshop [I learned skills that I will be able to use in my research/work]\",\n", | |
| " \"Please give us some feedback about the overall workshop [The material presented matched the workshop description]\",\n", | |
| " \"Please give us some feedback about the overall workshop [I would recommend this workshop to a friend/colleague]\",\n", | |
| " \"Please give us some feedback about the overall workshop [The workshop was worth my time]\"\n", | |
| "]\n", | |
| "\n", | |
| "for i, question in enumerate(questions):\n", | |
| " ax = fig.add_subplot(3, 2, i + 1)\n", | |
| "\n", | |
| " _ = sns.countplot(\n", | |
| " data=data,\n", | |
| " x=question, \n", | |
| " hue='I have attended the stream',\n", | |
| " order=[\"Strongly Disagree\", \"Disagree\", \"Neither Agree nor Disagree\", \"Agree\", \"Strongly Agree\"],\n", | |
| " ax=ax\n", | |
| " )\n", | |
| " _ = plt.xlabel(question.split(\"[\")[1][:-1])\n", | |
| " _ = plt.legend(loc=\"upper left\")\n", | |
| " \n", | |
| "_ = fig.suptitle(\"Please give us some feedback about the overall workshop\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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WrFmjiRMnasmSJXJ1dZWfn5+CgoLUqVMnVa5cOdu2cXFxmjt3rrp1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\n718QmnE1NI77rwAAAABJRU5ErkJggg==\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x111ca6748>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "_ = sns.countplot(\n", | |
| " data=data,\n", | |
| " x=\"How did you perceive the pace of the workshop? \", \n", | |
| " hue='I have attended the stream',\n", | |
| " order=[\"Slightly slow\", \"Just right\", \"Slightly fast\"]\n", | |
| ")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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ZMTExcnd31z333KPRo0crOzv7sjaXLl2q/fv3a/r06TLGKDAwUB06\ndNCBAwcUFRWluLg4tWjRQuXLl5eXl5eio6MVGRmppKQkSbrs/XmrO3/+vO6++25JOdto+vTpkqT0\n9HRNmjRJnp6eznlXrVqVa5R26tSp2rdvn9577z15enoqISFBzZs3V48ePXT48GENHz5cmZmZ8vHx\nUUxMjOx2u0aMGCG73e7cN8qUKaOYmBh9++23Kl26tHM7XuqLL77I81PFuLg4rVixQjabTc2bN1fn\nzp0v6/ftt9/WnDlzlJ6ergceeEDz589XVFSUwsLCtGjRIp0+fVqtW7dWjx49FBAQoPr166t+/foa\nO3asJCkgIEDjx4+Xn5+f1q1bpzZt2ujLL7/U2rVrtWrVKv3xxx/q06ePHnvsMS1YsEBff/21srKy\n5Ofnp9jYWK1YsULr169Xenq6jh49qldeeUXPPPOMtm7dqvHjx6to0aJyc3NT9erVZbfb1adPH6Wk\npCg9PV2DBg1S7dq1c61zQkKCIiMjlZWV5fx/UKlSJTVp0kQ1atTQoUOHVKJECcXGxsrd3V0tWrRQ\nbGzsLRF2OJbd2LHs7Nmzev3115WYmKiKFStq7Nix2rt3ryZOnCiHw6Hz589r+PDhqlGjRp77Q3p6\nugYOHKjz58+rXLlyznYXLlyozz77TG5ubqpRo4aGDBmSq9/vv/9eU6ZMkbe3t/P9sGvXrjzf93eK\n2+FczW63q3///jp+/LgCAwM1ZcoUnTp1StHR0crMzFRSUpJ69+6txx57TC1btlStWrW0Z88eubu7\na8aMGSpUqJAiIyN16NAh3XPPPcrKypIkrVy5UnPnzpWHh4eCg4M1ceJE2Ww2Z7+//fabxo0bJw8P\nD3l7e2vs2LHKyMjQkCFDVLJkScXHx6tGjRrO/f6WZnDDli5dat58803n49WrV5s33njDOBwOY7fb\nTevWrc2+fftMjx49zLp164wxxvz222+mbdu2udr5/vvvTYsWLUxmZqbZsmWLadSokcnIyDCHDh0y\nrVu3NsYYU69ePZOZmWmMMWbixInms88+M19++aUZMGCAMcaYEydOmClTpjjbstvtJjU11dSuXTtX\nX5mZmaZevXrGGGNat25tDh48aIwxZtGiRWbq1Kl5tnnpMosXLzbvvPOOMcaYlJQU8/jjjxtjjGnf\nvr1Zu3atMcaYf/3rX855zpw5Y/75z3/e6Ka+7W3fvt0MHTrUNGjQwDRt2tSsWrXKGGNMx44dzf79\n+820adPMRx99ZFavXm369OljjMnZdg8++KCJj483Q4YMMe+++64xJme/GzVqlDHGmEaNGpn09HSz\nadMm07dvX+NwOEzTpk3NH3/8Yfbt22dee+21y2p5+OGHjTHGxMXFmXHjxhljjElOTjZPPPGEOXPm\njGndurXZuXOnMSZnn+7du3eu5ZcuXWpeeuklY4wxK1asMG3atDEOh8P88MMPpkePHrn6MMaYvn37\nmk2bNpl58+Y53y979uwx8+bNM0uXLjVdunQxDofDxMfHm2bNmuXqKz4+3rRt29Y4HA7TpEkTc/r0\naWOMMTExMeaTTz7Js82LyxhjnNvVGGP2799vOnbs6NxuO3bsMMYYM3nyZLNw4UJjjDGHDh0y7du3\n/+sXtABt2rTJ1KlTx3Ts2NG0a9fOVKtWzWzatMkYY8yCBQvMiRMnjDHGzJw508yYMSPX9pg5c6ZJ\nS0szxhgzYsQI8/nnn5tNmzaZZs2amczMTJOammpq1KhhjDHmtddeM+vXrzfGGPOf//zHfPvtt6ZP\nnz7mv//9rzHGmI0bN5r+/fubPXv2mA4dOpjs7GyTnJxs6tata+Lj43PV3L9/f/Pdd98ZY/7/cXPf\nvn2mffv2Jisry2RnZ5tOnTqZAwcO5Nnvpcfai+8ZY4z56KOPzLRp00x8fLypXbu2sdvtxhhj2rZt\na/bt22eMyX3M6t69uzEmZ7+IiIhwbs+XX37ZZGdnm9jYWJOdnW2MMaZr165m69atZunSpaZr167G\nmJz9o2nTpsYYY5555hnn8XPkyJFm2rRpZu/evebZZ581ycnJ5vDhw85tdanevXub1atXG2OM2blz\np/MYX6lSJXP8+HFjjDHPPfec+eWXX4wxxmRlZZm6dev+1W7hMhzLrv9YVrt2bfPHH3+Y7Oxs89hj\nj5nTp0+b//znP2b37t3GGGOWL19uIiMjjTF57w9xcXHOfXnbtm2mUaNGxpicffHi/rJw4ULnuYIx\nxjgcDtOoUSPncWH+/Plm4sSJV3zfW9Hteq523333mWPHjhljcs6xtm/fbr799luzZcsWY4wxmzdv\nNt26dXP2++uvvxpjjOnTp49ZuXKlWb16tRk8eLAxxpijR4+a+++/3xhjzOuvv25WrlxpjDFm2bJl\n5vz587n6btWqlXOfXLVqlenbt685fPiwqVOnjklNTXWeE545c+a6Xg9XYmQnHxw4cEA1a9aUzWaT\nl5eXqlWrpgMHDjinS1LVqlXzvJ49PDxcHh4e8vf3V7ly5eTp6Sl/f39lZGRcNq/5v59IOnTokB54\n4AFJUqlSpdSnTx9t3LhR4eHh8vLykpeXl9zd3a9Y76FDhzRy5EhJUmZmpsLCwuTh4XFZmxc/DbhS\nHReFhIRIkvbu3att27bp559/drZ9/vx5+fv7X3njWdiRI0dUpEgRTZgwQVLOpybdu3e/7BNfSTp4\n8KCqV68uKedT1NDQUOdz9913nyQpMDBQ6enpefZls9n01FNPacWKFUpISFCbNm2uWNeBAwf08MMP\nS5KKFCmisLAwxcfH69SpU6pcubIkqVatWnr77bcvW/bi835+fgoLC5PNZlPRokVlt9svm/fifnLw\n4EHnNcjh4eEKDw/XsmXLVKVKFdlsNgUFBV1xvc6ePatTp06pb9++knJGLR555BGdPXv2sjbzen/l\n5dL9ddOmTVq5cqWknJGSW92ll7EdPHhQ7du314YNG1SqVCmNGzdOvr6+OnnypGrUqJFruRIlSmjI\nkCEqXLhwrn3t4vHHw8NDPj4+knIfX5o3by5JGj9+vGbPnq33339fxhh5enpq//79qlq1qtzc3FSk\nSBGFh4dfVm9SUpJKlCiRa9revXt1/PhxdenSRZJ07tw5HT16NM9+r3Tp26XHoLJly8rLy0tSzr4d\nHR0tKef4ExISovj4+Fyfhv/5/eTm5iZPT0/1799fvr6+OnHihPPYV6lSJUlSmTJlnMfkkydPOveh\nGjVq6OjRo6pQoYJeeOEF9e/fX1lZWerUqZO2bt2qqVOnSpK6deumAwcOqFatWpJy3kcnTpyQlDO6\nXqZMGWc/F99L7u7ucnd3l8PhkJtbwX7dlmPZjR3L7rnnHhUtWlRSznvxwoULKlmypGbMmCEfHx+l\npqaqSJEikvLeH/bt26d69epJkqpVqyYPj5xTuQkTJuiDDz7QW2+9perVq+d6XyQlJalIkSIqVaqU\nczu88847atiwYZ7v+zvB7XKuVqxYMedIZFBQkC5cuKCgoCDNnj1bixcvlsPhyHV+dnFfvri/HDp0\nSPfff7+knH3v4qhoRESE5syZowULFujee+9VkyZNcvV7+vRp54hWrVq1nFcLBAcHy9fXV1LOezev\ndb7VcIOCfBAWFqaffvpJkpSRkaFt27YpODg41/TffvtNpUuXvmzZS4cQ8+Lt7a3ExEQZY7Rr1y5n\nf7/99puknBOFl19++ZrauigkJERvvvmm4uLiNGDAADVo0CDPNt3c3ORwOCRJXl5eSkxMlCTt3Lkz\nV3sX/xGHhobqqaeeUlxcnObMmaMnn3xSfn5+11STFe3Zs0dRUVHOf54hISHy8/PL8+BWoUIF59D6\nuXPndPjwYedzeb2uNpvN+dpc9Oyzz2rVqlXasmWLGjRocNkyFw/AYWFh2rp1qyQpJSVFe/fuVdmy\nZVWyZEnt3r1bkrRlyxaVL18+z36vJisrS6mpqcrIyND+/fud/V3ct+Lj4zVgwIBrakvKOeiXLl1a\nM2bMUFxcnF577TXVrl07zzYv3V8vvm8kaceOHbnavHR/7dKli+Li4jRlyhS1bNnyL+u5lQQGBjr/\nHj58uMaPH6+JEyeqZMmSuU56kpOTNW3aNMXExGjs2LHy9vZ2Pp/Xa3Dptl2+fLni4uIUGhqqgQMH\nKi4uTtHR0WratKlCQkL0v//9Tw6HQ2lpac7X+1LFixdXcnJyrmmhoaG699579a9//UtxcXF65pln\nFB4enme/13IMujQIhISEaNKkSYqLi9OgQYPUoEEDffPNN2rYsKFznj+v8+7du7VmzRpNmTJFI0aM\nkMPhuOr2CQoKct4Y4mK9e/bsUWpqqubMmaOJEydqzJgxqlmzpuLi4hQXF6eGDRvmet/t2rXL+fpd\n6X1gjJGHh0eBBx2JY9mNHsvyen7cuHF64403NGnSJIWHh191nwsNDXVu0507dzpPdBcvXqzo6Ggt\nWLBAu3bt0i+//OJcplixYkpJSdGpU6ck5VzqenE7XOu5gtXcLudqeT0fExOjZ599VpMnT9ZDDz2U\n6xj/5/lDQ0Od+8KJEyecx83FixerT58+WrBggTIyMi67oUhgYKD27dsn6fbfXxjZyQeNGzfW5s2b\n1b59e2VkZKhFixaqVKmShg0bphEjRmjOnDnKzs52Xkv+d7zyyivq2rWrypYtq4CAAElSkyZN9MMP\nP6hDhw5yOBzq1avX32ozKipKAwcOVHZ2ttzc3DR+/HiVK1fusjbd3NwUHBysoUOHaujQoVq8eLGe\nf/55Va1aVYUKFbqs3Q4dOmj48OHq2LGjUlJS1LFjx9vyTXKzNGnSRAcOHFDbtm3l6+srY4wGDx6c\nZwBs2LChNmzYoPbt2yswMFA+Pj65vnPxZzVr1lT37t1zXetbqlQpFS5cWNWrV3d+8nepsLAwDRw4\nUOPHj9eIESPUoUMH2e129erVSyVKlNDYsWM1ZswYGWPk7u6u8ePH/+117ty5s5577jmVLVvW+clU\n+/btFRERoY4dOyo7O1sRERHOA+pfcXNzU2RkpLp37y5jjAoXLqzJkyerRo0al7VZokQJZWZm6s03\n/197dx8UVfX/AfyNPIRPIIKiDiaRD5lPYeMCSgILhsguyPK4Kj6iIoKSgqCkoWGRWzjiMDqmpqE1\nmhCMSmoqKAkIpYGmOKaiS6U8CgKC7O7n9wfDHdcFH/p9G5M+rxlm4O4555577uXOOfs59x4FgoKC\nEBkZiaKiIowePbrDskNDQxEXF4eDBw+ioaHhhf+PXoaCggIEBwejW7duaGxsRGxsLIyNjeHt7Y2A\ngACYmJjAwsJC6OAAbd94jx8/Hj4+PujRowdMTExQUVEBKyurDvexatUqrFu3Dtu2bYOxsTEUCgWc\nnZ2Fzm5zczPi4uIwcuRITJ06FX5+fujfv79OBAcA7OzsUFxcLEQ0gLZoiYODA+RyOR49eoSxY8fC\n0tKyw/3++eef2LZtG0aNGoXZs2djw4YNGDhwoPBN5ZPi4+MRExMDtVoNoK1DefToUcjl8k7bdMiQ\nIejevTtkMhmMjIzQr18/rfZ7kkKhEKJkPXv2hKmpKaytrZGSkoKMjAwYGhp2+OanVatWYe3atdi9\nezdUKpXw3GRnrl27JkRIXja+l/3/72VP8vLyQlhYGMzNzTt95q3dzJkzsXr1asjlctjY2AjtOWLE\nCPj5+cHMzAyWlpYYN26ckEdPTw8JCQmIiIgQolaffvrp365vV/Cq9dUe5+HhgfXr18PCwgIDBw5E\nTU1Np2nd3d1x4cIF+Pv7w8rKCmZmZgCAMWPGYO7cuTA1NUXv3r11vkhISEjARx99BAAwMDDAJ598\nItxLXzV69OQcJMbYS3fjxg2UlpbC09MTtbW1kEgkyM7OFqbnPK/FixdjzZo1GDJkyD9UU8aeX2Nj\nI8LCwrB3796XXZVXzqZNmyAWi1+5l7zwvYwx9rK9/Hg4Y0zHwIEDceTIEQQEBCAkJARRUVEv1Dlo\nbm6GTCbDW2+9xZ0D9q/Rs2dPTJ8+HcePH3/ZVXmlVFZWoqGh4ZUb6AB8L2OMvXwc2WGMMcYYY4x1\nSRzZYYwxxhhjjHVJPNhhjDHGGGOMdUk82GGMMcYYY4x1STzYYYx1CeXl5RCLxTrb2xdF+7c7ePAg\njhw5AgCIjY3tdAHNZ0lPT0dsbOwL5fk3tVFDQwNkMhkkEglu3bolbP87xwW0rSv0+GuM/5eCg4Nf\nKL1YLH7uxW7/DeX+Uzr7X62trcWSJUsglUohlUpx4sSJl1A7xlhXw+vsMMbYv8CFCxcgEoledjVe\nuqtXr8LIyOhvD/aeVFdXJyzq979WWFj4j5T7X5WUlIQRI0Zg27ZtUCqVCAgIwLvvvtvhmk2MMfa8\nOLLDGPtP0Gg0SEhIgKenJyQSCXbs2AEAkEqluHHjBgBg5cqVwiJqFy9exKJFi7TKeFZalUqFDz/8\nEIGBgXB1dUVYWBiam5vR0NCARYsWQSaTQSaT6axUnZeXh9OnTyM5ORm5ubkAgJycHPj5+cHFxQUH\nDhwA0LZOTUxMDGQyGby9vYVI0JNu376NmTNnQiKR4PPPPxdW1968eTMCAgLg7u6O4OBgVFVVaeW7\nd+8eFixYgICAADg7O2PLli0A2qIqH3zwAebPn48pU6YgPj4eQNvK9QqFAu7u7pg2bZqwfs7t27cx\nb948+Pj4QC6X48qVKzp1rKqqwuLFiyGVSuHj44OzZ8+iuroaa9aswbVr1xAaGtrpuSwpKYFcLoeP\njw/mz58PpVIJoG2g5O/vD6lUilmzZuHu3btISEhARUUFli5dqhNR2Lp1K7Zu3QoAsLe3R0hICLy9\nvdHa2oodO3bAx8cHXl5e2LRpE558cWn7QoP+/v4AgOzsbHh7e0MqlSIsLEynbdulpKRg+vTpcHd3\nR3FxMYC2QVP78bi6uuLkyZMA2iJ8CQkJkMvlEIvFSEtLAwDcv38fCxcuhFQqRWRkJFpaWgAApaWl\nCAgIgEwmg1wuR1lZWYd12L59O6ZNmwapVIrExESo1WqUl5dj+vTpiI6OhkQiwZw5c3D//n2tfB9/\n/DG++eYbAMCBAwfg4eEBAGhtbYWTkxNaW1s7bQexWIzIyEi4u7trLYB4/PhxeHl5oaamBhMnThSi\nZYMHD4aFhcVTF0tkjLHnQowx1gUolUoaNWoUeXl5af0MHz6ciIj27dtHYWFhpFKpqKmpiXx9fSk7\nO5sUCgWlpqYSEZFEIiGJREJERFu2bKH9+/dr7eNZaQsLCyk+Pp6IiNRqNc2aNYuOHTtG6enpwvYr\nV65QYmKiTv1jYmIoLS1N+H3x4sWk0Wjo2rVrZGdnJ+x/7969RET04MED8vT0pDt37miVk5aWRo6O\njoXGedkAAAmvSURBVFRdXU0tLS0UFBREP/74I5WVlVF4eDip1WoiIoqOjqZdu3YREQlttHPnTkpP\nTyciovr6erK1taXq6mpKS0sjJycnevDgATU1NdHkyZOptLSUsrKyKCgoiFpaWqihoYG8vLyooqKC\nAgMD6bfffiMiouvXr9P777+vc7zLli2j3bt3ExHRnTt3aNKkSVRZWUkFBQU0a9YsnfRpaWkUExND\nLS0tJJVK6Y8//iAiorNnz9KcOXOIiGjatGl0+vRpIiLav38/JSYmklKpJBcXF+Eaaf+diCg5OZmS\nk5OFNigoKCAiojNnzlBERASpVCpSq9W0YsUKysjI0KlTe7tVVVWRo6MjKZVKIiL68ssvKSIiQie9\ni4sL7dy5k4iIUlNThTQRERH0+++/ExFRXl6ecF3FxMTQ0qVLSaPRUGlpKYlEIiIiWr9+PSUlJRER\nUWFhIQ0fPpyUSiXFxsZSVlYWERGlp6fT999/r1OHnJwc8vf3p6amJmptbaXQ0FDat28fKZVKGjFi\nhHDewsPD6euvv9bK294uRESRkZHk4OBAlZWVlJ+fT8uXL39qO7i4uAjXd/t5yM3NJalUSpWVlTr1\n3L17NwUEBJBGo9H5jDHGXgRPY2OMdRn9+/dHZmam1rb251HOnz8PHx8f6Ovro3v37pBKpcjPz4eb\nmxv27NkDe3t7DB06FDdv3kR1dTXOnj2L5ORkrbKcnJyemnbQoEHo06cP9u/fj5s3b6KsrAxNTU2w\ntbVFUlIS7t27B2dn5+d6hsTV1RV6enoYNmwYamtrAbRFgJqbm4Vv+JuamnD9+nUMHjxYK69YLEbf\nvn0BAB4eHigsLISbmxtiYmLw3Xff4datW/j111/x+uuva+VbsGABCgoKsGvXLly/fh2tra14+PAh\nAMDW1ha9evUC0Pate11dHYqKiuDh4QEjIyMYGRkhMzMTjY2NuHz5MlavXi2U29TUhNraWpiZmQnb\nCgoKhOjI4MGDMW7cOBQXFwv76ExZWRmUSiWWLFkibGtoaEBNTQ0qKyvh4uICAJgxYwYAvNCzLOPG\njQMA5Ofno6SkBDKZDEDbwpaDBg3qNF9JSQnGjh0LKysrAEBgYKAQOXySm5sbAGDo0KHC4qoKhQLZ\n2dk4duwYiouL0djYKKSfNGkS9PT0MHz4cCHSUlhYiC+++AIAMGHCBOH8Ozk5YcOGDcjNzYVYLBba\n4nEFBQXw9PRE9+7dAQC+vr7IyMiAk5MTzM3N8fbbbwMAhg0bhrq6Oq28dnZ2WLt2LdRqNW7evIlp\n06ahqKgIly5dgrOz8zPbob19gbbncyIiIhAREQELCwut/Rw5cgSHDx/GV199BT09vU7bnTHGngcP\ndhhj/wkajUbrbyKCWq2Gra0tYmNjkZeXB5FIBHNzcxw7dgwqlUqng/ustKdOnUJycjJmz54NmUyG\n2tpaEBGsra3xww8/IDc3F9nZ2di9ezeysrLQrVvnM4n19fUBQKuzp9FooFAoMGrUKABtU8FMTU11\n8hoYGGjlMTAwwOXLl7Fy5UrMnTsX7u7u6Natm87UrMTERCiVSkgkEri5uSEvL09I89prrwnp9PT0\nQEQwMDDQql95eTlMTU2FgU+7u3fvok+fPjrt39H5eBaNRgMrKyuhfLVajaqqKhgaGmrVpaWlBRUV\nFVrb2uvdTqVSabWVsbGxUOacOXMwb948AEB9fb1wPjqr05PHolKpOkzb0XmdMWMG7OzsYGdnBwcH\nB0RFRQmftbf7046jvcypU6fC1tYW2dnZ2LNnD3JycuDi4iIM2sVisU5d29vh8X11tI/2z0eOHInD\nhw/DxsYGdnZ2yM/Pxy+//IKQkBBcvHjxqe3wZPkpKSmIioqCp6cnLC0thc8OHTqEuLi4Dq9txhh7\nUfzMDmPsP8He3h4ZGRlQq9V4+PAhDh8+DDs7OxgYGGDs2LFITU2FSCSCvb09tm/fDicnJ50ynpU2\nPz8fHh4e8PX1hYmJCc6fPw+1Wo19+/Zh69at8PDwwEcffYSamho0NDRola2vr//Mzr69vT2+/fZb\nAEBFRQW8vLzw119/6aQ7c+YM6uvr0dLSgqysLEycOBFFRUUQiUSQy+WwtrZGTk6Ozv7OnTuHBQsW\nwMPDA7du3cK9e/c67By3mzBhAk6cOCFEgEJCQlBVVQVra2thMHLu3DnMnDmzw2M5dOgQAECpVOLC\nhQt45513nnr8AGBjY4O6ujr8/PPPAIC0tDRERUWhd+/esLS0xE8//QQAyMzMxJYtW2BgYCB0uE1M\nTHD//n3U1NTg0aNHwvNRHdWtPUqlUqmwdOlSIQrzOH19fahUKiEq1R5FOnDgAOzs7J55LEDb8zdl\nZWVYvnw5Jk+ejFOnTj3zOnBwcBDat6SkBHfu3AEAREZG4tKlSwgKCsLy5ctx5coVuLq6IjMzE5mZ\nmVi+fDns7e1x9OhRNDc3Q6VSIS0tDfb29s9VV6AtepSSkgKRSASRSIRTp06hR48e6Nu37wu1Q58+\nfeDg4AC5XC5E+NqFhoZi5MiRz10nxhh7Go7sMMb+EwIDA1FWViY8gC6VSjFlyhQAbR24oqIivPnm\nm+jXrx+qq6vh7OzcYTlPS+vv74+oqCgcPXoUhoaGGD9+PMrLy7Fw4UKsWLECUqkU+vr6iI6OhomJ\niVa5EydORFJSEnr37t3pMYSHhyM+Ph4SiQRqtRrR0dE6U9GAtgHBokWLUF9fD4lEAkdHRwwbNgzh\n4eGQSqUAgNGjR+tM8Vq8eDFWrVoFY2NjDBgwoMM0j5syZQouX74MmUwGjUaD2bNn44033oBCoUB8\nfDx27twJQ0NDbN68WWc6UlxcHNatWye8dS0hIQH9+/fXet10R4yMjLBlyxZs3LgRLS0t6NWrFz77\n7DMAEParUChgZmaGTZs2wczMDIMGDUJwcDBSU1MREhICPz8/DBgwAGPGjOlwH2KxWHjYX61W4733\n3oOPj49OOldXV3h7eyM9PR0bNmxAeHg4WltbMWjQIGzcuPGpx9GuT58+8PPzg6enJwwMDGBvb4/m\n5mY0NTV1mmfZsmWIjY2Fp6cnbGxshGlsoaGhiIuLQ0pKCgwNDYUXSTzOxcUFV69eha+vL1QqFRwd\nHYWXOTwPZ2dnxMfHQyQSwdTUFObm5sL1b2Fh8cLtsGjRInh5eeHkyZPCFL9du3Zh2bJlnZ4fxhh7\nEXr0ZJyaMcYYY4wxxroAnsbGGGOMMcYY65J4sMMYY4wxxhjrkniwwxhjjDHGGOuSeLDDGGOMMcYY\n65J4sMMYY4wxxhjrkniwwxhjjDHGGOuSeLDDGGOMMcYY65L+DxyPe5Mx2oMLAAAAAElFTkSuQmCC\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x111e05dd8>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "_ = sns.countplot(\n", | |
| " data=data,\n", | |
| " x=\"How was the balance of lecture to hands-on work?\", \n", | |
| " hue='I have attended the stream',\n", | |
| " order=[\"Too much lecture\", \"Slightly too much lecture\", \"Balanced (lecture/hands-on)\", \n", | |
| " \"Slightly too much hands on\", \"Too much hands on\"]\n", | |
| ")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x112e56fd0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "fig = plt.figure(figsize=(18, 10))\n", | |
| "\n", | |
| "questions = [\n", | |
| " \"Please select how you felt the instructors performed as a team [Instructors gave clear answers to your questions]\",\n", | |
| " \"Please select how you felt the instructors performed as a team [Instructors were considerate]\",\n", | |
| " \"Please select how you felt the instructors performed as a team [Instructors were good communicators]\",\n", | |
| " \"Please select how you felt the instructors performed as a team [Instructors were enthusiastic]\"\n", | |
| "]\n", | |
| "\n", | |
| "for i, question in enumerate(questions):\n", | |
| " ax = fig.add_subplot(2, 2, i + 1)\n", | |
| "\n", | |
| " _ = sns.countplot(\n", | |
| " data=data,\n", | |
| " x=question, \n", | |
| " hue='I have attended the stream',\n", | |
| " order=[\"Never\", \"Rarely\", \"Sometimes\", \"Often\", \"All of the time\"],\n", | |
| " ax=ax\n", | |
| " )\n", | |
| " _ = plt.xlabel(question.split(\"[\")[1][:-1])\n", | |
| " _ = plt.legend(loc=\"upper left\")\n", | |
| " \n", | |
| "_ = fig.suptitle(\"Please select how you felt the instructors performed as a team\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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mTRqj0aMnatKkserWrYd+/HGHjhw5LC8vL40YMURr1nyjw4cPafbs92SMkbd3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\nKjLyiiZMCFJMTIy2bdsiY4w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6fv26qlevrh49eujw4cOqU6eO2rZt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\nCqt79+7aunWr/vjjD40cOVKGYahgwYKaMGGCcuXK6EeSk5O1bdsWjR0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JmjihUrOo7n5eWlwMBAvffee+rSpYtbxlQQBw8e1LJlyzRv3jyVKFFCVapUka+vrwICAvTDDz+o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\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x111f13320>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "fig = plt.figure(figsize=(18, 10))\n", | |
| "\n", | |
| "questions = [\n", | |
| " \"Please select how you felt the helpers performed as a team [Helpers gave clear answers to your questions]\",\n", | |
| " \"Please select how you felt the helpers performed as a team [Helpers were considerate]\",\n", | |
| " \"Please select how you felt the helpers performed as a team [Helpers were good communicators]\",\n", | |
| " \"Please select how you felt the helpers performed as a team [Helpers were enthusiastic]\"\n", | |
| "]\n", | |
| "\n", | |
| "for i, question in enumerate(questions):\n", | |
| " ax = fig.add_subplot(2, 2, i + 1)\n", | |
| "\n", | |
| " _ = sns.countplot(\n", | |
| " data=data,\n", | |
| " x=question, \n", | |
| " hue='I have attended the stream',\n", | |
| " order=[\"Never\", \"Rarely\", \"Sometimes\", \"Often\", \"All of the time\", \"Column 6\"],\n", | |
| " ax=ax\n", | |
| " )\n", | |
| " _ = plt.xlabel(question.split(\"[\")[1][:-1])\n", | |
| " _ = plt.legend(loc=\"upper left\")\n", | |
| " \n", | |
| "_ = fig.suptitle(\"Please select how you felt the helpers performed as a team\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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OnapSpYpt2+nTp+Xm5qZSpUo91OPu3bvX9rsx8M8UFRWlxMTEO7Yf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\nAABTInYAAAAAmNL/AzGZhW8nLayAAAAAAElFTkSuQmCC\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x1131e1588>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "_ = sns.countplot(\n", | |
| " data=data,\n", | |
| " x=\"Were there enough helpers at the workshop?\", \n", | |
| " hue='I have attended the stream',\n", | |
| " order=[\"No\", \"Yes\"]\n", | |
| ")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "#### Do you have specific comments about the instructors or helpers of the General Scientific Computing stream?" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "--------\n", | |
| "Great job!\n", | |
| "--------\n", | |
| "They managed to be clear and friendly\n", | |
| "--------\n", | |
| "This is an excellent setup for the tutorial. You had enough helpers so that it didn't slow the tutorial down and whenever I had a question, there was always a helper available.\n", | |
| "--------\n", | |
| "No, they were all fine\n", | |
| "--------\n", | |
| "python instructor was a bit confusing but it was already the afternoon, might have been my atention\n", | |
| "--------\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "for comment in filter(lambda x: str(x).strip() != \"nan\",\n", | |
| " data[data[\"I have attended the stream\"] == \"General Scientific Computing\"][\"Do you have specific comments about the instructors or helpers?\"].values):\n", | |
| " print(\"--------\")\n", | |
| " print(comment)\n", | |
| "print(\"--------\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "#### Do you have specific comments about the instructors or helpers of the Advanced Topics stream?" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "--------\n", | |
| "just to thank them for their time, particularly in regards to preparation\n", | |
| "--------\n", | |
| "Thank you!!\n", | |
| "--------\n", | |
| "very good work. Maybe next time the handouts should be sent ahead of the wokshop so participants can get familiar with the topic.\n", | |
| "--------\n", | |
| "They did a grat work. It was very useful. \n", | |
| "--------\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "for comment in filter(lambda x: str(x).strip() != \"nan\",\n", | |
| " data[data[\"I have attended the stream\"] == \"Advanced Topics\"][\"Do you have specific comments about the instructors or helpers?\"].values):\n", | |
| " print(\"--------\")\n", | |
| " print(comment)\n", | |
| "print(\"--------\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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xcbEKDAxU4cKF/2/cu6939Xxz2ufYAAAAAPnRpEkz7d+/VwcO/OC4LC4uVomJCbLZbKpY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\nAAAAAElFTkSuQmCC\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x11352f5c0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "_ = sns.countplot(\n", | |
| " data=data,\n", | |
| " x=\"How much of the information presented at this workshop was new to you? \", \n", | |
| " hue='I have attended the stream',\n", | |
| " order=[\"None of it\", \"Some of it\", \"About half of it\", \"Most of it\", \"All of it\"]\n", | |
| ")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "#### Any comments on what was the best thing about this workshop? [General Scientifc Computing]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 12, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "--------\n", | |
| "The informal atmosphere which enables deeper exchange than classic conferences. It would be great if this could be kept in future and the workshops do not become to formal!\n", | |
| "--------\n", | |
| "Good collaborative atmosphere: this always motivates people. Reasonable work for the time available\n", | |
| "--------\n", | |
| "I thought this workshop was excellent. I learned a lot and can apply it all directly into my work.\n", | |
| "--------\n", | |
| "The content were really targeted for the need of participants without a great knowledge of coding of getting aware of energy models functioning\n", | |
| "--------\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "for comment in filter(lambda x: str(x).strip() != \"nan\",\n", | |
| " data[data[\"I have attended the stream\"] == \"General Scientific Computing\"][\"Any comments on what was the best thing about this workshop?\"].values):\n", | |
| " print(\"--------\")\n", | |
| " print(comment)\n", | |
| "print(\"--------\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "#### Any comments on what was the best thing about this workshop? [Advanced Topics]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 13, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "--------\n", | |
| "git clone is really the best way of distributing files, better than zip files, as overwritten files can be recovered and new files stored (yes I know I could also create a local git repo to do the same thing) / Jupyter notebooks are great, I hadn't used them before\n", | |
| "--------\n", | |
| "The visualisation\n", | |
| "--------\n", | |
| "The whole atmosphere and the visualization part.\n", | |
| "--------\n", | |
| "It was a useful condensed package of ready-to-use tools for improving my daily work of start exploring new, relevant functions. Awesome!\n", | |
| "--------\n", | |
| "Everything was just right, it seems to have been very well planned and well thought out\n", | |
| "--------\n", | |
| "the open and welcoming atmosphere\n", | |
| "--------\n", | |
| "Insides to a many interesing topics, that touch the every day work. New inspiration to use different tools, packages,\n", | |
| "--------\n", | |
| "For me personally, the tutorial day and the personal chats with any of the participants, especially in the breaks, helping me to answer my question and/or solve problems.\n", | |
| "--------\n", | |
| "Stefan Pfenningers presentation on plotting fast was impressive\n", | |
| "--------\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "for comment in filter(lambda x: str(x).strip() != \"nan\",\n", | |
| " data[data[\"I have attended the stream\"] == \"Advanced Topics\"][\"Any comments on what was the best thing about this workshop?\"].values):\n", | |
| " print(\"--------\")\n", | |
| " print(comment)\n", | |
| "print(\"--------\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "#### Any comments on what needed the most improvement at this workshop? [General Scientific Computing]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 14, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "--------\n", | |
| "Would have liked to attend the Advanced tutorial too (space was limited though)\n", | |
| "--------\n", | |
| "The amount of coffee ;-)\n", | |
| "--------\n", | |
| "A few hours' tutorial is a good start but obviously not enough to get a thorough good introduction to the topic. It would be good to have a longer tutorial with more hands-on exercises. Since I guess the \"teaching capacity\" is limited, it could be an idea to have a short \"real tutorial\" plus a number of open spaces where people work together and only a smaller number of \"supervisors\" are at times present. \n", | |
| "--------\n", | |
| "I enjoyed the tutorial so much that I wish that it was 2-3 days long!\n", | |
| "--------\n", | |
| "Any in particular, I was just a bit disappointed that I could not follow the introduction to pyomo in the advanced topics workshop because it was in parallel with the general scientific computing workshop. Maybe it was a too advanced topic for the current workshop, but since pyomo is used by all the models presented at Openmod, I would have liked to know a bit about it even being new to coding.\n", | |
| "--------\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "for comment in filter(lambda x: str(x).strip() != \"nan\",\n", | |
| " data[data[\"I have attended the stream\"] == \"General Scientific Computing\"][\"Any comments on what needed the most improvement at this workshop? \"].values):\n", | |
| " print(\"--------\")\n", | |
| " print(comment)\n", | |
| "print(\"--------\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "#### Any comments on what needed the most improvement at this workshop? [Advacned Topics]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 15, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "--------\n", | |
| "Coding samples should already be filled in jupyter notebook, so that there is no need to copy. \n", | |
| "--------\n", | |
| "More time, better preparation / complete Installation\n", | |
| "--------\n", | |
| "the Jupyter notebooks should come with Creative Commons CC BY[-SA] licenses so that code and text can be extracted and worked into the openmod wiki and, if BY-SA, Wikipedia also / you should also have a feedback box on this form for any further comments / ps thanks for organizing all this\n", | |
| "--------\n", | |
| "Please split the GIS and the database tutorial, that was too much for the allocated time.\n", | |
| "--------\n", | |
| "Preparation by participants was not satisfactory and hindered reasonable pace.\n", | |
| "--------\n", | |
| "I have struggled to pre install some of the requested softwares and files before the tutorial, so more detailed information on that would have been useful to me.\n", | |
| "--------\n", | |
| "No improvement necessary, keep up whatever you're doing, you guys are great\n", | |
| "--------\n", | |
| "Keep the tutorial day.\n", | |
| "--------\n", | |
| "For preparing the material, program set up etc. on the personal computer a reminder about a week in advance would be great to have time to organise it. \n", | |
| "--------\n", | |
| "More time for the tutorial workshop, especially the GIS session was quite stressful and should be split into (a) PostgreSQL / PostGIS Introduction and (b) QGIS itself. Moreover, in the graphing tutorial we did not use Inkscape - I believe we were somehow quite short in time... \n", | |
| "--------\n", | |
| "It should be emphasized that people should have everything installed. It cost 45 min out of 90 minutes in the Pyomo session. \n", | |
| "--------\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "for comment in filter(lambda x: str(x).strip() != \"nan\",\n", | |
| " data[data[\"I have attended the stream\"] == \"Advanced Topics\"][\"Any comments on what needed the most improvement at this workshop? \"].values):\n", | |
| " print(\"--------\")\n", | |
| " print(comment)\n", | |
| "print(\"--------\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "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": "3.6.2" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
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