From 7ddf93d74119b382c369dd721b3de6e560782e5d Mon Sep 17 00:00:00 2001 From: mbobra Date: Mon, 10 Feb 2020 08:40:24 -0800 Subject: [PATCH] initial commit --- ...computational_tools_in_solar_physics.ipynb | 5430 +++++++++++++++++ 2019_survey/free_form_comments.csv | 46 + .../raw_survey_responses_no_comments.csv | 369 ++ CITATION | 2 + README.md | 16 +- 5 files changed, 5862 insertions(+), 1 deletion(-) create mode 100755 2019_survey/computational_tools_in_solar_physics.ipynb create mode 100644 2019_survey/free_form_comments.csv create mode 100644 2019_survey/raw_survey_responses_no_comments.csv create mode 100644 CITATION diff --git a/2019_survey/computational_tools_in_solar_physics.ipynb b/2019_survey/computational_tools_in_solar_physics.ipynb new file mode 100755 index 0000000..aff8d8e --- /dev/null +++ b/2019_survey/computational_tools_in_solar_physics.ipynb @@ -0,0 +1,5430 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# A Survey of Computational Tools in Solar Physics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Between February 7, 2019 and July 28, 2019, The SunPy Project opened a 13-question survey to understand the software and hardware usage of the solar physics community. The survey was similar to one conducted by Ivelina Momcheva and Erik Tollerud in 2015, who surveyed 1142 astronomers about [software use in astrophysics](https://arxiv.org/abs/1507.03989). This notebook analyzes the results of the survey." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import pandas as pd\n", + "import matplotlib\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.cm as cm\n", + "from matplotlib.colors import ListedColormap\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We used pandas version 1.0.1, matplotlib version 3.1.3, numpy version 1.18.1, and seaborn version 0.10.0 to analyze our results." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pandas version 1.0.1\n", + "matplotlib version 3.1.3\n", + "numpy version 1.18.1\n", + "seaborn version 0.10.0\n" + ] + } + ], + "source": [ + "print(\"pandas version\", pd.__version__)\n", + "print(\"matplotlib version\", matplotlib.__version__)\n", + "print(\"numpy version\", np.__version__)\n", + "print(\"seaborn version\", sns.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The survey received n=368 responses." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of responses to the survey: 368\n" + ] + } + ], + "source": [ + "df = pd.read_csv('raw_survey_responses_no_comments.csv', delimiter=',')\n", + "all_of_the_responses = len(df)\n", + "print(\"Total number of responses to the survey:\", all_of_the_responses)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We begin our analysis with Question 2, since we decided to drop four unusable responses for a total of n=364 usable responses. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Question 2: How would you describe the stage of your career?\n", + "\n", + "πŸ”² **Undergraduate student \n", + "πŸ”² Graduate student \n", + "πŸ”² Postdoc \n", + "πŸ”² Faculty, Staff Scientist, Researcher \n", + "πŸ”² Software Developer \n", + "πŸ”² Instrument Developer \n", + "πŸ”² Retired \n", + "πŸ”² My main profession is something other than solar physics \n", + "πŸ”² Other (Respondents can write in their own description)** " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "How would you describe the stage of your career?\n", + "1 year PhD student 1\n", + "Faculty, Staff Scientist, Researcher 197\n", + "Graduate student 75\n", + "Hobbyist 1\n", + "Instrument developer 5\n", + "My role is something other than solar physics or software development 1\n", + "Part time PhD student in Computational Astrophysics, with a full time job as a software developer 1\n", + "Ph.D 1\n", + "Postdoc 53\n", + "Recently completed PhD but now working in industry 1\n", + "Retired 4\n", + "Retired, but still doing research at a University 1\n", + "Software developer 16\n", + "Solar Dimension of Earthquake researches 1\n", + "SolarSoft, part time retiree. 1\n", + "Undergrad student and working in IT company as DevOps 1\n", + "Undergraduate student 6\n", + "partially retired 1\n", + "unemployed 1\n", + "dtype: int64" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grouped2 = df.groupby('How would you describe the stage of your career?')\n", + "grouped2.size()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The raw breakdown contains 8 categories, one of which was an open-ended response (which garnered 12 unique answers):\n", + "\n", + "1. **Faculty, Staff Scientist, Researcher**: 197\n", + "2. **Graduate student**: 75\n", + "3. **Postdoc**: 53\n", + "4. **Software developer**: 16\n", + "5. **Undergraduate student**: 6\n", + "6. **Instrument developer**: 5\n", + "7. **Retired**: 4\n", + "8. **Filled-in Responses**: 12\n", + " * Solar Dimension of Earthquake researches: 1\n", + " * Hobbyist: 1\n", + " * My role is something other than solar physics or software development: 1\n", + " * Part time PhD student in Computational Astrophysics, with a full time job as a software developer: 1\n", + " * Ph.D: 1\n", + " * unemployed: 1\n", + " * 1 year PhD student: 1\n", + " * SolarSoft, part time retiree: 1\n", + " * partially retired: 1\n", + " * Retired, but still doing research at a University: 1\n", + " * Recently completed PhD but now working in industry: 1\n", + " * Undergrad student and working in IT company as DevOps: 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We do not have statistically significant numbers to analyze the data by profession while retaining all these categories. Therefore, we will collapse them as follows:\n", + "\n", + "* Faculty, Staff Scientist, Researcher: 197 ==> Move to **Faculty, Staff Scientist, or Researcher** category\n", + "* Graduate student: 75 ==> Move to **Graduate or Undergraduate Student** category\n", + "* **Postdoc**: 53\n", + "* Software developer: 16 ==> Move to **Software or Instrument Developer** category\n", + "* Undergraduate student: 6 ==> Move to **Graduate or Undergraduate Student** category\n", + "* Instrument developer: 5 ==> Move to **Software or Instrument Developer** category\n", + "* Retired: 4 ==> Move to **Faculty, Staff Scientist, or Researcher** category\n", + "* Filled-in Responses: 12\n", + " * Solar Dimension of Earthquake researches: 1 ==> Move to **Faculty, Staff Scientist, or Researcher** category\n", + " * Hobbyist: 1 ==> Do not know where to put this response; drop row\n", + " * My role is something other than solar physics or software development: 1 ==> Do not know where to put this response; drop row\n", + " * Part time PhD student in Computational Astrophysics, with a full time job as a software developer: ==> Move to **Software or Instrument Developer** category\n", + " * Ph.D: 1 ==> Move to **Graduate or Undergraduate Student** category\n", + " * unemployed: 1 ==> Do not know where to put this response; drop row\n", + " * 1 year PhD student: 1 ==> Move to **Graduate or Undergraduate Student** category\n", + " * SolarSoft, part time retiree: 1 ==> Move to **Faculty, Staff Scientist, or Researcher** category\n", + " * partially retired: 1 ==> Move to **Faculty, Staff Scientist, or Researcher** category\n", + " * Retired, but still doing research at a University: 1 ==> Move to **Faculty, Staff Scientist, or Researcher** category\n", + " * Recently completed PhD but now working in industry: 1 ==> Do not know where to put this response; drop row\n", + " * Undergrad student and working in IT company as DevOps: 1 ==> Move to **Graduate or Undergraduate Student** category\n", + " \n", + "This gives the following breakdown with n=364:\n", + "* **Faculty, Staff Scientist, or Researcher**: 205\n", + "* **Graduate or Undergraduate Student**: 84\n", + "* **Postdoc**: 53\n", + "* **Software or Instrument Developer**: 22" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Concatenate given categories\n", + "df = df.replace(['Software developer'], ['Software or Instrument Developer']);\n", + "df = df.replace(['Instrument developer'], ['Software or Instrument Developer']);\n", + "df = df.replace(['Graduate student'], ['Graduate or Undergraduate Student']);\n", + "df = df.replace(['Undergraduate student'], ['Graduate or Undergraduate Student']);\n", + "df = df.replace(['Retired'], ['Faculty, Staff Scientist, or Researcher']);\n", + "df = df.replace(['Faculty, Staff Scientist, Researcher'], ['Faculty, Staff Scientist, or Researcher']);\n", + "\n", + "# Concatenate free-form categories\n", + "df = df.replace(['Solar Dimension of Earthquake researches'], ['Faculty, Staff Scientist, or Researcher']);\n", + "df = df.replace(['Part time PhD student in Computational Astrophysics, with a full time job as a software developer'], ['Software or Instrument Developer']);\n", + "df = df.replace(['Ph.D'], ['Graduate or Undergraduate Student']);\n", + "df = df.replace(['1 year PhD student'], ['Graduate or Undergraduate Student']);\n", + "df = df.replace(['SolarSoft, part time retiree.'], ['Faculty, Staff Scientist, or Researcher']);\n", + "df = df.replace(['partially retired'], ['Faculty, Staff Scientist, or Researcher']);\n", + "df = df.replace(['Retired, but still doing research at a University'], ['Faculty, Staff Scientist, or Researcher']);\n", + "df = df.replace(['Undergrad student and working in IT company as DevOps'], ['Graduate or Undergraduate Student']);" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Drop rows\n", + "df = df.drop(df.index[df['How would you describe the stage of your career?'] == 'Hobbyist']);\n", + "df = df.drop(df.index[df['How would you describe the stage of your career?'] == 'My role is something other than solar physics or software development'])\n", + "df = df.drop(df.index[df['How would you describe the stage of your career?'] == 'unemployed']);\n", + "df = df.drop(df.index[df['How would you describe the stage of your career?'] == 'Recently completed PhD but now working in industry']);" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "df = df.reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "How would you describe the stage of your career?\n", + "Faculty, Staff Scientist, or Researcher 205\n", + "Graduate or Undergraduate Student 84\n", + "Postdoc 53\n", + "Software or Instrument Developer 22\n", + "dtype: int64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grouped2 = df.groupby('How would you describe the stage of your career?')\n", + "grouped2.size()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "total_numbers = pd.DataFrame(grouped2.size())\n", + "total_numbers = total_numbers.reindex(['Faculty, Staff Scientist, or Researcher', 'Postdoc', 'Graduate or Undergraduate Student', 'Software or Instrument Developer'])\n", + "total_numbers = total_numbers.rename(columns={0: \"Total Numbers\"})" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Total Numbers
How would you describe the stage of your career?
Faculty, Staff Scientist, or Researcher205
Postdoc53
Graduate or Undergraduate Student84
Software or Instrument Developer22
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" + ], + "text/plain": [ + " Total Numbers\n", + "How would you describe the stage of your career? \n", + "Faculty, Staff Scientist, or Researcher 205\n", + "Postdoc 53\n", + "Graduate or Undergraduate Student 84\n", + "Software or Instrument Developer 22" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "total_numbers" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "364\n" + ] + } + ], + "source": [ + "total_responses = len(df)\n", + "print(total_responses)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Total Percentages
How would you describe the stage of your career?
Faculty, Staff Scientist, or Researcher56.318681
Postdoc14.560440
Graduate or Undergraduate Student23.076923
Software or Instrument Developer6.043956
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" + ], + "text/plain": [ + " Total Percentages\n", + "How would you describe the stage of your career? \n", + "Faculty, Staff Scientist, or Researcher 56.318681\n", + "Postdoc 14.560440\n", + "Graduate or Undergraduate Student 23.076923\n", + "Software or Instrument Developer 6.043956" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "percentages = (total_numbers/len(df))*100\n", + "total_percentages = percentages.rename(columns={'Total Numbers': \"Total Percentages\"})\n", + "total_percentages" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Conclusions: \n", + "* Slightly more than half of the respondents (56%) are Faculty, Staff Scientists, or Researchers (n=205) \n", + "* A quarter of the respondents (23%) are Graduate or Graduate or Undergraduate Students (n=84) \n", + "* About 15% of the respondents are Postdocs (n=53) \n", + "* 6% of the respondents are Software or Instrument Developers (n=22) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Question 1: Which of these areas of solar physics do you work in? Check all that apply.\n", + "\n", + "πŸ”² **Observational (Space-Based) \n", + "πŸ”² Observational (Ground-Based) \n", + "πŸ”² Instrumentation \n", + "πŸ”² Theory \n", + "πŸ”² Numerical Simulations** " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "theory_numbers = df['Which of these areas of solar physics do you work in? Check all that apply.'].str.contains('Theory').sum()\n", + "instrumentation_numbers = df['Which of these areas of solar physics do you work in? Check all that apply.'].str.contains('Instrumentation').sum()\n", + "ground_numbers = df['Which of these areas of solar physics do you work in? Check all that apply.'].str.contains('Ground').sum()\n", + "space_numbers = df['Which of these areas of solar physics do you work in? Check all that apply.'].str.contains('Space').sum()\n", + "sim_numbers = df['Which of these areas of solar physics do you work in? Check all that apply.'].str.contains('Simulations').sum()\n", + "observational_numbers = df['Which of these areas of solar physics do you work in? Check all that apply.'].str.contains('Observational').sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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All Participants
Observational (Space-Based)75.549451
Observational (Ground-Based)46.428571
Instrumentation25.549451
Theory28.846154
Numerical Simulations46.978022
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" + ], + "text/plain": [ + " All Participants\n", + "Observational (Space-Based) 75.549451\n", + "Observational (Ground-Based) 46.428571\n", + "Instrumentation 25.549451\n", + "Theory 28.846154\n", + "Numerical Simulations 46.978022" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data1 = {'All Participants': [(space_numbers/total_responses)*100., (ground_numbers/total_responses)*100., (instrumentation_numbers/total_responses)*100., (theory_numbers/total_responses)*100., (sim_numbers/total_responses)*100.]}\n", + "df1 = pd.DataFrame(data1, index=['Observational (Space-Based)', 'Observational (Ground-Based)', 'Instrumentation', 'Theory', 'Numerical Simulations'], columns = ['All Participants'])\n", + "df1" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Percentage of Observational (Space-Based or Ground-Based) 82.41758241758241\n" + ] + } + ], + "source": [ + "print(\"Percentage of Observational (Space-Based or Ground-Based)\",(observational_numbers/total_responses)*100.)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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All Participants
Observational (Space-Based)275
Observational (Ground-Based)169
Instrumentation93
Theory105
Numerical Simulations171
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" + ], + "text/plain": [ + " All Participants\n", + "Observational (Space-Based) 275\n", + "Observational (Ground-Based) 169\n", + "Instrumentation 93\n", + "Theory 105\n", + "Numerical Simulations 171" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data1a = {'All Participants': [space_numbers, ground_numbers, instrumentation_numbers, theory_numbers, sim_numbers]}\n", + "df1a = pd.DataFrame(data1a, index=['Observational (Space-Based)', 'Observational (Ground-Based)', 'Instrumentation', 'Theory', 'Numerical Simulations'], columns = ['All Participants'])\n", + "df1a" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Conclusions:\n", + "* Most of the solar physics community (75%) works on space-based missions.\n", + "* 82% of the community works on observational data (space-based or ground-based)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Question 3: What country is your institution in?\n", + "πŸ”² **Respondents check appropriate country from a list of options.**" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "grouped3 = df.groupby('What country is your institution in?')" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The survey garnered responses from 35 countries.\n" + ] + } + ], + "source": [ + "print(\"The survey garnered responses from\",len(grouped3.sum()),\"countries.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "What country is your institution in?\n", + "Argentina 2\n", + "Austria 5\n", + "Belgium 13\n", + "Brazil 5\n", + "China 4\n", + "Costa Rica 1\n", + "Czech Republic 2\n", + "Ethiopia 1\n", + "Finland 3\n", + "France 7\n", + "Germany 34\n", + "Greece 1\n", + "Hungary 3\n", + "India 29\n", + "Indonesia 3\n", + "Ireland 1\n", + "Italy 3\n", + "Japan 14\n", + "Latvia 1\n", + "Mexico 2\n", + "Netherlands 1\n", + "Norway 11\n", + "Romania 1\n", + "Russia 2\n", + "Serbia 1\n", + "Slovakia 1\n", + "South Korea 1\n", + "Spain 10\n", + "Sweden 5\n", + "Switzerland 6\n", + "Taiwan 2\n", + "Turkey 1\n", + "United Arab Emirates 1\n", + "United Kingdom 33\n", + "United States 154\n", + "dtype: int64" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grouped3.size()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Percentage of respondents from the United States: 42.30769230769231\n", + "Percentage of respondents from the United Kingdom: 9.065934065934066\n", + "Percentage of respondents from Germany: 9.340659340659341\n", + "Percentage of respondents from India: 7.967032967032966\n", + "Percentage of respondents from Japan: 3.8461538461538463\n", + "Percentage of respondents from Belgium: 3.571428571428571\n", + "Percentage of respondents from Norway: 3.021978021978022\n", + "Percentage of respondents from Spain: 2.7472527472527473\n" + ] + } + ], + "source": [ + "print(\"Percentage of respondents from the United States:\",(len(grouped3.get_group('United States')))/len(df)*100.)\n", + "print(\"Percentage of respondents from the United Kingdom:\",(len(grouped3.get_group('United Kingdom')))/len(df)*100.)\n", + "print(\"Percentage of respondents from Germany:\",(len(grouped3.get_group('Germany')))/len(df)*100.)\n", + "print(\"Percentage of respondents from India:\",(len(grouped3.get_group('India')))/len(df)*100.)\n", + "print(\"Percentage of respondents from Japan:\",(len(grouped3.get_group('Japan')))/len(df)*100.)\n", + "print(\"Percentage of respondents from Belgium:\",(len(grouped3.get_group('Belgium')))/len(df)*100.)\n", + "print(\"Percentage of respondents from Norway:\",(len(grouped3.get_group('Norway')))/len(df)*100.)\n", + "print(\"Percentage of respondents from Spain:\",(len(grouped3.get_group('Spain')))/len(df)*100.)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Conclusions:\n", + "* The survey garnered responses from 35 countries.\n", + "* The US had the largest number of respondents at 42%.\n", + "* About three-quarters of the respondents came from the U.S., U.K., Germany, India, and Japan." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Question 4: Do you self-identify as one or more underrepresented minorities in solar physics? This question is optional.\n", + "πŸ”² **Yes** \n", + "πŸ”² **No**" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number that identified as 1+ underrepresented minorities in solar physics: 49\n", + "Total number that did not identify as 1+ underrepresented minorities in solar physics: 251\n", + "Total number of people who answered this question: 300\n", + "Percentage of people who answered this question: 82.41758241758241\n", + "Percentage of total people that identified as 1+ underrepresented minorities in solar physics: 13.461538461538462\n", + "Percentage of the people who answered this question that identified as 1+ underrepresented minorities in solar physics: 16.333333333333332\n" + ] + } + ], + "source": [ + "total_um_yes = len(df.loc[df['Do you self-identify as one or more underrepresented minorities in solar physics? This question is optional.'] == 'Yes'])\n", + "total_um_no = len(df.loc[df['Do you self-identify as one or more underrepresented minorities in solar physics? This question is optional.'] == 'No'])\n", + "total_answered_question = total_um_yes + total_um_no\n", + "\n", + "print(\"Total number that identified as 1+ underrepresented minorities in solar physics:\", total_um_yes)\n", + "print(\"Total number that did not identify as 1+ underrepresented minorities in solar physics:\", total_um_no)\n", + "print(\"Total number of people who answered this question:\", total_answered_question)\n", + "print(\"Percentage of people who answered this question:\", (total_answered_question/total_responses)*100.)\n", + "print(\"Percentage of total people that identified as 1+ underrepresented minorities in solar physics:\", (total_um_yes/len(df)*100.))\n", + "print(\"Percentage of the people who answered this question that identified as 1+ underrepresented minorities in solar physics:\", (total_um_yes/total_answered_question)*100.)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Conclusions:\n", + "* Most of the survey respondents (82%) chose to answer an optional question about whether they self-identified as an underrepresented minority; 16% of this subset (13% of the total sample) said yes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Question 5: Do you self-identify as a unrepresented gender identity in Solar Physics? This question is optional.\n", + "πŸ”² **Yes** \n", + "πŸ”² **No**" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number that identified as as a unrepresented gender identity in solar physics: 31\n", + "Total number that did not identify as a unrepresented gender identity in solar physics: 260\n", + "Total number of people who answered this question: 291\n", + "Percentage of people who answered this question: 79.94505494505495\n", + "Percentage of total people that identified as as a unrepresented gender identity in solar physics: 8.516483516483516\n", + "Percentage of the people who answered this question that identified as as a unrepresented gender identity in solar physics: 10.652920962199312\n" + ] + } + ], + "source": [ + "total_um_yes = len(df.loc[df['Do you self-identify as a unrepresented gender identity in Solar Physics? This question is optional.'] == 'Yes'])\n", + "total_um_no = len(df.loc[df['Do you self-identify as a unrepresented gender identity in Solar Physics? This question is optional.'] == 'No'])\n", + "total_answered_question = total_um_yes + total_um_no\n", + "\n", + "print(\"Total number that identified as as a unrepresented gender identity in solar physics:\", total_um_yes)\n", + "print(\"Total number that did not identify as a unrepresented gender identity in solar physics:\", total_um_no)\n", + "print(\"Total number of people who answered this question:\", total_answered_question)\n", + "print(\"Percentage of people who answered this question:\", (total_answered_question/total_responses)*100.)\n", + "print(\"Percentage of total people that identified as as a unrepresented gender identity in solar physics:\", (total_um_yes/len(df))*100.)\n", + "print(\"Percentage of the people who answered this question that identified as as a unrepresented gender identity in solar physics:\", (total_um_yes/total_answered_question)*100.)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Conclusions:\n", + "* 79% of respondents chose to answer another optional question about whether they self-identified as a underrepresented gender identity; 11% of this subset (9% of the total sample) said yes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Question 6: Do you use software in your research?\n", + "\n", + "πŸ”² **Yes** \n", + "πŸ”² **No**" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "total_number = df['Do you use software in your research?'].count()\n", + "yes_numbers = len(df.loc[df['Do you use software in your research?'] == 'Yes'])\n", + "no_numbers = len(df.loc[df['Do you use software in your research?'] == 'No'])\n", + "yes_percentage = (yes_numbers/total_number)*100.\n", + "no_percentage = (no_numbers/total_number)*100." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Which of these areas of solar physics do you work in? Check all that apply.How would you describe the stage of your career?What country is your institution in?Do you self-identify as one or more underrepresented minorities in solar physics? This question is optional.Do you self-identify as a unrepresented gender identity in Solar Physics? This question is optional.Do you use software in your research?Have you had formal training in programming?Which of the following statements is most applicable to you?Which of the following have you personally utilized in your work within the last year?Have you cited software papers in your published research?Why haven’t you cited software in your research?On which of these have you run software for solar-physics research?
94Observational (Space-Based)Graduate or Undergraduate StudentUnited KingdomNoNoNoYes, a lot (e.g. CS courses at an undergraduat...I write mostly my own software.Python, SunPy, HTML/CSS, Github (or similar)NoI do not think software belongs in citationsLaptop / Desktop computer, Local Cluster
161Observational (Space-Based), Observational (Gr...Faculty, Staff Scientist, or ResearcherIndiaNaNNaNNoNoSomewhere in between.Spreadsheets (e.g. Excel)NoI do not think it is necessaryLaptop / Desktop computer
163TheoryGraduate or Undergraduate StudentIndiaNaNNaNNoNoSomewhere in between.MATLABNoI am not sure how to appropriately cite softwareLaptop / Desktop computer
\n", + "
" + ], + "text/plain": [ + " Which of these areas of solar physics do you work in? Check all that apply. \\\n", + "94 Observational (Space-Based) \n", + "161 Observational (Space-Based), Observational (Gr... \n", + "163 Theory \n", + "\n", + " How would you describe the stage of your career? \\\n", + "94 Graduate or Undergraduate Student \n", + "161 Faculty, Staff Scientist, or Researcher \n", + "163 Graduate or Undergraduate Student \n", + "\n", + " What country is your institution in? \\\n", + "94 United Kingdom \n", + "161 India \n", + "163 India \n", + "\n", + " Do you self-identify as one or more underrepresented minorities in solar physics? This question is optional. \\\n", + "94 No \n", + "161 NaN \n", + "163 NaN \n", + "\n", + " Do you self-identify as a unrepresented gender identity in Solar Physics? This question is optional. \\\n", + "94 No \n", + "161 NaN \n", + "163 NaN \n", + "\n", + " Do you use software in your research? \\\n", + "94 No \n", + "161 No \n", + "163 No \n", + "\n", + " Have you had formal training in programming? \\\n", + "94 Yes, a lot (e.g. CS courses at an undergraduat... \n", + "161 No \n", + "163 No \n", + "\n", + " Which of the following statements is most applicable to you? \\\n", + "94 I write mostly my own software. \n", + "161 Somewhere in between. \n", + "163 Somewhere in between. \n", + "\n", + " Which of the following have you personally utilized in your work within the last year? \\\n", + "94 Python, SunPy, HTML/CSS, Github (or similar) \n", + "161 Spreadsheets (e.g. Excel) \n", + "163 MATLAB \n", + "\n", + " Have you cited software papers in your published research? \\\n", + "94 No \n", + "161 No \n", + "163 No \n", + "\n", + " Why haven’t you cited software in your research? \\\n", + "94 I do not think software belongs in citations \n", + "161 I do not think it is necessary \n", + "163 I am not sure how to appropriately cite software \n", + "\n", + " On which of these have you run software for solar-physics research? \n", + "94 Laptop / Desktop computer, Local Cluster \n", + "161 Laptop / Desktop computer \n", + "163 Laptop / Desktop computer " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.loc[df['Do you use software in your research?'] == 'No']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For Question 6, we report $\\sqrt3/364$, or 0.5\\%, as the percentage error in the number of no responses. Since this question required respondents to pick one response from a binary choice, we apply that same uncertainty to the yes responses." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "no_numbers_error = np.sqrt(no_numbers)\n", + "no_percentage_error = (no_numbers_error/total_number)*100." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make a plot using [seaborn](https://seaborn.pydata.org/) to inherit seaborn display properties within the notebook:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.set(style=\"whitegrid\")\n", + "sns.set_context(\"notebook\", font_scale=1.5)\n", + "fig, ax = plt.subplots(figsize=(18, 4))\n", + "sns.set_color_codes(\"pastel\")\n", + "sns.barplot(x=100.0, y=['Yes'], label=\"Yes\", color=\"b\")\n", + "sns.set_color_codes(\"muted\")\n", + "sns.barplot(x=no_percentage, y=[''], label=\"No\", color=\"b\")\n", + "ax.legend(ncol=2, loc=\"lower right\", frameon=True)\n", + "ax.set(xlim=(0, 100), xlabel=\"Percentage of total respondents (n=368)\", title=\"Do you use software in your research?\")\n", + "sns.despine(left=True, bottom=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make a plot using [pandas plotting tools](https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html) with [seaborn color tables](https://seaborn.pydata.org/tutorial/color_palettes.html): " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# figure width of 14.4 corresponds to Latex default \\textwidth * 3\n", + "fig1 = df6.T.plot.barh(figsize=(14.4, 2), stacked=False, cmap=sns.light_palette(\"Navy\", as_cmap=True, reverse=True), legend=False, xerr=[no_percentage_error, no_percentage_error], capsize=4, ecolor='silver')\n", + "fig1.grid(False)\n", + "fig1.grid(axis='x', color='whitesmoke')\n", + "fig1.set_title('Do you use software in your research?')\n", + "fig1.set_xlabel('Percentage of total respondents (n='+str(total_number)+')')\n", + "fig1.axvline(0, color='lightgray', lw=1.75)\n", + "fig1.set_xlim(0.0, 110.0)\n", + "fig1.spines['top'].set_visible(False)\n", + "fig1.spines['right'].set_visible(False)\n", + "fig1.spines['bottom'].set_visible(False)\n", + "fig1.spines['left'].set_visible(False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Conclusions:\n", + "* Solar Physics community survey (n=364) had 99% Yes and 1% No. The 1% looks like accidents, though.\n", + "* We used exactly the same question as the Astrophysics community survey (see Figure 2 of Momcheva & Tollerud, 2015). Their results (n=1142) were 100% Yes and 0% No." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Question 7: Have you had formal training in programming?\n", + "πŸ”² **Yes, a lot (e.g. CS courses at an undergraduate or graduate level)** \n", + "πŸ”² **Yes, a little (e.g. online classes, books, workshops)** \n", + "πŸ”² **No**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Training vs. Career" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "grouped7 = df.groupby(['Have you had formal training in programming?', 'How would you describe the stage of your career?'], sort=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# get the size of each group and unstack the multi-index labels\n", + "question_7_numbers = pd.DataFrame(grouped7.size()).unstack().T.reset_index(level=0, drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "# reindex the dataframe to match the order of total_numbers_df\n", + "question_7_numbers = question_7_numbers.reindex(['Faculty, Staff Scientist, or Researcher', 'Postdoc', 'Graduate or Undergraduate Student', 'Software or Instrument Developer'])" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# reorganize the columns to go from most to least experience\n", + "question_7_numbers = question_7_numbers[['Yes, a lot (e.g. CS courses at an undergraduate or graduate level)', 'Yes, a little (e.g. online classes, books, workshops)', 'No']]" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Have you had formal training in programming?Yes, a lot (e.g. CS courses at an undergraduate or graduate level)Yes, a little (e.g. online classes, books, workshops)No
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" + ], + "text/plain": [ + "Have you had formal training in programming? Yes, a lot (e.g. CS courses at an undergraduate or graduate level) \\\n", + "How would you describe the stage of your career? \n", + "Faculty, Staff Scientist, or Researcher 3.617658 \n", + "Postdoc 8.849841 \n", + "Graduate or Undergraduate Student 7.806474 \n", + "Software or Instrument Developer 17.007534 \n", + "\n", + "Have you had formal training in programming? Yes, a little (e.g. online classes, books, workshops) \\\n", + "How would you describe the stage of your career? \n", + "Faculty, Staff Scientist, or Researcher 4.754534 \n", + "Postdoc 9.804061 \n", + "Graduate or Undergraduate Student 5.952381 \n", + "Software or Instrument Developer 12.856487 \n", + "\n", + "Have you had formal training in programming? No \n", + "How would you describe the stage of your career? \n", + "Faculty, Staff Scientist, or Researcher 3.617658 \n", + "Postdoc 3.773585 \n", + "Graduate or Undergraduate Student 4.761905 \n", + "Software or Instrument Developer NaN " + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "question_7_percent_err = (np.sqrt(question_7_numbers)) / total_numbers.values*100.\n", + "question_7_percent_err" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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lPYaKigq6d++OM2fOcGsjpaam4vr169yolfeN988//0R+fj68vLx4a5B8/fXXcHR0xNWrV3lTciwsLKCtrc197tixI6ysrPD777/zRhbp6+ujRYsW3Of27dsDADeKTHIMAFz+p06dQllZGezs7Hjn0KJFC3z//ff4/fffebHb2NigcePG3GfJQtFV1d+K0tPTMW7cOKioqGDjxo28eych/2X0tiBCCCGkDmhra8PExERqu2RthfKUlJSQmJiII0eO4P79+3j06BEyMjIAAG3atOHS9erVC6GhoThx4gREIhH++usvPH/+HH379gXwbgoP8G7o+bZt22TG9ezZs0pjtrOzQ+PGjXHixAl069YN9+7dw61bt/Dzzz8DANLS0gD8vyOgvI4dOwIAnj59Wmn+NaGmpsZ7wAXePfRLygWQr9wkMbdr107qGB06dJAa3l+dmpaBmpoaFBWlf+tSU1ND/fr//zNNcq4aGhq8dPXq1eOtpaKkpITff/8dZ86cQWpqKh4+fIicnBwAqNGaK1WRFbO8dbQyFV+xrKysLLV+jrz7AeDedPLgwQNYWlpK7dehQ4dq867pMauKt1evXjh16hSOHTuGY8eOoWXLlrCxsYGTkxO6du0K4P9tv7K6c/78eWRlZaFly5ZyxamhoQFtbW1cvHgR3t7eSEhIQJcuXWBlZYWlS5ciKSkJenp6SElJ4U2TUlZWxuHDh3H+/Hk8ePAADx8+5DpVKtahimXx6NEjAMDUqVMrjSszMxOamppS2ytri2pqarx6X5Nj9OvXD0eOHMGff/4Ja2trHDt2DPXq1cMPP/xQq3gl10pWPSrfziX7Sab7lde+fXvExsYiOzubK8eK7VtyDyhfzpJ7geRaSM7B1dVVZvwV10N5n/pb0cqVK/HmzRvs3r1b5r2TkP8q6lwhhBBCPnOhoaHYvn07unTpAj09PfTv3x/6+voIDQ3ldYY0a9YMVlZWOHHiBHx9fXHs2DE0adIE3bp1A/D/B043NzfY29vLPJashwCJRo0awd7eHmfOnEFRURGOHTsGJSUl9OjRA0DVD++SP9yrW/hQ3lfJyuqQqEiecpP8Ql5+8dSKMddETcugYgeRRPmOlfLK/6Iv69g+Pj6IjY2FoaEh9PX14eLiAiMjI3h4eMgTvlxkxSxvHa2MPNdTlqrKAwBKSkp4IwskGjRo8F7HA94vViUlJaxYsQK3bt3CqVOncO7cOezfvx979+7F9OnTMWbMmA/SfiqysbFBdHQ0iouLkZCQAEtLSwiFQjRt2hR//fUX3r59i9LSUtjY2AAACgsLMWzYMNy4cQMmJiYwMzODp6cnjI2NuTVZyqtYFyRxhoaGciMsKmrWrJnM7ZJrWVhYKPVd+bZYk2NYWFhATU0Nx48fh7W1NY4fPw4zMzOuE6M28VZGch3LXytZ101yrytfn96n3UvOISoqijeKqDLv29bKi4uLg52dHbp06VLrvAj5klDnCiGEEPIZS0tLw/bt29G/f39uwVQJWdMm+vXrh6lTp+LGjRv47bff0KNHD+7hUjKCoF69erxpEsC7Nwg9efIEjRo1qjKevn374vDhw0hMTMSZM2dgZWXFPXxI8q+4SCMAbmFIyfQIRUVFqTf2VHZO70PecmvTpg0UFBTw8OFDqTxkjSKqTk3K4EO7fPkyYmNj4ePjg8mTJ3PbJdNk2rZt+1GOW9M6+im1bdtW5qKksq73x/T06VM8ffoUXbt2hVAoxI8//oj09HR4eHhg48aNGDNmDPdwf//+fejq6vL2T01NRePGjWv8oG9tbY3IyEj88ccfuH37NqZNmwZFRUUYGhoiMTERGRkZ6NSpE3fs48eP459//sG8efMwaNAgLp/nz5/LdTxJ/VdXV5e6xyQkJKCsrExmZxcArn4+ePCAm6oCvFuUuPwboGpyDCUlJfTu3RsnTpzA7du3cefOHYwePbrW8ZZv5+VjlWwD+O1cMrqkvIcPH0JNTQ1qamoyy0NeklhatWrFTVGTiIuL+yhTdjIyMmSOsCLkv47WXCGEEEI+Y5IpHRVHlMTFxeHBgwdS6wHY2dlBRUUFERERePnyJbe2AABoampCJBLhwIEDvIel4uJiBAUFYdKkSdWu6WBhYQF1dXXs2bMHN27c4KYcAUDLli0hEolw+PBhpKenc9uLiooQHR0NZWVlWFhYAABatGiBjIwMXhz//PPPB3volbfc1NXVYWRkhMOHD/M6Av7++2+Zr7WuTk3K4EOTrDNS8Zx3796N/Pz8Gq3XAfz/F+7qphPVtI5+Sg4ODrh+/TqSk5O5bUVFRVKvFf7Y1qxZA09PT159/+qrr6CpqcmVs2SE2fr163llfu3aNcTHx8PGxqbKEQyKiopSo63EYjHU1NSwevVqKCgowNDQEABgYmKClJQUnDt3jhu1AlRehyRv4aruWpqbm6NBgwbYsGEDt84J8K5zxsfHB2FhYZWeg7m5ORo3bowtW7bwjlPxDVs1PUa/fv2QlZWFZcuWoVGjRrz1S943Xsl+0dHRvE7i9PR0/PrrrxCLxbwpPmfPnuWmPQHA7du3cf78eW7UX21I6s3atWt59ebGjRsYP348tmzZUuM8ZdWl8s6dO4cxY8bUPFhCvnA0coUQQgj5jHXq1AmtW7fGmjVrUFhYiK+++gopKSk4cOAAGjRoILXAZMOGDdGjRw8cOHAAmpqaUuu6BAcHw8PDA87Ozhg6dCjU1NRw9OhRXLlyBdOnT5d61WZF9evXR+/evbFjxw40btyYt6hs+fwHDRqEoUOHQkVFBYcPH8a1a9cQHByMpk2bAng3AubIkSPw9vbG0KFDkZGRgW3btqF9+/a8h5xPUW7+/v5wc3PDkCFD4Obmhvz8fGzevLnasqiMvGXwoenr60NVVRULFixAWloamjVrhoSEBBw7dkxmXamOZG2GDRs2wNramreYbnk1raOf0siRI3Ho0CF4eXlhxIgRUFdXx6FDh7jRBdVNK/pQ3NzccOjQIbi5ucHFxQXNmjXDxYsXcenSJUyaNAkA8N1338Hd3R3btm2Dl5cX7O3t8fLlS2zbtg1NmzbF9OnTqzyGuro6EhMTsWnTJhgaGkJXVxeKioqwtLTEkSNHoK2tjSZNmgAAjIyMUFxcjOfPn/Om+5ibm6N+/frw8/ODm5sb6tevj9jYWJw/fx5KSkrVXkt1dXVMmzYNCxYsgIuLCxwdHVFSUoKYmBgUFhbC39+/0n1VVVUxY8YMzJkzBx4eHujduzfu3LmDw4cP80bU1fQYBgYGaNOmDWJjY9GnTx+oqKjUOt7mzZtz+w0dOhT9+vXDmzdvsHPnTpSVlSE4OJiXXkFBAUOHDoW7uzuKi4uxZcsWqKurY+LEiVWWpzwEAgFXb7Kzs2Fvb4/s7Gxs374dKioqvFFs8pJVlyTevn2L+Ph4tGvXDvr6+rWOn5AvCY1cIYQQQj5jysrKWLduHfT19bF161YsWrQI165dQ1BQEHx9fZGXl8e9fURCMlqlT58+UvPr9fX1sXPnTohEIu7tRPn5+Vi4cKHcv0RK8rezs5OaRiTJX1tbG5s2bUJERAQaNGiAyMhI3ls/unXrhpCQEBQWFmLevHk4efIkZs+e/cFGddSk3EQiEbZt24a2bdti1apV2LNnD3788UeZC6HKQ94y+NBatGiBdevWoW3btoiKisKyZcvw9OlTLFu2DMOGDcPdu3drNE2nT58+MDc3x/79+xEWFlZpuvepo59Ks2bNsH37dlhYWGDbtm1YsWIFhEIhpkyZwsX+KQiFQkRHR+Obb77Bpk2bEBoaijt37mDWrFm8N/PMnDkTISEhyMjIwMKFC7F37144ODhg//791U7rGj16NNq3b49ly5Zh37593HbJW6ckC+cC795w1KRJEzRp0gQGBgbcdoFAgBUrVkBFRQXLli1DZGQkiouLER0djW7duiEpKanazk9PT08sX74c9evXR3h4ONavX4/27dtjy5YtMDY2rnLfYcOGISwsDLm5uVi0aBGSkpKwevVqqQ7JmhxDQUGBu2eVH2lX23g9PT0RHh4OBQUFLFu2DFu3boW+vj52794tNa2rd+/eGDJkCDZs2IDo6GiYmZlh165dMhf2fR8zZ87ETz/9hMzMTCxatAgxMTHo2rUrYmJiuAV2a6KyugS8W+DXz88Pu3bt+iCxE/IlUWAfaul4QgghhPwnXLlyBUOGDMG6det4UwoI+dxkZmaiWbNmUguvbtq0CYsWLcLp06c/2lo0hADvOtacnJywcOHCug6FEPKR0cgVQgghhNTIL7/8Ak1Nzfce2UHIp7Jo0SKYmZnx3gZVWlqKEydOQF1dXa7XRBNCCCHyoDVXCCGEECKX4OBgPH78GBcvXkRAQEClrxEm5HPh6OiIQ4cOYcSIEXB0dISCggJOnjyJK1eu4Oeff/4gr6UlhBBCABq5QgghhBA5ZWRkICUlBS4uLhgxYkRdh0NItSwsLLBu3To0aNAAK1asQFhYGIqKirBy5UoMHjy4rsMjhBDyBaE1VwghhBBCCCGEEEJqgUauEEIIIYQQQgghhNQCda4QQgghhBBCCCGE1AJ1rhBCCCGEEEIIIYTUAnWuEEIIIYQQQgghhNQCvYqZEPLZKiwsRGZmJl6/fo3S0tK6DocQQgghhBBSA/Xq1UOTJk2grq6OBg0a1HU4HxW9LYgQ8lkqLCzEo0eP0Lx5czRt2hRKSkpQUFCo67AIIYQQQgghcmCMobi4GLm5ucjKykK7du2+6A4W6lwhhHyWnj17BiUlJbRo0aKuQyGEEEIIIYTUwqtXr1BcXIxWrVrVdSgfDa25Qgj5LL1+/RpNmzat6zAIIYQQQgghtdS0aVO8fv26rsP4qKhzhRDyWSotLYWSklJdh0EIIYQQQgipJSUlpS9+DUXqXCGEfLZojRVCCCGEEEL+/f4Lf9dT5wohhJBPjpb7Ih8C1aP/JrrunwaVMyGE1Ax1rhBC/nUKCkrqOoT3iiEkJARCoRBxcXEyvz9z5gyEQiFWr15d2/A+iT179kAoFOLly5dy75Oeng5vb288f/682rRFRUXo27cvkpKSahPmB3fnzh0EBwfDzs4OYrEYdnZ2CAgIQGpqqlTax48fIzAwEDY2NhCJRDAzM8O4ceNw+fLlOoj8y1FUVIR58+bh2LFjdR3KR1FSUgKhUIh169bVdSjvzdraGiEhIR8838uXL2P8+PEfPN9/o/e5B8vjQ7avj1UPqhMeHg4dHR3us5+fH6Kjoz95HISQ/5b6dR0AIYTUVMOG9aGgMKdOY2Dspxrv4+fnh3PnzmH27Nk4cuQIVFRUuO9ev36N2bNnQywWY+zYsR8y1M9KfHw8zp07J1faiIgICIVCGBgYfOSo5Hf06FEEBgaiS5cumDhxIlq1aoWnT58iOjoagwYNQnR0NMRiMYB3HUlDhgxBu3bt4OvrCy0tLWRkZGDXrl1wd3fHqlWr0L179zo+o3+nFy9eYOvWrRCJRHUdCvnE9u7di3v37tV1GF+0L7F9TZ8+HY6OjrCxsUGHDh3qOhxCyBeKRq4QQsgnoqqqirlz5+Lp06cIDw/nfbd48WLk5ORg4cKFqFevXh1F+Pl4+vQpNm/ejHHjxtV1KJzU1FQEBQXBwcEBMTExcHJygqmpKQYOHIiYmBhoaGggICCAG0q/a9cuFBYWYvPmzejXrx+MjY3Ru3dvbNiwAQKBABEREXV8RoQQ8t+gpaWFH374AcuWLavrUAghXzDqXCGEkE/I2toaTk5O2LFjB65cuQIASExMxJ49ezBt2jR07NiRl37nzp3o3bs3RCIRunfvjvXr1/Pmwb969QrTpk2Dubk5xGIxBgwYgEOHDtU4rkePHmH69OmwtLSEtrY2zM3NERgYiNzc3BrlExsbCxcXF+jr68Pc3ByzZs1CdnY2gHdD2AMDAwEANjY2mDlzZqX5REdHo2PHjvjuu+9420+ePAknJyfo6OjAysoKS5YsQVFRUZUx5eTkICAgACYmJujatStCQkKwZMkSODg41Ojctm3bhrKyMgQFBUFRkf+/zyZNmiAgIAC9evXCmzdvAAAZGRlQUFCQWhm/fv368PX1xZAhQ6o8XlFREcLDw2FnZwddXV04Ojri+PHj3PRFDdQAACAASURBVPeMMRw4cAADBgyArq4ubGxssHjxYhQWFnJphg4dilGjRvHyjY+Ph1AoRHJyMoB3w+f79u2L8PBwGBkZwdbWFvn5+UhJSYG7uzsMDAxgYGAALy8vpKSk8PJKSEjA0KFDIRaLYWpqipCQEN5rFktLS7Fs2TLY2dlxdTg8PBwlJVVPqzt+/DhcXV2hr68PkUiEH374Abt27QIAPHz4kBvx4+fnV+V1fPXqFUJCQmBrawuRSAQTExNMmjQJz54945XRnDlzsHr1alhbW0NHRwfDhg3DtWvXKs23smk7FaciyJv3xYsXMWTIEOjq6qJXr164ePGi1DEzMzMxc+ZMmJmZQSwWY9iwYfj777+lYoqKisKAAQMgFouxadMmAMClS5fg4uICXV1d9OjRA0ePHoWdnR03BVFSJ3bt2gUbGxsYGhpy0/EkHYm6uroQi8UYOHAgTp06xYvtxo0b8PT0hJ6eHuzs7HD06FHe9xXrXPnyKV8/q7tevr6+OHDgAB49egShUMjd6/Lz87Fw4UJYWVlBR0cHTk5O+P333yu5ev/37Nkz+Pr6wsrKCrq6uhg+fDhvyt7Dhw8hFAqxZcsW9OzZE3p6epVOlSkqKsL8+fNhYWEBPT09TJkyBZs2bUKXLl145xsYGIjx48dDV1cXEyZMACDf/besrAyrVq2CjY0Nt29OTk6V5VlZ2b9v+6quvcujumvl5+cHW1tbqfVeAgICah1L3759cfr0aRr5RAj5aGhaECGEfGKBgYE4f/48QkNDERMTgzlz5sDIyAgeHh68dJGRkVi1ahU8PT1hYWGBK1euYPny5cjOzsaMGTMAvBvq/Pr1a8ydOxcqKio4cOAA/Pz80KZNG3Tt2lWueN68eQM3Nze0bt0as2fPhqqqKv766y9ERkZCRUUFwcHBcuWze/duzJo1C05OTpgwYQKePXuG5cuXIyUlBbt370b37t2RlpaGqKgoREVFSXWcSDDGcPToUQwbNoy3/eDBg/D398fAgQMxbdo0pKamIjw8HGlpaVi+fHmleY0bNw4PHjyAr68v1NXVsX79ely7dg1fffWVXOcl8ccff0BHRwcaGhoyv7ezs4OdnR332draGrt27cLgwYMxZMgQmJmZQSAQQFFREVZWVrCysqryeFOnTkV8fDwmTJgAbW1tnDhxAlOnToWKigqsra2xfPlyrFu3Dh4eHvD19cXt27excuVK3Lp1Cxs2bKjRqvypqalISEhAREQEcnNzUVxcDG9vb1hYWCAyMhIFBQVYvXo1Ro8ejdjYWKioqODixYsYNWoUrKyssGLFCrx69Qrh4eG4ffs2duzYgXr16mHNmjXYtWsXAgIC0KZNGyQnJ2P58uVo2LBhpetm/Pbbb5gyZQpGjhyJyZMno6CgANu3b0dISAhEIhG+++47rFq1Cj/++CMmTpxY6dSqsrIyjB49GoWFhfDz84OGhgZu3LiBiIgIFBUVYc2aNVzaX3/9Fd999x1++uknlJaWYuHChZg8eTJ+++03qY60mqou75SUFIwePRoWFhZYsWIFHj9+jOnTp/PyKCgogIeHB3Jycrh6vHPnTnh4eGDnzp3Q1tbm0kZFRWHGjBlo3bo1OnTogFu3bmHUqFEwNjZGREQE0tLS8NNPP6GgoEAq1qioKAQHByMvLw8ikQjR0dEICwvD5MmToauri+zsbKxbtw7Tpk3DmTNnoKmpiadPn8LNzQ2dOnXC0qVLkZubi4ULFyIjI6NG5STP9Zo0aRJyc3Nx69YtREREoF27dmCMwcfHB1evXsWkSZPQvn17HD16FOPHj8fq1avRrVs3mcd7/vw5nJ2d0axZM/j7+6Nx48bYvn07PD09ER0dDSMjIy7tqlWrMHPmTCgrK8PU1FRmfoGBgThz5gymTJmCb7/9Fjt37pQaoQgAhw8fhrOzM9asWQNFRUW5778LFizAzp07MWHCBIhEIhw7dqzS+15V3rd9ydPeqyPPterfvz8OHTqEpKQkGBoaAnjXcXXq1Cnu/5HvG4uBgQFatGiBo0ePYtKkSTUuO0IIqQ51rhBCyCfWrFkzzJ49GxMmTMDIkSPx9OlTrFmzhvcwnJOTg7Vr18Ld3R3+/v4AAEtLSzRq1AhLly7FiBEjoKWlhcuXL2PKlCmwt7cHABgbG0NDQwP168t/e7937x7atm2LpUuXolWrVgAAU1NTJCcn49KlS3LlIRmh0L17dyxcuJDb/t1332Ho0KE4cOAAXF1d0a5dOwBAly5dKu3cuH37NjIyMri1S4B3D15hYWGwt7fHggULAABWVlbQ0tLCpEmT4OXlBV1dXam8Lly4gKSkJGzYsIHrzDAxMXmvtU7S09NlHqMy9vb2CAoKQkREBFcmTZs2hZmZGYYOHQozM7NK971x4wZOnz6NOXPmwNXVFQBgZmaGx48fIyEhASKRCBs3bsTw4cMREBAA4F39aNmyJXx9fXH+/PlqO2/KKykpwcyZM7lRF5cvX0Z2djY8PT2569C+fXvs3bsXeXl5UFFRwdKlS7kFmCUdEJ07d4azszNOnjyJH374AYmJiRCLxXBycgLwrn42bNgQ6urqlcZy//59DB48mKv3ACAWi2Fubo7ExERoa2vj+++/BwC0bduW+++K0tPT0aRJE8yZM4e7biYmJnjw4AF+/fVXXlrGGDZu3IjGjRsDAHJzczFz5kzcu3ev0k5AeVWX99q1a6GlpYXIyEiu3TZt2pTrQAWA/fv3486dOzhw4AB3vtbW1hg4cCDCw8OxYcMGLq2ZmRnc3d25z9OnT0eLFi0QFRUFZWVlLn9fX1+pWN3d3XmjA9LS0uDt7Y0xY8Zw21q1aoXBgwfjypUrcHBwwObNmwEA69evR7NmzQAA7dq1k+ocrY4816tdu3ZQV1eHsrIy9PT0AABxcXGIj49HZGQkdx+0trZGTk4OFi9eXGnnysaNG/HmzRscOHAAWlpaAN6NqOvbty/CwsK4kRwA0K9fPwwYMKDS2FNTU3HkyBFee7WyskKfPn3w8OFDXtrGjRtj1qxZUFJSAgCkpKRUe//NysrCjh07MGbMGK5T0srKCunp6YiPj5e3iAG8f/uSp71X59y5c9VeKzMzM2hqauLYsWNc50pcXBzy8vLg6OhYq1gUFBQgEomQkJBQozIjhBB50bQgQgipA/b29ujTpw8SExMREBCAr7/+mvd9UlISCgsL0a1bN5SUlHD/7OzsUFJSwk0bMDY2xvLlyzF58mTs3bsXmZmZ8Pf35x485CEWixETEwNNTU2kpqYiLi4OGzZsQGpqKoqLi+XK4+7du8jKykLfvn152w0MDNCmTRu5O2mAd2/YAcArk7t37+Lly5fc+Uv+WVtbo379+rhw4YLMvC5evIjGjRvzOhpUVVVhbW0tdzwS9erVQ1lZWY328fDwwPnz57Fy5UoMGzYMmpqaOHnyJDw9PWX+qi3x119/AQB69OjB275p0ybMmDEDycnJKC4ulirvH374AfXr169ReUt07tyZ999qamrw9vbGTz/9hNOnT0NLSwszZsyAlpYW8vLycPXqVdja2qKsrIy7Hp07d0arVq2462FiYoJz585h+PDh2LhxI+7du4cRI0ZIxV3euHHj8PPPP3PHOHr0KNavXw8A1U4BK69169bYtm0bxGIxHj9+jPPnz2Pr1q1c2VU8d0nnBwCu4y8/P1/u41WmurwvX74MGxsbXodor169eJ2tFy9eROvWrfHdd99xZc0Yg62tLS5dusSbZlX+Okr27datG9exIslf1oicih1VwcHBmDJlCnJycpCcnIxDhw5h586dAMCV4eXLl2FkZMR1rACAoaEhNDU15Syhd2pyvSqen5KSEqysrHj3hu7du+P+/ftIT0+XuZ8kbknHCvCujffp0wcpKSm8kT2VdeCVjwHgt1dFRUX07NlTKm2nTp24jhVAvvtvcnIySktLpTqFe/fuXWVcsrxP+5K3vVdHnmulqKiIvn374uTJk9z99ujRo9DV1UX79u1rHUubNm2QlpZWgxIjhBD50cgVQgipI5aWljh69KjMB33JOiWenp4y933x4gWAd2/UiYqKwokTJ3DixAkoKirC0tISc+fO5X4FlceGDRuwfv16ZGdno2XLlhCJRGjUqJHcnSuSuf8tWrSQ+q5FixY1mpefl5cHALwHUkl5BAUFISgoSGofSXlUlJmZiebNm8uMqaZat26Np0+fVvp9QUEB8vPzpY7XuHFj9OjRg3vwkiyMu3btWjg5OaF9+/ZSeWVnZ0NRUVFm7EDl5V2vXj2oqanVeB0EZWVl3gOfqqoqYmJiEBUVhWPHjuGXX35Bo0aN4OTkhMDAQOTk5IAxhsjISERGRkrlJ7keY8eOhaqqKvbt24clS5Zg8eLFEAgEmDVrFoyNjWXGkpmZiVmzZuHs2bNQVFTEN998895vjDp48CDCw8ORnp4ONTU1dOnSBQ0bNpRaz6Fhw4a8z5KOh5p2pslSXd65ublSI3mUlZV5nRVZWVlIS0vjTf8pLycnh0tf/i1kkn0r5q+kpMTLX6J8mwOABw8eICQkBAkJCVBWVkaHDh24kTySMszJyYFQKJTKq2XLljJjrYq816u87OxsFBcX80a6lffixQuZo+Sys7Nlxq2hoYGysjJu7SRAulwqyszMlNleZZWBrLyqu/9K2nvF6/g+Zfw+7Uve9l4dea9V//79sWnTJiQkJEBXVxe///47N5KrtrE0atSoxvdHQgiRF3WuEELIZ6hJkyYA3nWetGnTRup7ya+tTZs2hb+/P/z9/XHv3j2cOXMGq1evxs8//yzzD09ZDh48iCVLliAwMBCOjo7cH/A//vgj7t69K1ceTZs2BfBuQcqKXr58KbMDoTKSB5Tc3FzuoUhSHsHBwTJH5VQ2zURLSwuZmZlS22Vtq46lpSV27NiBzMxMmcc7cuQIgoODsW3bNujp6cHBwQGDBw/mFq2U+PbbbxEUFIRBgwbh3r17MstGVVUVZWVlUg/Gt27dQmFhIa+8y9ePkpISZGVlcWWooKAg1UHw9u1buc63Y8eOCAsLQ2lpKa5cuYKDBw8iJiYG7du356b5jBkzRmp0jSR+4F1Hgru7O9zd3fHq1SvExcUhKioKEydORHx8vMy1EaZMmYInT55g69at0NXVhbKyMvLy8rBnzx654pZISEhAQEAARo4cCQ8PD67NLFiwoMrFauUhGVVScbFiecu2PDU1Nal2U1paynUyAu/qv0AgwPz582XmIakPsmhqakqtf1JSUiK1GGpFpaWlGDNmDFRVVbF//34IhULUr18fN2/e5E2rat68ucx2L+kQBaouL0lnw/teL1VVVaipqfGmRpVX2Wt3mzVrhpcvX0ptf/nyJRQUFNCsWTPeNaiKlpaWzPYqz7oz8tx/Je05IyOD197LlzEgX3t/n/Yluf9W196rI++16ty5M4RCIY4fP46MjAyUlJRwo3RqG0tubm6lndaEEFJbNC2IEEI+Q3p6elBSUsKrV6+go6PD/SssLMTy5cvx6tUrpKWlwdramntzR8eOHTFmzBiYmpry3oZSnb/++gstW7aEp6cn94d9Xl4ekpKSqvzFuLxOnTqhefPmOHLkCG97UlISnj59ys2dl2dx0NatWwMAbzh/p06doKamhqdPn/LKo1mzZli6dClSU1Nl5mVkZIT8/HzeugQFBQU4f/68XOdV3vDhw6GoqIgFCxZIPcDk5ORg3bp1aNOmDQwNDaGkpAQNDQ3s3btX5kNsamoqFBQUKl3PQ1JesbGxvO2hoaGIiIiAvr4+lJSUpMr7+PHjKC0t5fZXVVWVqguSKUdVOX78OMzMzJCZmYl69erBwMAAc+fORePGjZGeno6mTZtCKBTi4cOHvOvxzTffcIsYA4Crqyu3Rk6LFi3g7OyMYcOGITs7W+aCqsC7OtOnTx8YGRlxU1nOnTsH4P+jPeRZPFPyJp3JkydzD+olJSWIj4+Xu15Xpl69emjUqJFU2UresFMTZmZmiI2N5U3JOHfuHG+qj7GxMZ48eYKvvvqKV96nT59GTExMlWssde3aFXFxcbz8YmNjqx2V8+rVKzx8+BBDhgyBtrY2d4yK18LMzAyJiYm8joSbN2/yRnlJRtOUb9NZWVm4f/8+91ne61XxHmJsbIzs7Gw0aNCAVzZ///031q5dW+k9x8jICImJiXj+/Dm3rbS0FCdOnICenl6N1q0yNDSEoqIizpw5w21jjPE+V0ae+6+BgQGUlZV5bwsDpO8P8rT392lf8rb36tTkWvXv3x+///47jh8/DktLS65sahtLenp6jUZ1EkJITdDIFUII+Qy1aNECI0aMQFhYGHJycmBgYIC0tDSEh4dDTU0NnTp1grKyMlq1aoXQ0FC8fv0aX3/9Na5evYrz58/z3oSQnJwMDQ0NtG3bVuaxxGIxdu/ejaVLl8LKygrPnj3Dxo0buQdredSvXx9TpkzBTz/9hMDAQPzwww949uwZIiIi0KlTJ/Tv3x/A/39hP3nyJKysrGT+qtypUydoaWnhr7/+4qZMKSkpYdKkSZg3bx6Ad6NIsrOzsWLFCrx584ZbE6GoqAjXr19Hq1atoKWlBXNzc3Tt2hUzZszAtGnToKGhgejoaGRmZvJ+4UxPT0d6ejq6dOnCW5uivHbt2iE4OBizZ89Geno6Bg0aBC0tLdy/fx+bNm1CRkYGtm/fzj0gBAcHw8vLC05OThgxYgQ6d+6MsrIyXLp0CVu2bIG7uzu3wG9FOjo6sLOzw7x585CXlweBQICTJ08iKSkJmzZtgrq6Ory8vLBhwwbUq1cPVlZWuHPnDiIiImBqagpzc3MAgK2tLebMmYPFixfDxsYGiYmJUou5ymJoaIjS0lL4+Phg9OjRUFFRwZEjR1BQUMAtRDl16lT4+PggMDAQvXv35t7ocv/+fW7qlpGREaKjo6GhoQFdXV08e/YMmzdvhqWlpdT0lfLnfuDAAQgEAmhqauLy5ctYv349FBQUuHVKJL9e//nnn/j2229lTjMQi8VgjGHOnDlwdHREdnY2tm/fjjt37oAxhuLiYt5UqJqytbXFoUOHIBKJ8PXXX2Pfvn3vtZbDhAkTMGjQIIwePRpeXl549eoVIiIieA/3gwYNwo4dO+Dl5YUxY8ZAU1MTZ86cwdatWzF58uQq3ww1fvx4DBgwAOPHj8fw4cPx/Plzbr2fqjo7NTU10apVK2zZsgUaGhpQVVXFuXPnsG3bNgD/XzPGy8sL+/fvx8iRI/Hjjz+isLAQ4eHhvHbUpUsXaGpqIiIiAg0aNADw7s1E5euAvNeradOmePnyJc6dO4fvv/8ednZ20NPTw9ixYzF+/Hh88803uHz5MlavXo0BAwagUaNGMs9v5MiROHz4MDw9PfHjjz9ybwt68OABNm7cWN1l4/n222/Rr18/zJ8/H/n5+fj222+xb98+3L59u9p7qDz33yZNmmDcuHGIjIyEsrIyjIyMEBsbi7i4OF5e8rT3921f8rT36tTkWkkWFo6NjUVYWBgvn/eNhTGG5ORkjB49Wq54CSGkxhghhHyGrl+/XuX3wOw6/fch7Nu3jwkEAvbs2TOZ35eVlbHo6GjWs2dPpq2tzSwsLFhQUBB7+fIll+bFixfM39+fWVhYMG1tbebg4MDWrl3LysrKGGOMFRcXM4FAwIKCgiqNo6ysjIWHhzNra2umo6PD7O3t2bx581hMTAwTCATs0aNHMvfbvXs3EwgE7MWLF9y2w4cPs379+nHxzpo1i2VlZXHf5+XlMQ8PD6atrc18fHwqjSk0NJQ5OTlJbT906BDr378/E4lEzNTUlE2aNIk9ePCA+/7BgwdMIBCwyMhIbltGRgabNm0aMzAwYIaGhiw0NJRNmDCBDRgwgEuzbNmyKq9FeQkJCczHx4dZW1szkUjE7OzsWEBAAHv48KFU2vv377OAgADWrVs3pqOjw/T09NiQIUPY3r17uWtUmYKCArZw4UJmaWnJxGIxGzhwIIuLi+O+LysrY5s3b2Y9evRg2trazNbWli1ZsoTl5+dzaYqLi9miRYuYmZkZE4vFzNvbmyUmJjKBQMD+/vtv7txFIpHU8a9cucK8vLyYsbEx09HRYc7OzuzMmTO8NHFxcczFxYXp6OgwIyMjNnr0aPbPP//wjh8REcHs7e2ZSCRi5ubmbNasWSwnJ6fS83706BHz9vZmhoaGzNDQkDk7O7MjR44wT09PNmzYMC7dvHnzmJ6eHjMxMWGlpaUy89q6dSvr3r07E4lEzNbWlgUEBLCTJ08ygUDA4uPjGWOMubq6spEjR/L2u3DhAq+MZHnx4gWbOHEi09PTY0ZGRmz27Nnsl19+4ZWlvHmnpKSw4cOHM7FYzOzs7NjRo0eZsbExW7t2LZfm+fPnzM/Pj5mYmDAdHR3Wp08ftn37dl5ZCwQC3j4S8fHxzMnJiWlra7MePXqwX3/9lQkEArZly5Yqz/fatWvMzc2N6enpMWNjY+bm5sbOnz/PHBwc2IwZM7h0Dx48YGPGjGF6enrMwsKCbdmyhTk5ObFZs2ZxaZKTk5mLiwt3LbZs2cKCgoJ45SPP9bp58yZ3T4yOjmaMMfb69WsWGhrKLC0tuftgZGQkKy4urvT6McZYamoq8/HxYQYGBkxPT4+5u7uzS5cu8c5LIBCwI0eOVJkPY4y9ffuWzZ49mxkbGzM9PT02ffp0FhISwoyMjLg0supDTe6/mzdvZnZ2dkwkEjEPDw+2fft23j1YnvZem/ZVXXuXxcrKilcPanKtvLy8mIGBASsoKJD6rrpYZN3XkpKSmFAoZPfv368yZkLIx1Pd3/f/dgqM1XJsLCGEfAQ3btyo9A0NBQUlaNiwbgfefQ4xyOv8+fM4e/YsQkJC6joUuaWlpaFnz5745ZdfIBKJ3jufx48f4+rVq7C3t+f9ku7k5IR27dohIiLiQ4RLyGcrPj4ejRs35q1VdPPmTfTv3x/r1q2DjY1NHUb3ZcjMzMSFCxdga2vLjfoA3q2bkp6ejr1799ZhdEQiMDAQb968wYoVK+o6FEL+s6r6+/5L8O94MiCEkHI+h06NzyEGeZSWlmLDhg3w8PCo61BqpE2bNhg2bBjWr19f6w4QPz8/9O3bF46OjmCM4ejRo7h586bcQ9kJ+TdLTk7G+vXrMWPGDHz33Xd48eIFoqKi0KlTJ5iZmdV1eF+Ehg0bYs6cOThy5Ajc3NygpKSEP/74A6dPn8aiRYvqOjwC4NmzZzh16hR++eWXug6FEPIFo5ErhJDP0pfes/0pXb9+HV26dKnrMGqsoKAATk5O+Pnnn7kFWt/HuXPnEBUVhdu3b6OsrAzff/89JkyYAAsLiw8YLSGfp5KSEkRGRuLIkSPcYsTW1tbw9fWFhoZGXYf3xUhJSUFERASuXr2KwsJCdOzYEaNGjUKfPn3qOjQCwNfXF507d6b1VgipY1/63/fUuUII+Sx96TdfQgghhBBC/ku+9L/v6VXMhBBCCCGEEEIIIbVAnSuEEEIIIYQQQgghtUCdK4QQQgghhBBCCCG1QJ0rhBBCCCGEEEIIIbVAnSuEEEIIIYQQQgghtUCdK4QQQgghhBBCCCG1QJ0rhBBCPhrGmFzbCCGEEEII+TejzhVCCPlEQkJCIBQKERcXJ/P7M2fOQCgUYvXq1Z84svezZ88eCIVCvHz5EgDg6+uLXr16cd+fPn0agYGB3OeioiLMmzcPx44d47YNHToUo0aN+iDxMMbg6emJI0eOfJD8PhfVlXNd+pxi+Rgqlv3HVFJSAqFQiHXr1n30Y30q4eHh0NHRqVUe1tbWCAkJ+UAR1U5WVha6deuGtLS0ug6FEELIZ4g6Vwgh/zrFxWV1HcJ7xeDn54dWrVph9uzZePPmDe+7169fY/bs2RCLxRg7duyHCvOTmjRpEpYvX8593rJlC9LT07nPL168wNatW1FSUvJRjr9z504UFBSgb9++HyX/z0XFciaEfBrNmzeHh4cHZs6cSSPwCCGESKlf1wEQQkhNKSkpwtdX9uiPTyUszKbG+6iqqmLu3Lnw9vZGeHg4goODue8WL16MnJwcbN68GfXq1fuQoX4y7dq1q7Njv337FhEREVi0aFGdxfCp1GU5E/JfN2zYMERFReHs2bPo3r17XYdDCCHkM0IjVwgh5BOytraGk5MTduzYgStXrgAAEhMTsWfPHkybNg0dO3bkpd+5cyd69+4NkUiE7t27Y/369bxfTF+9eoVp06bB3NwcYrEYAwYMwKFDh2oc16NHjzB9+nRYWlpCW1sb5ubmCAwMRG5urtx5lJ8iMnToUFy6dAl//vknhEIhLl++zD2I+Pn5wcHBQWYepaWlWL16Nbp37w6RSITevXtj37591R579+7dAABLS0ve9oSEBAwdOhRisRimpqYICQnB69evq8yrqKgI8+fPh4WFBfT09DBlyhRs2rQJXbp0qTaOimJjY+Hi4gJ9fX2Ym5tj1qxZyM7O5r4PDw9H3759cfr0afTt2xcikQi9evWqcmpTxak41tbWiIqKwvz582FmZgZdXV14e3vj8ePHtS4LANi/fz8cHR0hFothZ2eHVatWobS0VGbaN2/eYNGiRXBwcIBIJIKhoSFGjRqF27dvc2mqq7OlpaVYtmwZ7OzsuHofHh7OG/GUn5+PhQsXwsrKCjo6OnBycsLvv//OiyUlJQXu7u4wMDCAgYEBvLy8kJKSUu35ypKQkIC+fftCR0cHzs7OuHDhAu/7Z8+ewdfXF1ZWVtDV1cXw4cNx+fJlXpqsrCzMmTMHdnZ2EIvFGDhwIE6fPl3lcefNmweRSMSlq217z8zMxPfff4+YmBhu261btyAUChEaGspty8vLg0gkwsGDB+U6v4cPH0IoFGLLli3o2bMn9PT0eNP/JFJTU2Fubg53d3fk5+cDAA4dOoR+/fpBLBbDzMwMfn5+UtOwiouLufqtp6eH0aNHS9VvyCTo1gAAIABJREFUedpar169cPLkSTg4OEBPTw/u7u64fv06l0aeuqesrAwHB4cvavoWIYSQD4M6Vwgh5BMLDAyEhoYGQkNDUVRUhDlz5sDIyAgeHh68dJGRkZg7dy5sbW2xZs0aDBw4EMuXL0dYWBiXZvr06Xjw4AHmzp2LtWvXQiAQwM/PT+rBripv3ryBm5sbnjx5gtmzZ2Pjxo1wc3PDoUOHsGLFivc6x9DQUHTp0gU6OjrYtWsXhEIhVq1aBQCYOHFipfnOmjULUVFRcHZ2xpo1a2BtbY2goCDew6Asv/76K+zs7FC//v8HZF68eBEjR45Es2bNsGLFCvj6+uLMmTPw9vautHMAeHd9du/eDW9vb0RERKCgoADh4eE1LoPdu3dj3Lhx+PbbbxEREYHJkyfj9OnT8PDwQGFhIZcuLS0N8+fPh5eXF9auXYuvvvoKM2bMkHp4rMrGjRvx+PFjLFq0CHPmzEFKSgqCgoJqXRZbtmxBYGAgDA0NsXr1aowYMQJr1qxBRESEzPTTp0/Hr7/+Ch8fH2zatAn+/v64efMmfH19eWmqqrNr1qzBrl27MHHiRGzatAkuLi5Yv3491q9fD+Dd2jo+Pj7Yu3cvvL29ERkZCYFAgPHjxyM2NhYAkJubC29vb7Rs2RKRkZFYunQp8vLyMHr0aKkpefKYNWsWBg4ciFWrVkFNTQ1jxozhOmqeP38OZ2dnXLt2Df7+/ggPD4eysjI8PT2RmJgI4F0bc3V1xenTpzF+/HisXLkS7du3x4QJE/Drr7/KPOaKFSsQExODsLAw2Nvby1V21VFXV4eOjg4uXrzIbfvzzz8BgItVsq20tBTW1tZynZ/EqlWrMH78eMyfPx+mpqa8754+fQovLy988803WLt2LRo1aoRLly4hMDAQvXv3xoYNG+Dv748LFy7Az8+Pt+/Bgwfx6NEjLFmyBCEhIUhOTualkbetPX/+HCEhIRg1ahTCwsKQm5uLESNG4Pnz5wCqr3sSPXv2RHJyco3aKCGEkC8fTQsihJBPrFmzZpg9ezYmTJiAkSNH4unTp1izZg0UFBS4NDk5OVi7di3c3d3h7+8P4N2ojEaNGmHp0qUYMWIEtLS0cPnyZUyZMoV7+DI2NoaGhgavk6E69+7dQ9u2bbF06VK0atUKAGBqaork5GRcunTpvc6xU6dOUFVVRb169aCnpwcA+P777wEAbdu25f67vLt372Lfvn0ICgriOposLS1RUlKC5cuXw9nZGQ0aNJDaLzc3F9evX8egQYN425cuXcotEKyo+O63hM6dO8PZ2RknT57E/9i79/ie6///4/eNiRkbc0pkPmSYncjEhw3TyaGUyLEUMsNyyvmspJlTzBzmWEgUHUSlshHLKakP8SVTDiNmq7HZ1t6/P3bZ6+dtk/GavWe7XS8Xl0vv1/v1fr0er9f7+Zb3/f08tG3bNtuxTp06pc8//1xTpkxR165dJUktWrRQu3btdPr06Vxff9Yv4IGBgZoxY4ax/ZFHHlG3bt20adMm4/jXrl3T0qVL9eijj0rKHPbTpk0bRUdHq0ePHrk6n6urq8LDw43rPH36tBYuXKirV6+qdOnSd3UvsnoRtWvXTpMmTZKU+X4kJiZm+1ItZfYmSU1N1aRJk4yeSX5+fkpMTFRYWJgSExPl7Ox82za7b98+eXl56bnnnjOeL1mypMqXLy9Jio6O1u7duxUeHm4cw9/fX4mJiQoNDVWrVq10/PhxJSQkqHfv3vLy8pIkubm5aePGjUpKSlLp0qVzdV+zDBs2TL169ZIkNW3aVIGBgYqMjNS7776rZcuW6erVq9q0aZMqV64sSQoICFD79u0VFham9evXa+PGjTp9+rQ++ugjeXh4GPskJCQoNDRU7dq1szrfihUrtGjRIoWGhlr1UsqLz3tAQIBWr16tjIwM2dvbKyYmRh4eHjpy5IgSEhLk4uKinTt3ytvbW+XLl9f06dNve31ZOnTooI4dO2Y756VLl/TKK6+oUqVKWrp0qRwdHY3rcXR0VN++fVWiRAlJkouLi1VvEkmqVq2aFixYYFznqVOntHTpUqWmpqpYsWJ39FmbMWOGnnzySUmSl5eX2rRpo/fff1/Dhw+/bdvL0qBBA0mZPZqqV6+e63sPACjc6LkCADbQpk0btWvXTvv27dPo0aNVrVo1q+cPHjyo69evq1WrVkpPTzf+tG7dWunp6cYvz35+fpo7d65ef/11bdy4UfHx8Ro1apQRaOSGl5eX1q5dq0qVKunUqVOKiopSZGSkTp06pbS0tDy97n+TdU1Z13jjNScmJurnn3/O8XXnzp1TRkaG1T1MSkrSzz//rJYtWyojI8M4Vt26dfXggw9mG9Zxcw1PPPGEsc3e3t74MpZbJ06c0JUrV7JNrtuwYUM99NBDVqGVnZ2d1fuV9QX22rVruT6ft7e3EZpIUpUqVSRJKSkpd30vTp48qYSEhGxDuF5//XW9//772fYvVaqUli9frscff1xxcXGKiYnRunXrFB0dLUlGW7pdm23SpImio6PVs2dPLVu2TCdPntRLL71k3MuYmBg5ODioRYsWVu0kMDBQv/32m+Li4lS3bl25uLioX79+mjRpkrZv367KlSvrjTfeMO7vnbgx4ChRooRatGihAwcOSMoMCBo3bmx13GLFiqldu3Y6fPiwUlJStH//ftWsWdMIVrJ06NBBFy9eVGxsrLFt27ZtmjFjhpo3b56t/eTF5z0r1Dl69Kj++ecf7d+/X3369JG9vb3RA2bnzp1q2bJlrq8vS06hqcVi0auvvqrY2FiNHz9eTk5OVteTlJSkDh06aPbs2dq/f79atGih4OBgq2P4+PhYBUjVqlWTxWLR33//fUeftQceeMDqs12pUiX5+voa1327tpelXLlycnR0ZNUgAIAVeq4AgI00b95cW7Zskb+/f7bnsuYK6N27d46vvXjxoiRp3rx5ioiI0LZt27Rt2zbZ29urefPmmjp1qtELJTciIyO1dOlSJSQkqGLFimrQoIFKlSqVr+FK1jVn/Sp/s6xrvlnWvCGlSpUytiUmJspisSg8PFzh4eG5PlZ8fLzs7e1Vrlw5q+0VK1a8/QXcIDExUZJUoUKFbM9VqFDBaq4TBwcHqy+OWSHJnaxGUrJkSavHWcfIyMi463uR9X64urrmuo6oqCi9/fbbOnXqlJycnOTu7m68L1nXc7s2279/fzk5Oemjjz7SzJkzFRoaqjp16mjChAny8/NTQkKC0tLSjB4pOV1PlSpVtHbtWkVEROiLL77QBx98oFKlSum5557TmDFjjF4SuXXzPXB1dTXew4SEBLm7u+f4moyMDF29elUJCQk5toWs4yYlJRnbjhw5ooCAAEVHR+uHH35QkyZNjOfy4vPu4eGhihUrGkN/rl27Jn9/f9WrV0/79u2Tm5ubzp07Z4Qrubm+LFk9Um6U9XdIlSpVFBYWptWrVxvPPfroo1q8eLFWrVql5cuXa/HixapYsaIGDBhg1Wvrxs+2lL19S7n7rFWoUMGqh2DWdRw7dkySbtv2buTo6JirOYsAAEUH4QoAFEBlypSRlPll6qGHHsr2fNavyGXLltWoUaM0atQonTx5Ut98840WLlyoN998M8cv0jnZvHmzZs6cqTFjxuiZZ54xusAPGjRIJ06cyKMrur2sX7TXrl2b45ffW3W/zwpCbvyik3X/XnvtNatfqm8+180qV66sjIwMXblyxWoowOXLl3N5FZnKli0rKXM4xM3+/PNPubm53dHxzLjbe5H1uvj4eKvtFy9e1MmTJ9WoUSOr7adOndKgQYP09NNPa+nSpcb7tXr1au3atcvY73Zt1t7eXr169VKvXr106dIlRUVFKSIiQoMHD9bu3bvl5OQkFxcXRUZG5lj3f/7zH0lSrVq1FBYWpn/++Uc//fSTNm/erLVr18rNzS3b/Ea389dff8nFxcV4fOnSJSMYcXZ2zjYBq5T5PtvZ2cnZ2VnOzs46efJkjvtIsgrzevXqpdGjR6tTp06aOHGiPv30U2M4XF583u3s7OTv7689e/bIYrGobt26KlOmjJo0aaKYmBhVqVJFVatWVd26dXN9fTeGQzcrXry4Vq5cqf3792vw4MHauHGj1RC+gIAABQQE6Nq1a4qJidGqVas0depUNWzYMMeeMDe7k8/ajRPcZrl06ZLxWb9d27txJbfExMRsISwAoGhjWBAAFEA+Pj5ycHDQpUuX5Onpafy5fv265s6dq0uXLuns2bPy9/fX119/LSnzy+Rrr72mxx57TOfPn8/1uQ4cOKCKFSuqd+/expeMpKQkHTx48I56T9zsxmEqkm67xHTWL8N//fWX1TWfOXNG8+fPtxp+cKMqVarI3t5ecXFxxrayZcvK3d1dp0+ftjpWjRo1NHfu3FuuGtOoUSPZ29vrm2++MbZZLBarx7lRu3ZtlStXLtuqPwcPHtS5c+eyBRP30t3ei9q1a8vZ2dmYJDbLBx98oIEDB2bb/5dfflFqaqqCgoKsgrCsYUEZGRm5arNdu3bV22+/LSmzp0GnTp3UvXt3JSQkKCUlxei98sADD1hdz48//qjFixfL3t5eW7duVdOmTRUfH69ixYqpYcOGmjp1qhwdHa3aSW7t3LnT+O/k5GRFRUUZ7bVx48bat2+fMSmqlDlfzbZt24zhLI0bN9apU6f0v//9z+q4W7ZsUeXKla2GtFWsWFHFihXTlClTjLlzJOXZ513KDDQOHDigmJgYo2dMkyZNdPToUW3dulUBAf9/qfncXN+/sbe3V/ny5fXEE08oICBAoaGhRlj5zjvv6MUXX5SU2ROkdevWeuONNyQp1+/TnXzWrl69ajVf0IULF/Tjjz8ak+/eru1liY+PV1pamqpWrZqrGgEARQM9VwCgAKpQoYJeeuklYyLQhg0b6uzZs5ozZ45cXFxUu3ZtlShRQg8++KCmTZumv//+W9WqVdPPP/+sXbt2KSQkxDjWoUOH5OrqesueH15eXvrwww81a9YstWjRQufPn9eyZcuML6Z3q2zZsvrpp58UExOj+vXrGz0h9uzZo5o1a2Yb1lG/fn099dRTGj16tAYMGKC6devq+PHjmjNnjry9vY15RG7m5OQkb29vHThwQN26dTO2Dx06VMHBwcZqJKmpqVq0aJF+++03q5V0brw/NWvWVIcOHTR9+nQlJyerZs2a+uijj3T8+HGrexEfH6/ff//dmLj3ZsWLF9eQIUM0adIkjRkzRm3bttX58+c1b9481a5dW88+++xd39e7kdt7cSMHBwcFBwdrxowZcnFxUUBAgI4dO6bIyEj1798/W++i+vXrq1ixYgoNDdVLL72klJQUffTRR0avleTkZLm5ud22zTZu3FgrVqyQq6urvL29df78ea1cuVLNmzdX6dKl1bp1a/n4+Kh///4aMGCAatSoof3792vhwoXq2LGjSpUqpUaNGumff/5RcHCw+vbtq9KlS+vzzz9XSkqKMewsKSlJJ06c0MMPP5xtwtKbzZw5U6mpqXJ1dVVkZKSSk5MVFBQkSXr11Vf16aefqnfv3ho0aJAcHR31/vvvKzY2VsuWLZMkderUSWvXrlVQUJBef/11VapUSZ988om+//57zZgxI9tQFSlzHp0uXbpo2bJlateunerUqZMnn3dJ+u9//6v09HTt2rVLPXv2lPT/g8WffvrJKjzLzfXl1oQJE9S+fXu99dZbmj17th577DGtWLFCY8aMUfv27ZWamqolS5bI1dVVjRs3ztUx7/SzNmLECA0bNkyOjo6aP3++nJ2djXtwu7aX5eDBg7Kzs1OzZs3u6PoBAIUb4QoAFFBvvPGGKlWqpA8++EARERFycXFRy5YtNXToUOOL7YIFCzRr1izNnj1bCQkJqlq1qkJCQtSvXz9JUnp6ul588UW98MILeuutt3I8zwsvvKCzZ89q06ZNWrVqlSpXrqxWrVqpW7dumjx5sv7444+7WhGje/fu+vnnn9W3b1/NmjVLTz75pF5++WVt2LBBO3bs0O7du7O9ZubMmYqIiNDq1at18eJFVaxYUS+++KIGDx78r+d66qmntHTpUqWnpxu/pLdq1UqLFy/WwoULNWjQIJUsWVLe3t567733VKtWrVvenylTpqh06dIKDw9XamqqAgMD1aVLF23dutU43zfffKPx48drzZo1xio/N+vatatKly6tpUuX6rPPPpOLi4sCAwM1bNiwbHOk3Gu5uRc56d27txwdHbVixQqtWbNGVatW1bBhw3IcVpM1DGfBggUKCgqSi4uLfHx8tGrVKr300kvav3+/3NzcbttmX3/9dTk4OGjDhg2aP3++ypYtq8DAQGM552LFimnZsmWaO3eu5s+frytXrqhq1aoaOHCgXnvtNUmZE5VGRkZq7ty5GjdunJKTk1WnTh2Fh4cbPRkOHz6sV155RaGhobcNu6ZNm6bp06fr3LlzatCggVavXq2aNWtKyuw59cEHHxjLBGdkZMjT01OrVq0yAgInJye9//77CgsL08yZM5WSkiJ3d3erFY9yMnz4cG3fvl0TJkzQunXr8uTznlVPo0aNtG/fPqP9Ojk5ycPDQ8ePH7daRjk315db1atXV1BQkObOnatnn31WAQEBCgsL07Jly4w5ZB599FGtXr36lsPVcpLbz1qJEiU0cuRIzZw5U4mJiWratKnCw8Pl7Ows6fZtL0t0dLR8fHzuanJkAEDhZWcx0+cbAO6Ro0eP3nK8fVpahhwcbDuqsSDUkFu7du3St99+q4kTJ9q6lHsmKSlJgYGBmjp16h2v7HOj+Ph4ff/992rZsqXR00bKnH8mLi5OGzduzItyUUDMnj3b6DFVWBSFz/vdmDNnjpYvX37LVcdy6+rVq2rRooVmzZqlVq1a5VF1AFA0/Nu/7wuD++ObAQDcoCCEGgWhhtz4559/FBkZqRYtWti6lHvKyclJwcHBioyMNDVPTMmSJTVlyhSNGDFC0dHR2rNnj0JDQ7V9+3b16tUrDyuGrV24cEHbt29Xw4YNbV1Knikqn3dbWrdunerVq0ewAgDI5v74dgAAuCvFihXTyJEji8QXgV69eqlkyZL67LPP7voYjo6OWr58udLT0zVixAgFBQUpJiZGs2bNyvd5UnBvOTs7a8GCBapUqZKtS8kzRenzbgvx8fF67733NGPGDFuXAgAogBgWBKBAKuzdBgEAAICipLD/+56eKwAAAAAAACYQrgAAAAAAAJhAuAIAAAAAAGAC4QoAAAAAAIAJhCsAAAAAAAAmEK4AAAAAAACYQLgCAAAAAABgAuEKAAAAAACACYQrAO47FoutK7j7Gnr16iUPDw8dPXo0x+fr16+v+fPnm6gMAAAAQH4rbusCAOBO2dlJ587ZtoaqVe/+tenp6Ro7dqw2bNig4sX5axgAAAC439FzBQDyWZkyZXTkyBEtXbrU1qUAAAAAyAOEKwCQzxo0aKB27dpp4cKFOnny5C33S0lJ0bx58/Tkk0/K09NTbdu21fr16/OxUgAAAAC5QbgCADYwfvx4lS5dWmPHjlVGRka25y0Wi/r166dVq1apW7duioiIULNmzTRp0iSFh4fboGIAAAAAt0K4AgA2UL58eU2YMEGHDh3SqlWrsj0fFRWlvXv3avr06erdu7eaN2+u8ePHq2vXrlq0aJGuXLlig6oBAAAA5IRwBQBspF27dmrdurXmzZun33//3eq5ffv2ycHBQU888YTV9g4dOig1NVU//fRTfpYKAAAA4F8QrgCADU2ePFkODg4aN26cLDes75yYmChXV1fZ21v/NV2hQgVJ0t9//52vdQIAAAC4NcIVALChypUra9SoUdq7d6/WrVtnbC9btqwuX76cbT6WP//8U5JUrly5fK0TAAAAwK0RrgCAjb3wwgv673//q7CwMCNM8fPzU1pamr766iurfT///HM5ODjIy8vLFqUCAAAAyEFxWxcAAJCmTZum9u3bG0OD/P391bhxY40bN05xcXF65JFHFBUVpQ8++EBBQUEqW7asjSsGAAAAkIWeKwBQADz00EMaPny48dje3l6LFy/W888/r8jISAUFBWnPnj2aMmWKhgwZYsNKAQAAANzMznLjDIoAUEAcPXpU9erVy/E5i0Wys8vnggpgDQAAAMD94t/+fV8Y0HMFwH2nIIQaBaEGAAAAAAUD4QoAAAAAAIAJhCsAAAAAAAAmEK4AAAAAAACYQLgCAAAAAABgAuEKgAKLxcwAAACA+19R+Hc94QqAAqlEiRJKTk62dRkAAAAATEpOTtYDDzxg6zLuKcIVAAVShQoVdObMGcXHxystLa1IpN0AAABAYWGxWJSWlqb4+HidOXNGrq6uti7pnrKz8I0FQAGVkpKiP//8UykpKUpPT7d1OQAAAADuQPHixVWyZElVrFhRJUuWtHU59xThCgAAAAAAgAkMCwIAAAAAADCBcAUAAAAAAMAEwhUAAAAAAAATCFcAAAAAAABMIFwBAAAAAAAwgXAFAAAAAADABMIVAAXSr7/+ausSUEQlJyfbugQUYbQ/2AptD7ZC24Ot5HXbI1wBAOAGFovF1iWgCKP9wVZoe7AV2h5sJa/bHuEKAAAAAACACYQrAAAAAAAAJhCuAAAAAAAAmEC4AgAAAAAAYALhCgAAAAAAgAmEKwAAAAAAACYQrgAAAAAAAJhAuAIAAAAAAGAC4QoAAAAAAIAJhCsAAAAAAAAmEK4AAAAAAACYQLgCAAAAAABgAuEKAAAAAACACYQrAAAAAAAAJhCuAAAAAAAAmEC4AgAAAAAAYALhCgAAAAAAgAmEKwAAAAAAACYQrgAAAAAAAJhAuAIAAAAAAGAC4QoAAAAAAIAJhCsAAAAAAAAmEK4AAHADR0dHW5dwz6SkpNu6BAAAgEKpuK0LAICc2NnZyc5uiq3LAAoVi2WSrUsAAAAolOi5AgAAAAAAYALhCgAANtC7t5utS8gXsbGxti4BAADgniNcAQDABopKuHL69GlblwAAAHDPEa4AAAAAAACYwIS2AADYyNy5Pvl6vkOHDuXr+QAAAIoKeq4AAAAAAACYQM8VAABsZMiQ/O1JYrE8m6/nk6SoqKh8PycAAEB+o+cKAAAAAACACYQrAAAAAAAAJhCuAABgAytXxtq6hHxRo0YNW5cAAABwzxGuAABgA0UlXHFzc7N1CQAAAPcc4QoAAAAAAIAJhCsAAAAAAAAmsBQzgALJYpEslkm2LgMA8pWjo6OtS8gTaWkZcnDgNzwAQNFRKMIVi8UiOzs7W5dRYOooSPLinnBfiyY7O2nEiChblwEAuAthYQG2LgEAgHx1258URo8eLXd391v+2b17d37Uafj444/l7u6uuLg4SdLJkyfVrVu3e3KupKQkzZs3T08//bS8vLz06KOPqlu3bvroo49ksViM/f766y+98cYbOnjw4B2fY+bMmfLz85OPj4+++OILxcXFqUePHvL09FTz5s2VlpaW4+suX76st956S23atFGDBg3k5+en3r17a/v27Xd0/t27d8vd3V2HDh2649pvZ8OGDZo5c6bVY3d3d/3555+5er2Z+5pX0tPTc2z3Hh4eatKkifr06aOff/7ZZvXdK3f6XgEAAABAUZarnitVqlTRvHnzcnyudu3aeVrQnfryyy/1448/5vlxMzIy1K9fP507d079+vVTrVq1lJKSoh07dmjcuHE6ceKERo0aJUk6evSoPv30U7344ot3dI7jx48rMjJSXbt2Vfv27VW7dm0tWbJEP/30k8LCwlS5cmU5ODhke93Vq1fVvXt3FStWTEFBQapevbr+/vtvffbZZxo4cKAmTpyoHj165KoGLy8vrV+/Xo888sgd1Z4bERERatq0qfE4MDBQjzzyiFxcXHL1+ru9r/dC9+7d9eyzzxqPU1NT9X//93+KiIjQq6++qi+//FLly5e3YYUAAAAAAFvJVbhSokQJ+fj43OtaCpQffvhBBw8e1Jo1a/Too48a21u1aiV7e3utXr1a/fr1M/WFOiEhQZLUoUMH4xwJCQmqUqWKnnrqqVu+buvWrYqNjdWOHTv04IMPGtvbtGmj/v37a+7cuerWrZvs7W8/1tnJySnf3tvy5cvftwHEgw8+mO0++fn5qWrVqgoKCtLXX39dIEIgAEDRUqeOdPy4rau4f8TGxrI8OADgnsizmcauXbummTNn6oknnlCDBg3UsGFD9enTR7/++qvVflFRUeratat8fHzUokULvfnmm7p69aokaf78+apfv362Y7u7u2vhwoXZts+fP9/oUePu7q758+crJCRErVu3thq2I0nDhg1Tp06dcn09ly9flpTZg+VmPXv21JAhQyRlDqt56aWXJEk9evRQ7969JWUOJ4mIiFC7du3k6ekpX19fde/eXfv375ckzZkzR7169TJe9/jjj8vf318ff/yx/vjjj1te8421/fPPP9meCw4OVlBQkNVwooMHD6p3797y9fVVs2bNNHr0aMXHxxv13zws6Ndff1WfPn3k6+urRx99VEOHDtWFCxeM57Nes3fvXr300kvy8vJS8+bNNXv2bON++fv76+zZs9q4caMxjOvmoSaXLl3SsGHD1KxZM3l5ealjx4765JNP/vW+5tbBgwf18ssvq3HjxvLz89PQoUN17tw54/kNGzbI19dXa9asUdOmTdWkSROdOXPmjs4hSWXKlJEkqzlh4uPjNW7cODVt2lReXl7q3r17tt5V0dHR6ty5s3x8fOTn56eBAwfq1KlTVvt8+eWXeu655+Tp6akWLVpo5syZSk1Ntdpn69at6tq1q3x9fdWgQQO1bdtW69evN57Peq/Wr1+vgIAANWrUyBhm9d133+nFF1+Uj4+P/P399dZbbyk5Odnq+Pv371fnzp3l6emp1q1ba9WqVVbPJycna8aMGWrRooU8PT313HPPaceOHVb7+Pv765133lHPnj3l5eWlN99JAS8HAAAgAElEQVR88w7uMADg37i727qC+8vp06dtXQIAoJDKdbiSnp6e7c+NAcbIkSO1efNm9e/fX8uXL9eYMWN07NgxjRgxwtjvu+++U//+/VWpUiXNmzdPQ4YM0aeffqqxY8feVfGdO3c2egusX79enTt3VqdOnXT27FkjxJAy507Zvn27nn/++Vwfu3HjxipVqpQGDx6sWbNmad++fbp+/bokqVatWkavFS8vL02cOFGSNHXqVE2YMEGSNGPGDC1ZskTdu3fXsmXLNGXKFF2+fFkhISG6fv26unXrZvW6d999V4sWLVJAQICqVKmi9evX3zIMat68uezt7dWzZ08tXLhQhw8fVnp6uiTJ29tbffr00QMPPCBJ+vnnn42QIiwsTOPHj1dMTIyCg4NzPHbWHDZXr17VzJkzNWXKFB05ckQ9e/ZUUlKS1b5ZwciSJUv01FNPafHixUY4smjRIlWsWFGtW7fW+vXrc+yxMnz4cMXGxmrq1KlavHix6tSpo5EjR2r//v23vK+5ER0drR49eqhUqVIKDQ3VuHHj9PPPP6tr165GqCRJKSkpWrdunWbMmKExY8aoWrVqtzxmRkaGVdu/evWq9u/fr2nTpsnZ2VktW7Y0jvnyyy9r586dGjFihObNmycnJye9/PLL+t///icp81ezQYMGydvbW4sWLdLUqVN14sQJBQUFGZ+VzZs3KyQkRHXr1tXChQvVr18/rV27ViNHjjRq+uqrrzRkyBD5+vpq4cKFmj9/vh588EFNnDjROFeWiIgIjR8/XuPHj1eDBg20fft2DRgwQA8++KDmzZunkJAQbd68WePHj7d63aRJk9SxY0ctXrxYDRo00PTp0415liwWi4KDg7Vx40b169dP4eHhqlOnjgYMGKDvvvvO6jirV69W48aNFR4ebjW8CgAAAAAKg1wNC/r999/l4eGRbfvkyZPVrVs3Xb9+XcnJyZowYYIxnMXPz09JSUmaMWOGrly5ovLly2v+/Plq0KCB3n33XeMYFotFK1asyPbFPTeqVKmiKlWqSJIxZKNixYqqXLmyPvnkEzVu3FhS5q/7FotF7dq1y/WxK1eurIiICI0bN05LlizRkiVL5ODgIF9fXz3zzDN6/vnnVaxYMTk5ORnzztSqVUu1atWSJF28eFEjRoywmvukePHiGjp0qE6ePKn69etbva5evXqSMofO3G4YloeHh2bNmqVp06Zp3rx5mjdvnkqVKiU/Pz916tRJTz75pLFvRESEKlWqpCVLlqhEiRKSMocCTZs2LceeGvPnz1eZMmW0YsUKlSpVSpL06KOP6vHHH9fatWv12muvGft269ZNQUFBkqTHHntM27dv13fffafnnntO9evXV4kSJVS+fPlbXsv+/fs1ZMgQtWnTRlJmm3F1dVXx4sVveV9zY/bs2XJ3d1dERITRo6Rhw4Z6+umntWLFCg0fPlxSZmAyaNAgBQTcfkWDOXPmaM6cOVbbHnjgATVu3FihoaGqVKmSpMwJl//v//5PmzZtMt5Tf39/Pf/885ozZ44iIyP1008/6fr16xowYIBcXV0lZba3HTt2KDk5WSVLllRYWJjatGmjt99+W5LUokULVa5cWSEhIXrllVfk7e2t3377TZ07dzbm/pEy59Bp1qyZ9u3bZ/WZ7dWrlx5//HHj8fz58+Xj46O5c+ca2zIyMrR69Wpdu3bN2DZ69GgjlPTx8dE333yjmJgYNWvWTNHR0dq9e7fCw8ON99Df31+JiYkKDQ1Vq1atjOPUrFlTr7/++m3vMwDgzt0wvVmBcS8mygcAoCDL9YS2CxYsyLb9oYcekpT5JXPZsmWSpAsXLujUqVOKjY01fr1OS0tTSkqKjhw5oqFDh1od44UXXtALL7xg6iJuVKxYMXXs2FHr1q3TxIkTVaJECW3atEmtWrXK9USqWZo2bart27dr//79+v777415WPbu3avPPvtMkZGRRmBxs6wA6fLly8b9+PbbbyXplisA3Ym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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig2 = question_7_percentages.plot.barh(figsize=(14.4, 7), stacked=False, cmap=sns.light_palette(\"Navy\", as_cmap=True, reverse=True), xerr=question_7_percent_err, ecolor='silver', capsize=4)\n", + "fig2.grid(False)\n", + "fig2.grid(axis='x', color='whitesmoke')\n", + "fig2.set_title('Have you had formal training in software development?',y=1.225)\n", + "fig2.set_xlabel('Percentage in each category')\n", + "fig2.set_ylabel('')\n", + "fig2.axvline(0, color='lightgray', lw=1.75)\n", + "fig2.set_xlim(0.0, 101.0)\n", + "fig2.invert_yaxis() # match the order of the legend colors to the order of the bar colors\n", + "fig2.legend(bbox_to_anchor=(0., 1.02, 0.945, .102), loc='lower left', mode=\"expand\", borderaxespad=0., ncol=1)\n", + "fig2.spines['top'].set_visible(False)\n", + "fig2.spines['right'].set_visible(False)\n", + "fig2.spines['bottom'].set_visible(False)\n", + "fig2.spines['left'].set_visible(False)\n", + "fig2.figure.savefig(\"Figure2.png\",bbox_inches='tight',dpi=300)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "grouped_7a = df.groupby(['Have you had formal training in programming?'], sort=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "question_7a_numbers = pd.DataFrame(grouped_7a.size()).rename(columns={0: \"Total Numbers\"})" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Total Numbers
Have you had formal training in programming?
Yes, a little (e.g. online classes, books, workshops)155
Yes, a lot (e.g. CS courses at an undergraduate or graduate level)134
No75
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" + ], + "text/plain": [ + " Total Numbers\n", + "Have you had formal training in programming? \n", + "Yes, a little (e.g. online classes, books, work... 155\n", + "Yes, a lot (e.g. CS courses at an undergraduate... 134\n", + "No 75" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "question_7a_numbers" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "63.18681318681318 Β± 4.166415079149203 of the community has no formal training in software development.\n" + ] + } + ], + "source": [ + "total_no_formal_training = question_7a_numbers['Total Numbers']['Yes, a little (e.g. online classes, books, workshops)'] + question_7a_numbers['Total Numbers']['No']\n", + "percentage_no_formal_training = (total_no_formal_training / len(df))*100.\n", + "percentage_err_no_formal_training = (np.sqrt(total_no_formal_training) / len(df))*100.\n", + "print(percentage_no_formal_training,\"Β±\",percentage_err_no_formal_training,\"of the community has no formal training in software development.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "36.81318681318682 Β± 3.180174973294018 of the community has formal training in software development.\n" + ] + } + ], + "source": [ + "total_formal_training = question_7a_numbers['Total Numbers']['Yes, a lot (e.g. CS courses at an undergraduate or graduate level)']\n", + "percentage_formal_training = (total_formal_training / len(df))*100.\n", + "percentage_err_formal_training = (np.sqrt(total_formal_training) / len(df))*100.\n", + "print(percentage_formal_training,\"Β±\",percentage_err_formal_training,\"of the community has formal training in software development.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Training vs. Expertise" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "space = df['Which of these areas of solar physics do you work in? Check all that apply.'].str.contains('Space-Based').groupby(df['Have you had formal training in programming?']).sum()\n", + "ground = df['Which of these areas of solar physics do you work in? Check all that apply.'].str.contains('Ground-Based').groupby(df['Have you had formal training in programming?']).sum()\n", + "sims = df['Which of these areas of solar physics do you work in? Check all that apply.'].str.contains('Simulations').groupby(df['Have you had formal training in programming?']).sum()\n", + "theory = df['Which of these areas of solar physics do you work in? Check all that apply.'].str.contains('Theory').groupby(df['Have you had formal training in programming?']).sum()\n", + "inst = df['Which of these areas of solar physics do you work in? Check all that apply.'].str.contains('Instrumentation').groupby(df['Have you had formal training in programming?']).sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Observational (Space-Based)Observational (Ground-Based)InstrumentationTheoryNumerical Simulations
Have you had formal training in programming?
No56.035.021.028.029.0
Yes, a little (e.g. online classes, books, workshops)114.072.036.038.072.0
Yes, a lot (e.g. CS courses at an undergraduate or graduate level)105.062.036.039.070.0
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" + ], + "text/plain": [ + " Observational (Space-Based) \\\n", + "Have you had formal training in programming? \n", + "No 56.0 \n", + "Yes, a little (e.g. online classes, books, work... 114.0 \n", + "Yes, a lot (e.g. CS courses at an undergraduate... 105.0 \n", + "\n", + " Observational (Ground-Based) \\\n", + "Have you had formal training in programming? \n", + "No 35.0 \n", + "Yes, a little (e.g. online classes, books, work... 72.0 \n", + "Yes, a lot (e.g. CS courses at an undergraduate... 62.0 \n", + "\n", + " Instrumentation Theory \\\n", + "Have you had formal training in programming? \n", + "No 21.0 28.0 \n", + "Yes, a little (e.g. online classes, books, work... 36.0 38.0 \n", + "Yes, a lot (e.g. CS courses at an undergraduate... 36.0 39.0 \n", + "\n", + " Numerical Simulations \n", + "Have you had formal training in programming? \n", + "No 29.0 \n", + "Yes, a little (e.g. online classes, books, work... 72.0 \n", + "Yes, a lot (e.g. CS courses at an undergraduate... 70.0 " + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_7b_numbers = {'Observational (Space-Based)': space, 'Observational (Ground-Based)': ground, 'Instrumentation': inst, 'Theory': theory, 'Numerical Simulations': sims}\n", + "question_7b_numbers = pd.DataFrame(data_7b_numbers)\n", + "question_7b_numbers" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Observational (Space-Based)Observational (Ground-Based)InstrumentationTheoryNumerical Simulations
Have you had formal training in programming?
No20.36363620.71005922.58064526.66666716.959064
Yes, a little (e.g. online classes, books, workshops)41.45454542.60355038.70967736.19047642.105263
Yes, a lot (e.g. CS courses at an undergraduate or graduate level)38.18181836.68639138.70967737.14285740.935673
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" + ], + "text/plain": [ + " Observational (Space-Based) \\\n", + "Have you had formal training in programming? \n", + "No 20.363636 \n", + "Yes, a little (e.g. online classes, books, work... 41.454545 \n", + "Yes, a lot (e.g. CS courses at an undergraduate... 38.181818 \n", + "\n", + " Observational (Ground-Based) \\\n", + "Have you had formal training in programming? \n", + "No 20.710059 \n", + "Yes, a little (e.g. online classes, books, work... 42.603550 \n", + "Yes, a lot (e.g. CS courses at an undergraduate... 36.686391 \n", + "\n", + " Instrumentation \\\n", + "Have you had formal training in programming? \n", + "No 22.580645 \n", + "Yes, a little (e.g. online classes, books, work... 38.709677 \n", + "Yes, a lot (e.g. CS courses at an undergraduate... 38.709677 \n", + "\n", + " Theory \\\n", + "Have you had formal training in programming? \n", + "No 26.666667 \n", + "Yes, a little (e.g. online classes, books, work... 36.190476 \n", + "Yes, a lot (e.g. CS courses at an undergraduate... 37.142857 \n", + "\n", + " Numerical Simulations \n", + "Have you had formal training in programming? \n", + "No 16.959064 \n", + "Yes, a little (e.g. online classes, books, work... 42.105263 \n", + "Yes, a lot (e.g. CS courses at an undergraduate... 40.935673 " + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_7b_percentages = {'Observational (Space-Based)': space / df1a['All Participants']['Observational (Space-Based)'], 'Observational (Ground-Based)': ground / df1a['All Participants']['Observational (Ground-Based)'], 'Instrumentation': inst / df1a['All Participants']['Instrumentation'], 'Theory': theory / df1a['All Participants']['Theory'], 'Numerical Simulations': sims / df1a['All Participants']['Numerical Simulations']}\n", + "question_7b_percentages = pd.DataFrame(data_7b_percentages)*100.\n", + "question_7b_percentages" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Observational (Space-Based)Observational (Ground-Based)InstrumentationTheoryNumerical Simulations
Have you had formal training in programming?
No2.7212053.5006394.9275015.0395263.149219
Yes, a little (e.g. online classes, books, workshops)3.8825745.0208776.4516135.8708704.962153
Yes, a lot (e.g. CS courses at an undergraduate or graduate level)3.7261644.6591766.4516135.9476174.892749
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" + ], + "text/plain": [ + " Observational (Space-Based) \\\n", + "Have you had formal training in programming? \n", + "No 2.721205 \n", + "Yes, a little (e.g. online classes, books, work... 3.882574 \n", + "Yes, a lot (e.g. CS courses at an undergraduate... 3.726164 \n", + "\n", + " Observational (Ground-Based) \\\n", + "Have you had formal training in programming? \n", + "No 3.500639 \n", + "Yes, a little (e.g. online classes, books, work... 5.020877 \n", + "Yes, a lot (e.g. CS courses at an undergraduate... 4.659176 \n", + "\n", + " Instrumentation Theory \\\n", + "Have you had formal training in programming? \n", + "No 4.927501 5.039526 \n", + "Yes, a little (e.g. online classes, books, work... 6.451613 5.870870 \n", + "Yes, a lot (e.g. CS courses at an undergraduate... 6.451613 5.947617 \n", + "\n", + " Numerical Simulations \n", + "Have you had formal training in programming? \n", + "No 3.149219 \n", + "Yes, a little (e.g. online classes, books, work... 4.962153 \n", + "Yes, a lot (e.g. CS courses at an undergraduate... 4.892749 " + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_7b_percentage_err = {'Observational (Space-Based)': np.sqrt(space) / df1a['All Participants']['Observational (Space-Based)'], 'Observational (Ground-Based)': np.sqrt(ground) / df1a['All Participants']['Observational (Ground-Based)'], 'Instrumentation': np.sqrt(inst) / df1a['All Participants']['Instrumentation'], 'Theory': np.sqrt(theory) / df1a['All Participants']['Theory'], 'Numerical Simulations': np.sqrt(sims) / df1a['All Participants']['Numerical Simulations']}\n", + "question_7b_percentage_err = pd.DataFrame(data_7b_percentage_err)*100.\n", + "question_7b_percentage_err" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Conclusions:\n", + "* Although 99$\\pm$0.5\\% of the solar physics community uses software in their research and 91$\\pm$5\\% often or occasionally write their own software, 63$\\pm$4\\% of the community has not had any formal training (e.g., computer science courses) at an undergraduate or graduate level.\n", + "* Students today are twice as likely to have a lot of formal training in programming compared with faculty, researchers, and staff scientists (51% compared with 27%).\n", + "* The amount of formal training does not vary with area of expertise.\n", + "* We used almost exactly the same question as the Astrophysics community survey. They asked, \"Have you had formal training in software development?\" with the options of Yes, No, or A little (see Figure 6 in Momcheva & Tollerud, 2015). They found that only 8$\\pm$1\\% of the astrophysics community received substantial training; however, their question did not define β€œa lot\" or β€œa little\". Also their survey was from 5 years ago. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Question 8: Which of the following statements is most applicable to you?\n", + "πŸ”² **I write mostly my own software.** \n", + "πŸ”² **I mostly use software written by others.** \n", + "πŸ”² **Somewhere in between.**" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "grouped8 = df.groupby(['Which of the following statements is most applicable to you?', 'How would you describe the stage of your career?'], sort=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "question_8_numbers = pd.DataFrame(grouped8.size()).unstack().T.reset_index(level=0, drop=True)\n", + "question_8_numbers = question_8_numbers.reindex(['Faculty, Staff Scientist, or Researcher', 'Postdoc', 'Graduate or Undergraduate Student', 'Software or Instrument Developer'])\n", + "question_8_numbers = question_8_numbers[['I write mostly my own software.', 'Somewhere in between.', 'I mostly use software written by others.']]" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Which of the following statements is most applicable to you?I write mostly my own software.Somewhere in between.I mostly use software written by others.
How would you describe the stage of your career?
Faculty, Staff Scientist, or Researcher34.14634157.5609768.292683
Postdoc30.18867960.3773589.433962
Graduate or Undergraduate Student33.33333358.3333338.333333
Software or Instrument Developer45.45454545.4545459.090909
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" + ], + "text/plain": [ + "Which of the following statements is most applicable to you? I write mostly my own software. \\\n", + "How would you describe the stage of your career? \n", + "Faculty, Staff Scientist, or Researcher 34.146341 \n", + "Postdoc 30.188679 \n", + "Graduate or Undergraduate Student 33.333333 \n", + "Software or Instrument Developer 45.454545 \n", + "\n", + "Which of the following statements is most applicable to you? Somewhere in between. \\\n", + "How would you describe the stage of your career? \n", + "Faculty, Staff Scientist, or Researcher 57.560976 \n", + "Postdoc 60.377358 \n", + "Graduate or Undergraduate Student 58.333333 \n", + "Software or Instrument Developer 45.454545 \n", + "\n", + "Which of the following statements is most applicable to you? I mostly use software written by others. \n", + "How would you describe the stage of your career? \n", + "Faculty, Staff Scientist, or Researcher 8.292683 \n", + "Postdoc 9.433962 \n", + "Graduate or Undergraduate Student 8.333333 \n", + "Software or Instrument Developer 9.090909 " + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "question_8_percentages = question_8_numbers / total_numbers.values*100.\n", + "question_8_percentages" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Which of the following statements is most applicable to you?I write mostly my own software.Somewhere in between.I mostly use software written by others.
How would you describe the stage of your career?
Faculty, Staff Scientist, or Researcher4.0812685.2989172.011271
Postdoc7.54717010.6733104.218996
Graduate or Undergraduate Student6.2994088.3333333.149704
Software or Instrument Developer14.37398914.3739896.428243
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" + ], + "text/plain": [ + "Which of the following statements is most applicable to you? I write mostly my own software. \\\n", + "How would you describe the stage of your career? \n", + "Faculty, Staff Scientist, or Researcher 4.081268 \n", + "Postdoc 7.547170 \n", + "Graduate or Undergraduate Student 6.299408 \n", + "Software or Instrument Developer 14.373989 \n", + "\n", + "Which of the following statements is most applicable to you? Somewhere in between. \\\n", + "How would you describe the stage of your career? \n", + "Faculty, Staff Scientist, or Researcher 5.298917 \n", + "Postdoc 10.673310 \n", + "Graduate or Undergraduate Student 8.333333 \n", + "Software or Instrument Developer 14.373989 \n", + "\n", + "Which of the following statements is most applicable to you? I mostly use software written by others. \n", + "How would you describe the stage of your career? \n", + "Faculty, Staff Scientist, or Researcher 2.011271 \n", + "Postdoc 4.218996 \n", + "Graduate or Undergraduate Student 3.149704 \n", + "Software or Instrument Developer 6.428243 " + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "question_8_percent_err = (np.sqrt(question_8_numbers)) / total_numbers.values*100.\n", + "question_8_percent_err" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig3 = question_8_percentages.plot.barh(figsize=(14.4, 7), stacked=False, cmap=sns.light_palette(\"Navy\", as_cmap=True, reverse=True), xerr=question_8_percent_err, ecolor='silver', capsize=4)\n", + "fig3.grid(False)\n", + "fig3.grid(axis='x', color='whitesmoke')\n", + "fig3.set_title('Which of the following statements is most applicable to you?',y=1.225)\n", + "fig3.set_xlabel('Percentage in each category')\n", + "fig3.set_ylabel('')\n", + "fig3.axvline(0, color='lightgray', lw=1.75)\n", + "fig3.set_xlim(0.0, 101.0)\n", + "fig3.invert_yaxis()\n", + "fig3.legend(bbox_to_anchor=(0., 1.02, 0.945, .102), loc='lower left', mode=\"expand\", borderaxespad=0., ncol=1)\n", + "fig3.spines['top'].set_visible(False)\n", + "fig3.spines['right'].set_visible(False)\n", + "fig3.spines['bottom'].set_visible(False)\n", + "fig3.spines['left'].set_visible(False)\n", + "fig3.figure.savefig(\"Figure3.png\",bbox_inches='tight',dpi=300)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Are the people who write mostly their own software better trained? Maybe marginally." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Percentage of highly trained people who write their own software: 44.354838709677416 plus or minus 5.98080523152876\n" + ] + } + ], + "source": [ + "mostly_write_own_software = df.loc[df['Which of the following statements is most applicable to you?'] == 'I write mostly my own software.']\n", + "highly_trained = len(mostly_write_own_software.loc[mostly_write_own_software['Have you had formal training in programming?'] == 'Yes, a lot (e.g. CS courses at an undergraduate or graduate level)'])\n", + "print(\"Percentage of highly trained people who write their own software:\",(highly_trained/len(mostly_write_own_software)*100.),\"plus or minus\",(np.sqrt(highly_trained)/len(mostly_write_own_software)*100.))" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "91.48351648351648 plus or minus 5.013265821674357 percent of people often or occasionally write their own software.\n" + ] + } + ], + "source": [ + "print(100 * (question_8_numbers['I write mostly my own software.'].sum() + question_8_numbers['Somewhere in between.'].sum()) / len(df),\"plus or minus\",(100 * (np.sqrt((question_8_numbers['I write mostly my own software.'].sum() + question_8_numbers['Somewhere in between.'].sum()))) /len(df)),\"percent of people often or occasionally write their own software.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Conclusions:\n", + "* 92% of solar physicists often or occasionally write their own software\n", + "* This doesn't really vary by stage of career. \n", + "* People who write mostly their own software are no better trained than everyone else: 44$\\pm$6\\% of people who write their own software reported \"a lot (e.g., computer science courses)\" of formal training, compared with 37$\\pm$3\\% of the entire community.\n", + "* We used exactly the same question as the Astrophysics community survey (see Figure 3 in Momcheva & Tollerud, 2015). They also found that roughly a third of respondents wrote mostly their own software and that this percentage did not vary much by career stage." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Question 9: Which of the following have you personally utilized in your work within the last year? Check all that apply.\n", + "\n", + "πŸ”² **SolarSoft \n", + "πŸ”² Python \n", + "πŸ”² SunPy \n", + "πŸ”² Shell Scripting \n", + "πŸ”² C \n", + "πŸ”² C++ \n", + "πŸ”² Fortran \n", + "πŸ”² IRAF \n", + "πŸ”² Perl \n", + "πŸ”² Javascript \n", + "πŸ”² Julia \n", + "πŸ”² Matlab \n", + "πŸ”² Java \n", + "πŸ”² R \n", + "πŸ”² SQL \n", + "πŸ”² Ruby \n", + "πŸ”² HTML / CSS \n", + "πŸ”² Spreadsheets (e.g. Excel) \n", + "πŸ”² Mathematica \n", + "πŸ”² MPI \n", + "πŸ”² Github (or similar) \n", + "πŸ”² Other (Respondents can write in their own description.)**" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "breadth = []\n", + "for i in range(len(df)):\n", + " breadth.append(len(df['Which of the following have you personally utilized in your work within the last year?'][i].split(\", \")))" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "df = df.assign(Breadth=pd.Series(breadth).values)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The average number of software tools used by all participants is: 5.0\n", + "The average number of software tools used by Faculty, Staff Scientist, or Researcher is: 5.0\n", + "The average number of software tools used by Postdocs is: 5.0\n", + "The average number of software tools used by Graduate or Undergraduate Students is: 4.0\n", + "The average number of software tools used by Software or Instrument Developers is: 7.0\n" + ] + } + ], + "source": [ + "fssr = df.loc[df['How would you describe the stage of your career?'] == 'Faculty, Staff Scientist, or Researcher']\n", + "postdoc = df.loc[df['How would you describe the stage of your career?'] == 'Postdoc']\n", + "gs = df.loc[df['How would you describe the stage of your career?'] == 'Graduate or Undergraduate Student']\n", + "us = df.loc[df['How would you describe the stage of your career?'] == 'Software or Instrument Developer']\n", + "print(\"The average number of software tools used by all participants is:\",df['Breadth'].median())\n", + "print(\"The average number of software tools used by Faculty, Staff Scientist, or Researcher is:\",fssr['Breadth'].median())\n", + "print(\"The average number of software tools used by Postdocs is:\",postdoc['Breadth'].median())\n", + "print(\"The average number of software tools used by Graduate or Undergraduate Students is:\",gs['Breadth'].median())\n", + "print(\"The average number of software tools used by Software or Instrument Developers is:\",us['Breadth'].median())" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "def count_all_the_software_tools(list_of_all_values):\n", + " \"\"\"\n", + " \n", + " Input: list_of_all_values, list of lists containing all the responses to the question:\n", + " \n", + " \"Which of the software tools following have you personally utilized in your work within the last year? Check all that apply.\"\n", + " \n", + " \"\"\"\n", + " IDL_exclusives = 0\n", + " Python_exclusives = 0\n", + " IDL_Python_both = 0\n", + " proprietary_count = 0\n", + " free_count = 0\n", + " IDL_count = 0\n", + " SolarSoft_count = 0\n", + " Python_count = 0\n", + " SunPy_count = 0\n", + " Shell_count = 0\n", + " c_count = 0\n", + " cp_count = 0\n", + " Fortran_count = 0\n", + " IRAF_count = 0\n", + " Perl_count = 0\n", + " Javascript_count = 0\n", + " Julia_count = 0\n", + " Matlab_count = 0\n", + " Java_count = 0\n", + " R_count = 0\n", + " SQL_count = 0\n", + " Ruby_count = 0\n", + " HTML_count = 0\n", + " Spreadsheets_count = 0\n", + " Mathematica_count = 0\n", + " MPI_count = 0\n", + " Github_count = 0\n", + " for i in range(len(list_of_all_values)):\n", + " single_row_list = list_of_all_values[i].split(\", \")\n", + " if \"IDL\" in single_row_list:\n", + " IDL_count += 1\n", + " proprietary_count += 1\n", + " if \"SolarSoft\" in single_row_list:\n", + " SolarSoft_count += 1\n", + " proprietary_count += 1\n", + " if \"Python\" in single_row_list:\n", + " Python_count += 1\n", + " free_count += 1\n", + " if \"SunPy\" in single_row_list:\n", + " SunPy_count += 1\n", + " free_count += 1\n", + " if \"Shell scripting\" in single_row_list:\n", + " Shell_count += 1\n", + " free_count += 1\n", + " if (\"C\") in single_row_list:\n", + " c_count += 1\n", + " free_count += 1\n", + " if \"C++\" in single_row_list:\n", + " cp_count += 1\n", + " free_count += 1\n", + " if \"Fortran\" in single_row_list:\n", + " Fortran_count += 1\n", + " free_count += 1\n", + " if \"IRAF\" in single_row_list:\n", + " IRAF_count += 1\n", + " free_count += 1 # was proprietary, now free\n", + " if \"Perl\" in single_row_list:\n", + " Perl_count += 1\n", + " free_count += 1\n", + " if \"Javascript\" in single_row_list:\n", + " Javascript_count += 1\n", + " free_count += 1\n", + " if \"Julia\" in single_row_list:\n", + " Julia_count += 1\n", + " free_count += 1\n", + " if \"MATLAB\" in single_row_list:\n", + " Matlab_count += 1\n", + " proprietary_count += 1\n", + " if \"Java\" in single_row_list:\n", + " Java_count += 1\n", + " free_count += 1\n", + " if \"R\" in single_row_list:\n", + " R_count += 1\n", + " free_count += 1\n", + " if \"SQL\" in single_row_list:\n", + " SQL_count += 1\n", + " free_count += 1\n", + " if \"Ruby\" in single_row_list:\n", + " Ruby_count += 1\n", + " free_count += 1\n", + " if \"HTML/CSS\" in single_row_list:\n", + " HTML_count += 1\n", + " free_count += 1\n", + " if \"Spreadsheets (e.g. Excel)\" in single_row_list:\n", + " Spreadsheets_count += 1\n", + " # unclear if proprietary or free; some are proprietary, some are free so no count either way for this\n", + " if \"Mathematica\" in single_row_list:\n", + " Mathematica_count += 1\n", + " proprietary_count += 1\n", + " if \"MPI\" in single_row_list:\n", + " MPI_count += 1\n", + " # could be open MPI or MPI so no count either way for this\n", + " if \"Github (or similar)\" in single_row_list:\n", + " Github_count += 1\n", + " if (\"IDL\" in single_row_list and \"Python\" not in single_row_list):\n", + " IDL_exclusives += 1\n", + " if (\"Python\" in single_row_list and \"IDL\" not in single_row_list):\n", + " Python_exclusives += 1\n", + " if (\"IDL\" in single_row_list and \"Python\" in single_row_list):\n", + " IDL_Python_both += 1\n", + " software_names = [\"IDL\", \"SolarSoft\", \"Python\", \"SunPy\", \"Shell Scripting\", \"C\", \"C++\", \"Fortran\", \"IRAF\", \"Perl\", \"Javascript\", \"Julia\", \"MATLAB\", \"Java\", \"R\", \"SQL\", \"Ruby\", \"HTML / CSS\", \"Spreadsheets (e.g. Excel)\", \"Mathematica\", \"MPI\", \"Github (or similar)\"]\n", + " user_counts = [IDL_count, SolarSoft_count, Python_count, SunPy_count, Shell_count, c_count, cp_count, Fortran_count, IRAF_count, Perl_count, Javascript_count, Julia_count, Matlab_count, Java_count, R_count, SQL_count, Ruby_count, HTML_count, Spreadsheets_count, Mathematica_count, MPI_count, Github_count]\n", + " idl_vs_python = [IDL_exclusives, Python_exclusives, IDL_Python_both]\n", + " return user_counts, proprietary_count, free_count, idl_vs_python" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Scientific Stack vs. Career" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "# All participants\n", + "list_of_all_values_all = df['Which of the following have you personally utilized in your work within the last year?'].tolist()\n", + "user_counts_all, proprietary_count_all, free_count_all, idl_vs_python_all = count_all_the_software_tools(list_of_all_values_all)\n", + "user_percentages = (np.array(user_counts_all)/total_responses) * 100.\n", + "\n", + "# Faculty, Staff Scientist, or Researcher\n", + "fssr = df['How would you describe the stage of your career?'] == 'Faculty, Staff Scientist, or Researcher'\n", + "list_of_all_values_fssr = df['Which of the following have you personally utilized in your work within the last year?'][fssr].tolist()\n", + "user_counts_fssr, proprietary_count_fssr, free_count_fssr, idl_vs_python_fssr = count_all_the_software_tools(list_of_all_values_fssr)\n", + "user_percentages_fssr = (np.array(user_counts_fssr)/(total_numbers['Total Numbers']['Faculty, Staff Scientist, or Researcher'])) * 100.\n", + "\n", + "# Postdocs\n", + "postdoc = df['How would you describe the stage of your career?'] == 'Postdoc'\n", + "list_of_all_values_pd = df['Which of the following have you personally utilized in your work within the last year?'][postdoc].tolist()\n", + "user_counts_pd, proprietary_count_pd, free_count_pd, idl_vs_python_pd = count_all_the_software_tools(list_of_all_values_pd)\n", + "user_percentages_pd = (np.array(user_counts_pd)/(total_numbers['Total Numbers']['Postdoc'])) * 100.\n", + "\n", + "# Graduate or Undergraduate Students\n", + "gs = df['How would you describe the stage of your career?'] == 'Graduate or Undergraduate Student'\n", + "list_of_all_values_gs = df['Which of the following have you personally utilized in your work within the last year?'][gs].tolist()\n", + "user_counts_gs, proprietary_count_gs, free_count_gs, idl_vs_python_gs = count_all_the_software_tools(list_of_all_values_gs)\n", + "user_percentages_gs = (np.array(user_counts_gs)/(total_numbers['Total Numbers']['Graduate or Undergraduate Student'])) * 100.\n", + "\n", + "# Software or Instrument Developers\n", + "us = df['How would you describe the stage of your career?'] == 'Software or Instrument Developer'\n", + "list_of_all_values_us = df['Which of the following have you personally utilized in your work within the last year?'][us].tolist()\n", + "user_counts_us, proprietary_count_us, free_count_us, idl_vs_python_us = count_all_the_software_tools(list_of_all_values_us)\n", + "user_percentages_us = (np.array(user_counts_us)/(total_numbers['Total Numbers']['Software or Instrument Developer'])) * 100." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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All ParticipantsFaculty, Staff Scientist, or ResearcherPostdocGraduate or Undergraduate StudentSoftware or Instrument Developer
IDL73.07692378.04878075.47169859.52381072.727273
SolarSoft56.86813261.46341560.37735845.23809550.000000
Python66.20879158.53658575.47169878.57142968.181818
SunPy37.08791231.70731739.62264250.00000031.818182
Shell Scripting39.01098940.97561035.84905732.14285754.545455
C18.68131920.97561011.32075513.09523836.363636
C++16.20879115.60975616.98113214.28571427.272727
Fortran34.89011040.48780532.07547229.7619059.090909
IRAF2.4725273.4146340.0000002.3809520.000000
Perl6.86813210.2439020.0000002.3809529.090909
Javascript7.9670339.2682933.7735850.00000036.363636
Julia3.0219783.4146345.6603771.1904760.000000
MATLAB19.23076919.02439026.41509419.0476194.545455
Java3.5714292.9268290.0000001.19047627.272727
R4.6703304.8780495.6603772.3809529.090909
SQL10.43956012.1951223.7735851.19047645.454545
Ruby0.5494510.4878051.8867920.0000000.000000
HTML / CSS20.87912125.85365916.9811325.95238140.909091
Spreadsheets (e.g. Excel)32.69230833.65853730.18867926.19047654.545455
Mathematica10.9890119.26829315.09434013.0952389.090909
MPI19.50549520.97561022.64150919.0476190.000000
Github (or similar)44.50549541.46341550.94339642.85714363.636364
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" + ], + "text/plain": [ + " All Participants \\\n", + "IDL 73.076923 \n", + "SolarSoft 56.868132 \n", + "Python 66.208791 \n", + "SunPy 37.087912 \n", + "Shell Scripting 39.010989 \n", + "C 18.681319 \n", + "C++ 16.208791 \n", + "Fortran 34.890110 \n", + "IRAF 2.472527 \n", + "Perl 6.868132 \n", + "Javascript 7.967033 \n", + "Julia 3.021978 \n", + "MATLAB 19.230769 \n", + "Java 3.571429 \n", + "R 4.670330 \n", + "SQL 10.439560 \n", + "Ruby 0.549451 \n", + "HTML / CSS 20.879121 \n", + "Spreadsheets (e.g. Excel) 32.692308 \n", + "Mathematica 10.989011 \n", + "MPI 19.505495 \n", + "Github (or similar) 44.505495 \n", + "\n", + " Faculty, Staff Scientist, or Researcher Postdoc \\\n", + "IDL 78.048780 75.471698 \n", + "SolarSoft 61.463415 60.377358 \n", + "Python 58.536585 75.471698 \n", + "SunPy 31.707317 39.622642 \n", + "Shell Scripting 40.975610 35.849057 \n", + "C 20.975610 11.320755 \n", + "C++ 15.609756 16.981132 \n", + "Fortran 40.487805 32.075472 \n", + "IRAF 3.414634 0.000000 \n", + "Perl 10.243902 0.000000 \n", + "Javascript 9.268293 3.773585 \n", + "Julia 3.414634 5.660377 \n", + "MATLAB 19.024390 26.415094 \n", + "Java 2.926829 0.000000 \n", + "R 4.878049 5.660377 \n", + "SQL 12.195122 3.773585 \n", + "Ruby 0.487805 1.886792 \n", + "HTML / CSS 25.853659 16.981132 \n", + "Spreadsheets (e.g. Excel) 33.658537 30.188679 \n", + "Mathematica 9.268293 15.094340 \n", + "MPI 20.975610 22.641509 \n", + "Github (or similar) 41.463415 50.943396 \n", + "\n", + " Graduate or Undergraduate Student \\\n", + "IDL 59.523810 \n", + "SolarSoft 45.238095 \n", + "Python 78.571429 \n", + "SunPy 50.000000 \n", + "Shell Scripting 32.142857 \n", + "C 13.095238 \n", + "C++ 14.285714 \n", + "Fortran 29.761905 \n", + "IRAF 2.380952 \n", + "Perl 2.380952 \n", + "Javascript 0.000000 \n", + "Julia 1.190476 \n", + "MATLAB 19.047619 \n", + "Java 1.190476 \n", + "R 2.380952 \n", + "SQL 1.190476 \n", + "Ruby 0.000000 \n", + "HTML / CSS 5.952381 \n", + "Spreadsheets (e.g. Excel) 26.190476 \n", + "Mathematica 13.095238 \n", + "MPI 19.047619 \n", + "Github (or similar) 42.857143 \n", + "\n", + " Software or Instrument Developer \n", + "IDL 72.727273 \n", + "SolarSoft 50.000000 \n", + "Python 68.181818 \n", + "SunPy 31.818182 \n", + "Shell Scripting 54.545455 \n", + "C 36.363636 \n", + "C++ 27.272727 \n", + "Fortran 9.090909 \n", + "IRAF 0.000000 \n", + "Perl 9.090909 \n", + "Javascript 36.363636 \n", + "Julia 0.000000 \n", + "MATLAB 4.545455 \n", + "Java 27.272727 \n", + "R 9.090909 \n", + "SQL 45.454545 \n", + "Ruby 0.000000 \n", + "HTML / CSS 40.909091 \n", + "Spreadsheets (e.g. Excel) 54.545455 \n", + "Mathematica 9.090909 \n", + "MPI 0.000000 \n", + "Github (or similar) 63.636364 " + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "software_names = [\"IDL\", \"SolarSoft\", \"Python\", \"SunPy\", \"Shell Scripting\", \"C\", \"C++\", \"Fortran\", \"IRAF\", \"Perl\", \"Javascript\", \"Julia\", \"MATLAB\", \"Java\", \"R\", \"SQL\", \"Ruby\", \"HTML / CSS\", \"Spreadsheets (e.g. Excel)\", \"Mathematica\", \"MPI\", \"Github (or similar)\"]\n", + "\n", + "df9 = pd.DataFrame({'All Participants': list(user_percentages), \n", + " 'Faculty, Staff Scientist, or Researcher': list(user_percentages_fssr), \n", + " 'Postdoc': list(user_percentages_pd),\n", + " 'Graduate or Undergraduate Student': list(user_percentages_gs),\n", + " 'Software or Instrument Developer': list(user_percentages_us)},\n", + " index=software_names)\n", + "df9" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some rows have an 'All Participants' value of less than 5%:" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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All ParticipantsFaculty, Staff Scientist, or ResearcherPostdocGraduate or Undergraduate StudentSoftware or Instrument Developer
IRAF2.4725273.4146340.0000002.3809520.000000
Julia3.0219783.4146345.6603771.1904760.000000
Java3.5714292.9268290.0000001.19047627.272727
R4.6703304.8780495.6603772.3809529.090909
Ruby0.5494510.4878051.8867920.0000000.000000
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" + ], + "text/plain": [ + " All Participants Faculty, Staff Scientist, or Researcher Postdoc \\\n", + "IRAF 2.472527 3.414634 0.000000 \n", + "Julia 3.021978 3.414634 5.660377 \n", + "Java 3.571429 2.926829 0.000000 \n", + "R 4.670330 4.878049 5.660377 \n", + "Ruby 0.549451 0.487805 1.886792 \n", + "\n", + " Graduate or Undergraduate Student Software or Instrument Developer \n", + "IRAF 2.380952 0.000000 \n", + "Julia 1.190476 0.000000 \n", + "Java 1.190476 27.272727 \n", + "R 2.380952 9.090909 \n", + "Ruby 0.000000 0.000000 " + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df9[df9['All Participants'] <= 5.0 ]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot all software tools used by 5% or more of the community:" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "df9 = df9[df9['All Participants'] >= 5.0 ]" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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All ParticipantsFaculty, Staff Scientist, or ResearcherPostdocGraduate or Undergraduate StudentSoftware or Instrument Developer
Proprietary Software583.000000344.0094.0115.0030.0000
Open Source Software992.000000576.00132.0197.0087.0000
Ratio of Python to IDL Users (Python/IDL)0.9060150.751.01.320.9375
\n", + "
" + ], + "text/plain": [ + " All Participants \\\n", + "Proprietary Software 583.000000 \n", + "Open Source Software 992.000000 \n", + "Ratio of Python to IDL Users (Python/IDL) 0.906015 \n", + "\n", + " Faculty, Staff Scientist, or Researcher \\\n", + "Proprietary Software 344.00 \n", + "Open Source Software 576.00 \n", + "Ratio of Python to IDL Users (Python/IDL) 0.75 \n", + "\n", + " Postdoc \\\n", + "Proprietary Software 94.0 \n", + "Open Source Software 132.0 \n", + "Ratio of Python to IDL Users (Python/IDL) 1.0 \n", + "\n", + " Graduate or Undergraduate Student \\\n", + "Proprietary Software 115.00 \n", + "Open Source Software 197.00 \n", + "Ratio of Python to IDL Users (Python/IDL) 1.32 \n", + "\n", + " Software or Instrument Developer \n", + "Proprietary Software 30.0000 \n", + "Open Source Software 87.0000 \n", + "Ratio of Python to IDL Users (Python/IDL) 0.9375 " + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df9a = pd.DataFrame({'All Participants': list([proprietary_count_all, free_count_all, (user_counts_all[2]/user_counts_all[0])]), \n", + " 'Faculty, Staff Scientist, or Researcher': list([proprietary_count_fssr, free_count_fssr, (user_counts_fssr[2]/user_counts_fssr[0])]), \n", + " 'Postdoc': list([proprietary_count_pd, free_count_pd, (user_counts_pd[2]/user_counts_pd[0])]),\n", + " 'Graduate or Undergraduate Student': list([proprietary_count_gs, free_count_gs, (user_counts_gs[2]/user_counts_gs[0])]),\n", + " 'Software or Instrument Developer': list([proprietary_count_us, free_count_us, (user_counts_us[2]/user_counts_us[0])])},\n", + " index=['Proprietary Software', 'Open Source Software', 'Ratio of Python to IDL Users (Python/IDL)'])\n", + "df9a" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Percentage of Python and IDL users across all participants: 45.32967032967033\n" + ] + } + ], + "source": [ + "print(\"Percentage of Python and IDL users across all participants:\", (idl_vs_python_all[2]/(len(df)))*100.)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "exclusive_idl_all = (idl_vs_python_all[0]/len(df))*100.\n", + "exclusive_idl_fssr = (idl_vs_python_fssr[0]/(total_numbers['Total Numbers']['Faculty, Staff Scientist, or Researcher']))*100.\n", + "exclusive_idl_pd = (idl_vs_python_pd[0]/(total_numbers['Total Numbers']['Postdoc']))*100.\n", + "exclusive_idl_gs = (idl_vs_python_gs[0]/(total_numbers['Total Numbers']['Graduate or Undergraduate Student']))*100.\n", + "exclusive_idl_us = (idl_vs_python_us[0]/(total_numbers['Total Numbers']['Software or Instrument Developer']))*100.\n", + "exclusive_python_all = (idl_vs_python_all[1]/len(df))*100.\n", + "exclusive_python_fssr = (idl_vs_python_fssr[1]/(total_numbers['Total Numbers']['Faculty, Staff Scientist, or Researcher']))*100.\n", + "exclusive_python_pd = (idl_vs_python_pd[1]/(total_numbers['Total Numbers']['Postdoc']))*100.\n", + "exclusive_python_gs = (idl_vs_python_gs[1]/(total_numbers['Total Numbers']['Graduate or Undergraduate Student']))*100.\n", + "exclusive_python_us = (idl_vs_python_us[1]/(total_numbers['Total Numbers']['Software or Instrument Developer']))*100." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Participants who use IDL and do not use PythonParticipants who use Python and do not use IDL
All Participants27.74725320.879121
Faculty, Staff Scientist, or Researcher33.17073213.658537
Postdoc22.64150922.641509
Graduate or Undergraduate Student16.66666735.714286
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" + ], + "text/plain": [ + " Participants who use IDL and do not use Python \\\n", + "All Participants 27.747253 \n", + "Faculty, Staff Scientist, or Researcher 33.170732 \n", + "Postdoc 22.641509 \n", + "Graduate or Undergraduate Student 16.666667 \n", + "Software or Instrument Developer 31.818182 \n", + "\n", + " Participants who use Python and do not use IDL \n", + "All Participants 20.879121 \n", + "Faculty, Staff Scientist, or Researcher 13.658537 \n", + "Postdoc 22.641509 \n", + "Graduate or Undergraduate Student 35.714286 \n", + "Software or Instrument Developer 27.272727 " + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "exclusive_names = [\"Participants who use IDL and do not use Python\", \"Participants who use Python and do not use IDL\"]\n", + "\n", + "df9b = pd.DataFrame({'All Participants': [exclusive_idl_all, exclusive_python_all], \n", + " 'Faculty, Staff Scientist, or Researcher': [exclusive_idl_fssr, exclusive_python_fssr], \n", + " 'Postdoc': [exclusive_idl_pd, exclusive_python_pd],\n", + " 'Graduate or Undergraduate Student': [exclusive_idl_gs, exclusive_python_gs],\n", + " 'Software or Instrument Developer': [exclusive_idl_us, exclusive_python_us]},\n", + " index=exclusive_names)\n", + "df9b.T" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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flHC/mkwv36TXAF4+ZvPQ9C4BAAC85J55uJIzZ05lzpxZFy5csFhevHhxrVixwvg8YsSIJPs++dvC69eva+jQodq4caNMJpOKFi1qDPVJfJVzTExMssOP8ubNqxs3bqSp9u3bt2vMmDE6efKksmTJIhcXF6OmlF4dffPmTUkyers86cqVK8kuj4mJUZ48eZIsT1z2119/GctS81tUBwcHi1f0PunBgwdJetDY29tbfLaxSZiCx2w2KyYmRmazOckDfL58+Sw+37x5U2fOnJGrq2uSYz7+4P7kedy8eVMPHz5M0n7evHmTrT9LliwWn48cOaLhw4fryJEjcnBwUPHixVXg/3dzeNq9YWtr+49Ciblz52r69Om6efOm8uTJIzc3Nzk4OOju3bsp7nPz5k1dunQp2WsjJcxn4uTk9NTjuru7y93dXV26dFFsbKzmzp2r0NBQzZ49W3369EnzeSRKzbVJS/3J3UspBa0AAAAA8Kp5Ln1gq1evri1btuju3bvGA7WDg4MxtEBKeFj+u9fW9unTR6dOndK8efPk7e0tOzs7xcbGatmyZcY2OXPm1M8//5xk38TQQ/q/HhtPPuw9/mB89uxZBQYGqlatWpo5c6YKFSokk8mkxYsXa/v27SnWmDVrVknSokWLkjxgSjKGkTwpW7ZsOnr0aJLliWFMWgOAPHny6MaNG3rw4EGSUCM+Pl5Xr15NNsxJSY4cOWRjY2NMSpro8esqSY6OjipfvnyaH/Rz586tjBkz6vr16xbLnzxecm7fvq0OHTqoZMmSWr9+vd58803Z2Nho69at2rBhg8U5PNleYnCUKPHeePJevHPnjvH3devWKSQkRMHBwWrQoIERSnTv3l2//vprinVmzZpVxYoV09ixY5Ndn9J3PHbsWEVFRem7776zWO7g4KCAgABt2LDhb19r/OT5PBkCpeba/NP6AQAAAOC/5rm8Lahjx466f/++Bg8enOxwmlu3buny5ct/287+/ftVu3Zt+fr6GsMPtm3bJun/gpLy5cvrzJkzFkHF9evXdejQIeOzo6OjJOnixYvGsgcPHujw4cPG559//llxcXHy9/dX4cKFjYfuxGAl8XhPDilJ7EkTExNj9DJwd3fXxYsXNWXKlBSHRvj4+OjMmTMWNUjS+vXrlTdv3jRPXOvj46O4uDht2rQpybotW7bowYMHyQ5PSkmmTJnk7e2tDRs2WPTa2bx5c5Ljnjp1SsWKFbM4/2XLlmn9+vUptp8hQwZ5e3snqTdxno6nOXnypG7evKk2bdqoePHiRo+bxHsjsd4KFSooKirKeCuVlPB9Pn5PJndvnDhxwiJE2r9/v3LmzKn27dsbwcqdO3e0f/9+i8DuyXujXLlyunDhgvLly2dxbTZt2qSFCxcmCcESFS1aVKdOndI333yTZN2dO3d05coVOTs7S/q/3kaPc3R0TPL2oyff8JOaa/NP609OWodiAQAAAMDL5Ln0XHFxcdG4ceM0YMAANWrUSE2aNFGJEiUUFxenvXv3asWKFYqNjbWYjDQ5Hh4eWrt2rUqWLCknJycdOHBAM2fOlMlkMkKLDz/8UAsWLFCXLl3Us2dPZcmSRRERERYPvdmzZ5e3t7fmz5+vwoULK3v27FqwYIHu3btnPCC6urrK1tZW48ePV5s2bRQXF6fIyEht2bJFkozjJfZU+frrr+Xl5SUXFxe99957GjBggP744w+VLFlSv//+uyZPnixXV1djqMqTGjRooIULFyogIEDdu3eXk5OTvv76a23btk2jRo1K9qH5acqWLatq1aqpf//+OnnypLy9vRUXF6effvpJ8+bNU+3atVWmTJk0tdmrVy+1bt1aQUFBatKkiU6ePKkZM2ZYbNO2bVutXr1a7dq1U5s2bZQtWzatXr1aa9asMSY4TUnXrl3VunVr9e7dW/Xr19eJEyc0ZcoUScmHBoneeOMNOTo6Kjw8XCaTSTY2NtqwYYNWrlwp6f96aQQGBmrjxo3q2LGj2rVrp6tXryo0NNQiFPD19ZW9vb3GjBmj7t27686dO5oyZYpy5MhhbOPh4aGvvvpK48aNU9WqVXXp0iXNmTNHV69etRhakzVrVkVHR2vXrl0qVaqUGjZsqEWLFqlt27bq3LmznJyctGXLFs2dO1ddu3ZNcZ6chg0bau3atQoODtaePXtUpUoVZcuWTadPn9aCBQvk4OCg1q1bS0oIUkwmk3bt2qVixYrJ09NT1apV0+bNmxUSEqJq1aopOjpaq1evtjhGaq7NP60/OVmzZtXVq1e1detWlSxZMsnwMgAAAAB4mT2XnitSwsSna9euVcWKFfXll1+qS5cu6tGjh3bt2qWmTZvq+++/V8uWLZ/aRkhIiNzd3TVixAgFBgZq06ZNGj58uCpVqmT8Jt7Ozk7z589X2bJlNWrUKA0YMEAVKlRQtWrVkrTl5uamQYMGqX///nJ1dTUeUKWE3gITJ07UhQsX5O/vryFDhkiSFi5cKJPJpOjoaElSzZo15e7urn79+mnOnDlG261atdKiRYvUoUMHzZ49W40bN1ZERESK55Y5c2YtWrRIlStX1oQJE9S1a1edPHlSU6dOVZMmTdJ+wSVNnTpV/v7++vbbbxUQEKCePXsar7OeNGlSmtsrW7asvvjiC128eFFdu3bVsmXLNGbMGIttnJyctGTJEuXLl0+DBw9WQECAjh8/rkmTJqlhw4ZPbd/X11eTJ0/W0aNH1aVLF0VGRhpvhnraPDNZs2ZVeHi4Hj58qKCgIPXt21cXL17UokWLlCVLFuPeeP3117Vo0SJJUo8ePRQREaG+ffsqe/bsRlvZsmXT1KlT9fDhQwUGBio0NFSBgYFyc3MztmnQoIECAwO1fv16dejQQVOnTlXZsmU1YsQIXbt2TadOnZIkNW/eXBkzZlTHjh21Y8cOZcmSRYsXL5anp6dCQkLUqVMnbd++XYMHD1a3bt1SPD87OzvNnTtX3bt31y+//KLg4GC1adNG4eHh8vHx0YoVK4xQJ3PmzGrbtq02btyoTp06KT4+Xo0aNVLHjh21bt06derUSQcPHjRCq0SpuTb/tP7kNGjQQAULFlRgYKDWrl2bpn0BAAAA4EVnMqc0UyvwnG3atEkFCxa0eBPQ1q1b1alTJ61ZsybJG4Lw33Ls2DG5uCxJ7zIA/Ae8SG8Leny+OuDfxL2H9MK9h/TyrO+95zIsCEiNrVu3atOmTerTp48KFy6sP/74Q1OmTJGPjw/BCgAAAADgpUG4gnTTv39/2dnZacqUKfrzzz+VO3du1apVSz169Ejv0gAAAAAASDWGBQF4ITEsCPh/7d15fA/X/sfxVyyxCxFLLbGkksgiYq0tIfaiwbU2ova1LlpLuCqUn70XEaS4VTtRW1pLbA2ldmorbmsLJVwSS+xkfn94ZK6vBOEbjbrv5+ORx0POzJz5zJkj+c4n55yRP4umBYmo70naUd+TtJLafe+NLWgrIiIiIiIiIvK/QMkVERERERERERErKLkiIiIiIiIiImIFJVdERERERERERKyg5IqIiIiIiIiIiBWUXBERERERERERsUKGtA5ARCQ5hmG8Va9HFZF31717j8icWR+JRERE5PVp5IqIiMhT7ty5k9YhyJ9MiRURERGxlpIrIiIiIiIiIiJWUHJFRERERERERMQKSq6IiIiIiIiIiFhByRURERERERERESsouSIiIiIiIiIiYgUlV0RERERERERErKDkioiIiIiIiIiIFZRcERERERERERGxgpIrIiIiIiIiIiJWUHJFRERERERERMQKSq6IiIiIiIiIiFhByRURERERERERESsouSIiIiIiIiIiYgUlV0RERERERERErKDkioiIiIiIiIiIFZRcERERERERERGxgpIrIiIiIiIiIiJWUHJFRERERERERMQKSq6IiIiIiIiIiFhByRUREeOetWQAACAASURBVBERERERESsouSIiIiIiIiIiYgUlV0RERERERERErKDkioiIiIiIiIiIFZRcERERERERERGxgpIrIiIiIiIiIiJWUHJFRERERERERMQKSq6IiIiIiIiIiFhByRURERERERERESsouSIiIiIiIiIiYgUlV0RERERERERErKDkioi8lWxsbNI6BPkflTVr1rQO4X+SYaR1BCIiIiKvL0NaByAi8jwXL6Z1BCLyZylYMK0jEBEREXl9GrkiIiIiIiIiImIFJVdERERERERERKyg5IqIiIiIiIiIiBWUXBERERERERERsYKSKyIiIiIiIiIiVlByRURERERERETECkquiIiIiIiIiIhYQckVERERERERERErKLkCGIaR1iG8s96ltn2XrkVERERERERSz1ufXDl58iT9+vWjatWqeHh4UK1aNfr27cuJEydeua7Hjx8zZMgQvL29KVu2LHv37mX58uWMGzfuDUT+1+Hi4sL06dNTtc5bt24RFBTEvn37UrXetHLw4EG6dev2wn1WrFiBi4uLxZenpye1a9dm1KhRxMbG/knRJvUm7rGIiIiIiIg8kSGtA3iREydO0KZNG8qWLcsXX3yBvb09MTExzJs3j5YtWzJv3jzKlCmT4vp27NjB8uXL6dmzJ1WqVMHd3Z0hQ4ZQrly5N3gV/5tOnjzJypUradq0aVqHkiq+++47fv/99xTtO2PGDOzt7TEMg7t373Ls2DFmzZrFtm3bWLJkCfb29m84WhEREREREfkzvdXJlblz55InTx5mzpxJ+vTpzfJatWrRoEEDpk+fzsyZM1Nc3/Xr1wFo1qwZRYoUSfV4RQDc3NwoUKCA+X2VKlWoVq0aLVu2ZOLEiYwePToNoxMREREREZHU9lZPC7p27RqGYZCQkGBRni1bNoYMGUKDBg0syletWkXTpk0pU6YMPj4+jBs3jnv37gEQFBTEgAEDAKhduzaBgYH4+fkRHR3NypUrcXFxYe7cubi4uHDy5EmzzkWLFuHi4sLatWvNsp9//hkXFxfOnz8PQGRkJG3atMHb2xsPDw8aNGjAokWLzP13796Ni4sLS5cupUaNGpQrV469e/cCsHfvXgICAvDy8qJSpUoMHTqUmzdvvrRtXnStidfbsWNHvvjiC7y9vfH390/xmiF+fn6EhoYyduxYqlSpgpeXF506deLcuXPmPrGxsXz++edUrVqV0qVL4+/vz6pVq8zrDQgIAKBdu3YEBgYCEBgYyKBBg+jVqxdeXl50797dbJtnpw8FBgbSvn178/vE9uvfvz/e3t588MEHhIaGEh8fz+DBgylXrhxVq1ZlwoQJFtcZFxfH0KFDqVy5MqVLl6ZNmzbs37/f4lwuLi4sWbKEwYMHU6FCBby9venTpw/Xrl0z2/K7777jjz/+wMXFhRUrVqSoHZ9WqlQp6tWrR0REBHfv3jXLX3T///jjD1xdXVmyZIlFXZcuXcLV1ZXly5cDcO/ePcaNG4ePjw+enp40adKEzZs3vzCemJgYBg4cSPXq1fHy8iIgIIA9e/aY2y9cuGD2+86dO+Pl5UWtWrWYO3euRT0JCQmEhYVRu3ZtPDw8qF+/PsuWLbPYJ7n7LiIiIiIi8i55q5MrPj4+XLhwgdatW7Nw4UJOnTplbqtfv77FlJOQkBCCgoKoUKECoaGhdOjQgSVLltC9e3cMw6Bnz5707t0bgNDQUIKDgwkNDaVAgQL4+vqydOlSmjVrhq2tLTt37jTr3bVrF4CZDAHYtm0bTk5OFClShM2bN/P3v/+d0qVLM336dKZOnUrhwoUZMWIEhw8ftrieadOmMWTIEIYOHYqXlxd79+6lQ4cOZMuWjSlTpjBw4ECioqLo1KkTjx49em67vOxaE+3evZv//Oc/TJ8+nb59+2JjY5Pitv/22285c+YMY8aMYeTIkRw9epTBgweb2wcMGMCpU6cYMWIEM2fOxM3NjUGDBrF7927c3d358ssvARg2bBjBwcHmcT/88AO5cuUiLCyMTz75JMXxAIwbN47cuXMzffp0atasydSpU2nevDlZsmQhNDSUOnXqMHv2bDZs2ADA/fv3ad++PVFRUXz22WeEhIRgZ2dH+/btk9ybiRMnAjB58mQGDBjAjz/+yNixYwHo2bMnfn5+5M2b10yQvY4qVarw8OFDjhw5AvDS+1+oUCHKly/PmjVrLOpZs2YNmTJlol69ehiGwaeffkp4eDidOnVi2rRplCpVil69erFp06Zk47hy5QrNmzfn0KFDDBw4kEmTJpE5c2Y6dOhg0fcBgoODyZcvH1OnTqVmzZqMHj2aefPmmduHDx9OaGgoTZs2JSwsjJo1a/LFF18wf/58i3qsue8iIiIiIiJvu7d6WlBAQAD/+c9/mDNnjvmwbm9vT7Vq1QgMDKR06dLAk+k+s2bN4uOPP2bIkCEAVKtWjfz589OvXz+2bt1KjRo1zKlApUqVonDhwgDY2tpib29vrt1SsWJFdu3aRfv27TEMw0wWPJ1c+emnn8wH7FOnTtGsWTOLxIO3tzeVKlViz549ZowAbdu2pW7duub3X331FU5OToSFhZEu3ZM8l5ubG02bNmXt2rV89NFHSdokpdcK8OjRI7788kvy5cv3ym2fK1cupk+fbk7Hio6OZurUqdy6dYscOXKwZ88eevXqRe3atc12y5UrFxkzZiR79uw4OTkB8P777/P++++b9WbKlIng4GBsbW2BJwmglHJ3d+cf//gHAK6urqxYsYI8efIwbNgwAD744AO+//57fvnlF+rVq8fq1as5efIky5Ytw9PTE3iSsGvevDmTJk1izpw5Zt2urq6MGTMGgKpVq3LkyBEzOeHo6Ii9vT22travtMbPs/LkyQPA1atXgZTdf39/f4YNG8aVK1fM+7h27Vr8/PzInj07O3bs4KeffiIkJIR69eqZ13jz5k0mTJhg3p+nzZkzh5s3b7Js2TLee+89AGrUqIG/vz8TJ040R8QAeHl5mdOYfHx8uHLlCmFhYQQGBnL27FnCw8MZOHAgHTt2BJ70xcePHzNlyhQz8QVJ77uIiIiIiMi75K1OrtjY2NCvXz86duzITz/9xM6dO9m9ezcRERF8//33fPHFFwQEBHDo0CEePHhAw4YNLY6vX78+AwcOZPfu3SkebeDj48OUKVN49OgRv/32Gzdu3CA4OJjPPvuM2NhY7t27x++//87w4cMB6Nq1KwC3b9/mzJkzREdHmyMTHj58aFG3q6ur+e+7d+9y6NAhunbtSkJCgjn1qWTJkhQsWJCff/452eTKq1yrg4PDayVW4MlD9dPr3CSuIXLnzh1y5MhBpUqVmDp1Kr/++ivVq1fH19eXQYMGvbTe999//7UfsJ9OVOXOnZv06dNblNnY2GBnZ2dOq9m5cyf58+enVKlSFiOBatasyddff82DBw/MWMqWLWtxrgIFClhM30ltKb3/9evXZ+TIkaxbt45PPvmEs2fPcuzYMXMU1s6dO0mfPj0+Pj4W1+jn58emTZu4cOGCmUhMtG/fPsqVK2cmVgDSpUvHhx9+yJQpU4iPjzfLGzVqZHFs3bp1iYyM5PTp0+zZswfDMKhZs2aSc8+dO5fDhw9TqVIl4PXuu2EYFCz4SoeIyCu4d+8RmTO/PR8DDANeYYCliIiIyFvl7flU9QJ2dnY0atTIfND79ddfGThwIOPGjaNRo0bcuHEDgLx581ocly5dOuzt7S0eFl+mRo0ajB49miNHjvDLL7/g4uJCjRo1SJ8+Pfv27SMuLg47OzvzYTw2Npbg4GA2bdqEjY0NRYsWpXz58gBJ1jjJmjWr+e+bN2+a61WEhYUliePKlSvJxvcq1/r0+V5V5syZk9QP/72mSZMmERYWxrp164iMjCRdunRUqVKFL7/8kkKFCj23XmtiypYt2yvVd/36dWJiYnB3d092e1xcHPnz5weSv95n1/qxVuI9zZcvX4rvf44cOahZsyZr167lk08+4YcffsDe3p7q1asDT67x8ePHzx1Rc+XKlSTJlRs3blCsWLEk+zo4OGAYBrdv3zbLEtsnUeLom5s3b5oLRNevX/+F1wuvd99tbGywsRnxyseJSMoYRvDLd/oTKbEiIiIif2VvbXIlJiaG5s2b06dPH1q0aGGxzc3Njb59+9KrVy8uXLiAnZ0dAP/5z39wdHQ090tISCA2NpbcuXOn+LxFixalWLFi/Pzzzxw5coSKFSuSNWtWPD092bNnDzExMVSvXt0c1dG/f3/OnDnDt99+i7e3N7a2tty9e5fw8PAXnidbtmzY2NjQsWPHJAvzJm5PTmpeqzVy5MjBgAEDGDBgAKdPn2bz5s1Mnz6dkSNHJpsseJ7EdWCeTWTcvn2bnDlzWh2jk5MT48aNS3b7n9VWiXbt2kWWLFlwd3fn8ePHKb7//v7+9OjRg0uXLrFu3To+/PBDMmR48l83R44c5MiRw2KK09OKFy+epCxnzpzm1KSnJSZDcufObf47Li7OYp/E4/LkyUOOHDkAWLBgQZLkFJAkqSMiIiIiIvKuemsXtM2bNy/p06dn0aJF3L9/P8n206dPkyVLFhwdHfHy8sLW1jbJwp/r16/n4cOHlCtX7rnnSRyR8TRfX19+/vlnDh48aE5rSFyLZdeuXRZTjPbv30/9+vWpVKmSOe1h27ZtQNKEwdOyZ8+Om5sbZ8+exdPT0/wqXrw4kydP5tChQ8keZ821ppaYmBh8fX1Zv349ACVKlKBLly5UqVKFS5cuAVhMKXqR7NmzA5jHwZORFU8vXvy6KlSowMWLF8mXL59FG2/evJn58+eTMWPGFNeV0ut5npMnTxIZGUmTJk3IkiXLK93/6tWrkzt3bmbPns3vv/+Ov7+/xTXeunWLDBkyWNRz+PBhZsyYkewixhUqVGD//v3ExMSYZQkJCaxfvx5PT0+L6TtRUVEWx0ZGRlKoUCEcHR3NEVo3btywOPelS5cICQl5o9OqRERERERE3iZv7ciV9OnTM2zYMHr37s3f/vY3AgICcHJy4u7du+zYsYOFCxfy2WefmX8979SpE2FhYWTIkAFfX19+++03pk6dSsWKFc0pFMnJmTMnv/76q7n4bObMmalRowZz587FxsbGfICsVKkSX3/9tbm+RaLSpUsTERFBqVKlyJ8/PwcOHGDmzJnY2Ni89OGyT58+dO/enaCgID788EMePHjArFmz+Pe///3c9Uty5cr12teaWgoUKEChQoUYNWoU8fHxODo6cvToUbZu3UrPnj0BzFEnUVFR2NnZWaw38zQXFxfee+89pk6dao7W+Prrr82FUK3RrFkzFixYQIcOHejWrRv58+cnKiqKOXPm8Omnn77S25Ny5MjB1atX2bp1K6VKlXrhWja//vqrmbi4c+cOR48e5ZtvvsHR0ZF+/fqZ+6X0/mfMmJGGDRuyePFiihUrZrHOTI0aNShbtizdu3enZ8+eFCtWjAMHDjBt2jQaNWqU7AioDh06sHr1aj755BN69+5NtmzZWLRoEadOnWLmzJkW+/7www84ODhQpUoVtmzZwsaNG5kwYQLwZA2hRo0aMWTIEM6fP0+pUqX47bffmDRpEu7u7hTUgikiIiIiIvI/4q1NrgDUqlWL8PBw/vWvfxEWFsa1a9fIlCkTbm5uTJ48mTp16pj79u3bFwcHBxYsWMCiRYtwcHCgVatW9O7dO9nRKYk6dOjAqFGj6NSpE3PnzqVs2bKUL1+erFmz4ujoSK5cuYAnC55mzJgRLy8vc2oOwNixYxk5cqT5NqNixYoxYsQIIiIi2L9//wuvz9fXl9mzZxMaGkrv3r3JlCkTnp6ezJs3D2dn5+ce97rXmpqmTp3KxIkTmTJlCnFxcbz33nv07t2bLl26AE+mozRq1IiFCxeyfft2vv/++2TrSZ8+PSEhIYwePZp+/frh4ODAJ598wunTp4mOjrYqxmzZsrFw4UK++uorxo4dy+3btylSpAhffPEFbdu2faW6mjZtypYtW+jVqxd9+/alc+fOz923R48e5r9tbW0pUqQILVu2pEuXLmYyEF7t/vv7+7NgwQIaN25sUZ4uXTpmzZrFlClTCA0NNe9F9+7d6datW7Lx5cuXj8WLFzNx4kSCg4NJSEjAw8ODOXPmmCO1EvXt25ft27ezYMECHB0d+ec//2mxmPLYsWMJCwtjwYIFXL58GQcHB5o3b87f//73lzeqiIiIiIjIO8LGeHbVVRH5n3fhwgVq1arF+PHjLaYh/ZlOnjyJq+uSNDm3yP+Ct21BW3nizp07Vi3+LvK61PckrajvSVpJ7b731q65IiIiIiIiIiLyV6DkioiIiIiIiIiIFd7qNVdEJG0ULlyYkydPpnUYIiIiIiIifwkauSIiIiIiIiIiYgUlV0RERERERERErKDkioiIiIiIiIiIFZRcERERERERERGxgpIrIiIiIiIiIiJWUHJFRERERERERMQKehWziLyVDMPAMILTOgyRd9a9e4/InFkfA0RERERSg0auiIiIPOXOnTtpHcKfQokVERERkdSj5IqIiIiIiIiIiBWUXBERERERERERsYKSKyIiIiIiIiIiVlByRURERERERETECkquiIiIiIiIiIhYQckVERERERERERErKLkiIiIiIiIiImIFJVdERERERERERKyg5IqIiIiIiIiIiBWUXBERERERERERsYKSKyIiIiIiIiIiVlByRURERERERETECkquiIiIiIiIiIhYQckVEREREREREREr2BiGYaR1ECIiIiIiIiIif1UauSIiIiIiIiIiYgUlV0RERERERERErKDkioiIiIiIiIiIFZRcERERERERERGxgpIrIiIiIiIiIiJWUHJFRERERERERMQKSq6IiIiIiIiIiFhByRURERERERERESsouSIiIiIiIiIiYgUlV0TkrfLDDz/QsGFDSpcuTYMGDVi1alVahyTvoISEBBYvXkzjxo3x9vamdu3ajBkzhvj4eHOfI0eOEBgYiLe3N9WqVeOf//wnDx8+TMOo5V306aefUqdOHYuy7du387e//Q0vLy/8/Pz45ptv0ig6edfs3buXNm3a4OXlRbVq1Rg5ciS3b982t6vvyZuyePFiGjRoQJkyZWjcuDEREREW29X3JLUdP34cd3d3YmJiLMpT0tde9zNg+uHDhw9PrQsQEbHG2rVr+fzzz/noo4/o0aMH9+/fZ/LkyZQsWZL3338/rcOTd8isWbMYP348zZo1o1u3bhQrVoy5c+dy4MAB/P39OXfuHG3atKFw4cIMGjSIYsWKMWPGDGJjY/H19U3r8OUdsXr1ar7++mvs7Oxo164dAAcOHKBTp05UrFiRvn37kiNHDkJCQsiSJQtly5ZN44jlr+yXX37hk08+wcPDg6CgIJydnfn222/5/fffqV+/vvqevDFLly4lODiYpk2b0rNnTxISEpgwYQLvv/8+JUuWVN+TVHfq1Ck6d+7MrVu36NChA9mzZwdS9jvWms+ANoZhGG/86kREUqBOnTp4eHgwadIks6xv376cPHmSdevWpWFk8i4xDINKlSrRsGFDgoODzfK1a9fSr18/Vq1axYIFC9ixYwcbNmzA1tYWgEWLFjFq1Ch+/PFH8ufPn1bhyzvi8uXLNG7cmCxZsmBra8vGjRsBaN++PXfu3CE8PNzcd8KECYSHh7Njxw6zP4q8qrZt2wIwf/58bGxsAFi4cCFz5szh+++/p0ePHup78ka0bt0aW1tb5s2bZ5YFBASQLl065s+fr597kmoePXrE0qVL+eqrr8iYMSPXr19n69atFChQAEjZ79h//OMfr/0ZUNOCROStcP78eaKjo6lbt65Feb169Th9+jTnz59Po8jkXXP79m0++ugjGjVqZFFeokQJAKKjo9mxYwc1a9a0+EBXv359Hj9+zPbt2//UeOXdNHToUKpWrUrlypXNsvv377Nv375kfw7evHmTAwcO/NlhyjsiNjaWffv20aZNGzOxAk8ecDdt2kS6dOnU9+SNuX//PtmyZbMoy5UrF9evX9fPPUlV+/fvZ+LEiXTs2JH+/ftbbEtpX7PmM6CSKyLyVjh9+jQAxYsXtygvWrQoAGfOnPnTY5J3U/bs2Rk6dCjlypWzKN+0aRMATk5OXLp0KUlftLe3J3v27OqLYrVly5Zx7NgxvvjiC4vy8+fP8/DhQ/0clFT373//G8MwsLOzo2/fvpQpU4Zy5coRHBzMvXv31PfkjWrXrh0//fQT69atIz4+nvXr1xMVFYW/v7/6nqQqJycnNm3axKeffkr69OkttqWkr929e9eqz4AZUuEaRESsduvWLQBzTmSixL90PL3QqEhqO3ToEDNnzqR27drkzJkTSNoX4Ul/VF8Ua/zxxx+MGTOGMWPGYG9vb7FNPwflTYmNjQUgKCiIOnXqMGPGDE6ePMnkyZO5f/8+rVq1AtT35M1o2LAhu3btom/fvmZZ06ZN6dy5MwcPHgTU9yR1ODg4PHdbSn7HPm+fxP1e1h+VXBGRt8LLln9Kl04D7eTN2L9/P927d6dw4cKMGjWKBw8evHB/9UV5XYZhMGTIEHx9falXr16y219EfU9eV+JbLsqWLWuuNVW5cmUMw2DcuHG0bNnyhcer74k1evTowcGDBxk8eDBubm4cOnSI6dOnkz17dj788MMXHqu+J6klJb9jrf09rOSKiLwVcuTIAWDxSkj4718sEreLpKa1a9cSFBREsWLFmD17Nrlz5zb74LN9EZ70R/VFeV0LFy7k5MmTfP/99zx69Aj474e9R48e6eegvDGJf5n18fGxKK9WrRpjx47lyJEjgPqepL4DBw6wfft2xowZQ7NmzQCoWLEiOXPmZNiwYTRv3hxQ35M3LyW/YxNHrLzuZ0AlV0TkrZA4tzE6OhoXFxez/Ny5cxbbRVLLnDlzGDduHBUrVmTatGnmL8xs2bKRP39+s+8lunbtGrdv31ZflNcWGRlJXFwc1apVS7LN3d2d4cOHkz59eqKjoy22JX6vvievq1ixYgBJRuYljmgpXLiw+p68ERcvXgRI8krl8uXLA3D8+HH1PflTODo6vrSvWfsZUOOsROStULRoUQoXLsz69estyjds2ECxYsUoWLBgGkUm76Jly5YxduxYGjRowOzZs5P8JaJq1ar8+OOPFg8ikZGRpE+fnooVK/7Z4co7YsSIEXz33XcWXzVr1qRAgQJ899131K9fn/Lly7NhwwaLocmRkZHkyJEDDw+PNIxe/sqcnJwoVKgQa9eutSj/8ccfyZAhA97e3up78kYkPozu37/fovyXX34BnrypT31P/gyZMmVKUV+z5jNg+uHDhw9/I9GLiLyiHDlyMGPGDOLi4rCxseGbb75h1apVBAcHU7JkybQOT94R165do3PnzuTPn5/PP/+ca9euERMTY37Z2tri5ubGN998w759+7CzsyMqKooJEybQokULGjdunNaXIH9RuXPnJn/+/BZf27dv58qVK/Tv358sWbJQoEABwsLCOHXqFFmyZGHVqlXMmjWL3r17U6lSpbS+BPmLsrGxIW/evMyZM4ezZ8+SPXt21q1bx7Rp02jbti1169ZV35M3Il++fJw4cYJFixaRKVMm7t+/T2RkJF999RWVK1emS5cu6nvyRhw/fpzNmzfToUMHc7pPSvpa8eLFX/szoI3xslVbRET+REuWLOGbb77h0qVLFClShK5du9KkSZO0DkveIatWrWLQoEHP3T5+/Hj8/f3Zt28f48eP5/jx4+TOnZsmTZrQu3dvMmbM+CdGK++6oKAg9u/fz8aNG82yjRs3EhISwpkzZ8ifPz8BAQF07NgxDaOUd8WmTZuYNm0av//+O3ny5KFVq1Z069bNXKRRfU/ehAcPHhAaGkpERATXrl2jUKFCNGrUiK5du2Jrawuo70nqW7FiBYMHD2br1q0UKFDALE9JX3vdz4BKroiIiIiIiIiIWEFrroiIiIiIiIiIWEHJFRERERERERERKyi5IiIiIiIiIiJiBSVXRERERERERESsoOSKiIiIiIiIiIgVlFwREREREREREbGCkisiIiIiTwkKCsLFxcXiq1SpUpQtW5YWLVqwcuXKtA4xVVy7do07d+6kdRjJmjp1Ki4uLly4cCGtQ3ktb1P8CQkJb0UcIiLvugxpHYCIiIjI22jw4MHkzp0bAMMwiI+PJyIigqCgIOLi4ujYsWMaR/j6tm7dSv/+/Vm5ciVZs2ZN63CSqFOnDo6Ojtjb26d1KH9p8fHxtG/fHl9fX3r37p3W4YiIvNOUXBERERFJRu3atSlcuLBFWfPmzfnwww+ZNm0abdu2xdbWNo2is87hw4e5efNmWofxXK6urri6uqZ1GH95169f58iRI/j6+qZ1KCIi7zxNCxIRERFJocyZM+Pn50d8fDy//fZbWocjIiIibwklV0RERERegY2NDQCPHz82yw4ePEiHDh3w9vbG29ubjh07cvjwYYvj/Pz8GDp0KEOGDKF06dL4+PgQGxsLwKFDh+jSpQvly5enUqVKdO3alZMnT1ocn9JzDBs2jNWrV9OwYUM8PT2pW7cuCxcuNPcJCgoiNDQUgFq1ahEYGGhuW7duHW3btqVcuXJ4eHjg5+fH+PHjefDggcV5Dh06RLt27fD29qZ69epMnTqV0NBQXFxcLPaLiYlh4MCBfPDBB3h6etKkSRMiIiJe2sbPrlkydepUPD09OXv2LN26dcPb25sKFSowaNAg4uLiXlrfjRs3GDlyJNWrV8fDw4MGDRowd+5cDMOw2O/YsWP07t2bKlWq4O7uTuXKlfn888+JiYmx2C8+Pp7Ro0dTo0YNvLy8aNy4McuWLUty3ujoaLp37463tzcVK1YkKCiI69evvzTelNT/slh3795NrVq1AMx7k9ie9+/fZ9KkSfj5+eHh4UGtWrWYMmVKkvscHx/PiBEjqFatGmXKlKF79+7s27cPejEBNAAAD2hJREFUFxcXVqxYYe73+PFjZs+eTb169fDw8KBatWoEBweb/TsxHhcXF1auXEnjxo3x9PRk8ODB+Pj40Lx58yRtsG3bNlxcXIiKinppe4mIvA00LUhEREQkhRISEtizZw+2trY4OTkBsGPHDrp164arqyt9+vThwYMHrFixgoCAAObMmUP58uXN49esWUOJEiUYMmQIV69exd7enn379tG+fXvy5ctH586dyZw5M/PmzaNdu3YsX76cwoULv9I5fvrpJ9avX0/btm1xcHBg6dKlfPnllxQuXBhfX19atWpFfHw8GzduZPDgwZQsWRKAZcuWMXToUPz8/Ojfvz8PHz5k48aN/Otf/wJg4MCBABw9epR27drh4OBAr169uHv3LvPmzSNdOsu/2V2+fJkWLVpgGAaBgYHY2dmxefNmBgwYwJUrV+jcufMrt327du0oX748gwYN4siRI3z33Xfcu3ePKVOmPPe4O3fu0LZtWy5dusTHH39MgQIF2LVrF6NHj+bs2bMEBwcDcPLkST7++GOKFi1K165dyZIlCwcOHGD16tWcO3eO7777DoAHDx4QEBDAb7/9RsuWLXF1dWXr1q0MHTqUu3fv0q5dO/PcPXv2pFatWgQFBXHgwAFWrlzJzZs3mT59+nPjTUn9KYnVycmJwYMHM2bMGOrUqUOdOnWwt7fn8ePHdOvWjQMHDtCyZUucnJw4evQoYWFhHD9+nBkzZmBjY8Pjx4/p3LkzR44coU2bNhQtWpQ1a9bQq1evJDH369ePyMhI6tatS7t27Thz5gyLFy9m165dLFu2jJw5c5r7fvnllzRr1owWLVpQsGBB7OzsmDNnDhcuXLCYhrdmzRpy5cpF1apVU95JRETSkiEiIiIipkGDBhnOzs7GsWPHjGvXrhnXrl0zrly5Yhw8eNDo06eP4ezsbIwePdowDMN4/PixUatWLaN169bGo0ePzDpu375t1KlTx/D39zfLatasabi6uhoxMTEW52vevLlRtWpVIzY21iw7ffq04erqaowbN+6Vz+Hi4mIcP37cLLty5Yrh4uJifPbZZ2ZZSEiI4ezsbJw/f94sq1+/vtGqVSsjISHBLHv48KHh4+NjNGrUyCxr166dUaFCBePatWtm2bFjxwxXV1fD2dnZoh0rVqxoXL582SxLSEgwPvvsM8PDw8O4evXqc+/Bs/Elfj9mzBiL/Tp16mS4ubkZd+7ceWFd7u7uxokTJyzKv/rqK8PZ2dlsq2HDhhleXl5GXFycxX79+vUznJ2dzfKFCxcazs7ORkREhMV1ffzxx0bVqlWNx48fm/GOHDnSoq7AwEDD3d3duH///nPjTUn9KY31/PnzhrOzsxESEmLus3z5csPZ2dnYtm2bxbFLliwxnJ2djY0bNxqGYRgrV640nJ2djfDwcHOfBw8eGM2bNzecnZ2N5cuXG4ZhGFu3bjWcnZ2NUaNGWdS3du1aw9nZ2Rg3bpxhGIaxa9cuw9nZ2ejUqZPFfkePHjWcnZ2NmTNnmmX37983ypYtawwbNuy57SQi8rbRtCARERGRZDRt2pTKlStTuXJlqlWrRqtWrdi8eTOBgYF8/vnnAPz666+cP3+e2rVrc+PGDWJjY4mNjeXevXvUrFmT48ePc/nyZbNOR0dH8ufPb35/7do1Dh8+TOPGjc03EwEUL16c5cuX06VLl1c+R/HixS0Wg82bNy8ODg5cvXr1hdcbERHBzJkzzWlPifHlzJnTfGXzjRs32LNnDx999JHFm3zc3NwsRhgkJCSwadMmypcvT4YMGcyY4+LiqFu3Lg8ePGDHjh0pvheJGjRoYPF9qVKlePTo0Qun2mzYsAFnZ2fy5s1rxhEbG0vt2rUB+PHHHwEYPnw4W7ZsIVeuXOax8fHxZMqUCcBsg6ioKOzt7WnUqJG5n42NDePHj2fhwoUW7ff0PgCenp48fPjwhVOZUlJ/SmN9XnvY29vj7u5u0R6+vr6kT5/enIazadMm7OzsaNasmXlsxowZ6dChg0V9W7ZsAaBbt24W5Q0aNKB48eJs3rzZorxChQoW37u7u1OiRAnWrVtnlm3dupX4+Pgk7Sci8jbTtCARERGRZEyYMAEHBwcA0qVLR86cOXFycjIfYOHJmhoA48ePZ/z48cnWc/HiRTOhkidPHottf/zxBwBFixZNcpybmxsAO3fufKVzJPf6YltbWxISEp5zpU9kzJiRvXv38sMPP3D69Gmio6O5du0aAIUKFQLg/PnzJCQkJBtviRIl+OmnnwCIi4vj1q1bbNq0iU2bNiV7vkuXLr0wnuQ8e22Jb2t6ev2bZ0VHR3Pv3j0qV678wjhsbGyIi4vj66+/5uTJk0RHR3Px4kVzXZbE9vvjjz9wdHS0SKLAf9voac/e78yZMwPw8OHD58ab0vpTEmtyoqOjiY2NfWl7nDt3jsKFC5M+fXqL7SVKlLD4/sKFC+TMmdP8v/I0Jycntm3bZlGWXP9s1KgRISEhnD9/niJFirBmzRree+89i+luIiJvOyVXRERERJJRtmzZJK9iflbiQ2yfPn0oU6ZMsvs8/TD67INq4vHPPkhbc45n1z5JqZEjR7JgwQLc3NwoU6YM/v7+eHt7M3LkSPOB+9GjRwDJvoL66aRTYrKjXr16tG7dOtnzFSlS5JVjfFE7Pc/jx48pV64cn376abLb8+XLB8DatWvp378/+fLl44MPPsDHxwcPDw+2b9/O119/bVFfSuN43XhfdlxKY31e/cWKFTPXmnlW4vooDx8+JFu2bEm2P3vvjWcWBX5aQkICGTNmtCh79v8AQOPGjQkJCTEXVI6KiqJNmzav1X4iImlFyRURERGR15Q4miBr1qxUqVLFYtvhw4e5ceOGOVohOe+99x7w3xEwT5swYQJ2dnbmNIrXPUdK/PHHHyxYsAB/f/8ko2Oenk6UmBA5e/ZskjrOnTtn/tve3p4sWbLw6NGjJDFfvHiRX3/9lSxZslgVc0oVKlSI27dvJ4njxo0b7Ny50xyF89VXX1G0aFGWL19O1qxZzf2+//57i+MKFiyY5E1O8GQqy9q1axkwYIBV8aak/pTGmpzChQtz9OhRPvjgA4tEXOICxgUKFACe3OsjR45gGIZFkuPp+wxP2nf79u1cvXo1yeiVM2fOmH38RRwdHSldujRbtmyhRIkS3L17l8aNG7/0OBGRt4nWXBERERF5TR4eHuTNm5f58+dz+/Ztszw+Pp6+ffsyePDgZP9Snyh//vy4urqyZs0a4uPjzfLz588zb948rl69avU5kpP4UJ046uDGjRsAvP/++xb7bd26lbNnz5ojVvLkyYO3tzc//PCDeUxivE9P/8iQIQM+Pj5s3bqVEydOWNQ5duxYevXqlaJXKKcGPz8/Tpw4wdatWy3KZ8yYQZ8+ffjtt98AuH79OgULFrRIVly6dIkNGzYA/x2N4+Pjw9WrV9m4caNFfXPnziUqKspi7ZzXkZL6UxprYr94epqQn58f169fZ/HixRb1L1myhH79+pnT0OrUqUNcXJzFWigJCQksWbLE4jg/Pz+AJCNmNm3axJkzZ6hRo0aKrrtx48YcPnyYiIgISpQoYU6LExH5q9DIFREREZHXlDFjRoYOHUq/fv1o1qwZzZs3J1OmTCxbtoyLFy8yceJEMmR48cetwYMH07lzZ/72t7/RokUL0qVLx4IFC8iZMyddunRJlXM8K3Hdi9mzZ+Pj40P16tUpWLAgYWFh3L9/nwIFCnD48GFWrlxJpkyZLJI6gwYNIjAwkObNm9O6dWsePHjA/Pnzk6zz0b9/f3bv3k1AQAABAQEULFiQqKgofvzxR1q1amW+AvpN69atGxs2bKBXr160bt2akiVLsn//flavXo2Pjw8+Pj7Ak6TG2rVrGTZsGJ6enly4cIHw8HDu3r0LYLZB69atWb58Of369SMgIIDixYsTFRXFjh07GD169Csnup6VkvpTGmuuXLlIly4dmzdvpmDBgtStW5cWLVqwcuVKRo4cybFjxyhdujT//ve/Wbp0Ke7u7uYCtk2bNmXJkiUMHDiQgwcPUqxYMSIjI/nll1+A/0558vX1pVatWsybN4/Lly9TqVIlzp49y+LFiylSpEiShW6f58MPP2Ts2LFERkbSu3dvq9pQRCQtKLkiIiIiYoX69etjZ2fHjBkzmD59OunSpaNkyZLMmDGDmjVrvvT4Dz74gLlz5xISEsK0adPIlCkTFSpUYMCAAeTNmzdVzvGshg0bsmHDBlasWMGePXuoVasWM2fOZOzYscybNw/DMHB0dGTIkCE8evSI//u//+Po0aN4eHjg7e3N7NmzmTRpEpMnTyZXrlwEBgZy6tQpIiMjzXM4OjoSHh5OSEgI4eHh3LlzhyJFijB48GACAwNfOebXlStXLpYuXUpISAjr169n6dKlFCxYkJ49e9K1a1dzFM/w4cPJmjUrW7ZsYfXq1RQoUIAmTZpQp04d2rRpw65du3BzcyNz5szMnz+fyZMns2bNGm7duoWTkxOTJ09O8jaj15GS+lMaa5YsWejXrx//+te/GDVqFI6OjlSqVIlvv/2WadOmERkZSUREBPny5aNNmzb06tXLnK6VMWNGZs+ezYQJE4iIiOD+/ftUrVqVESNGEBQUZK69YmNjw5QpU5g1axarVq1iy5Yt5MmTh1atWtG7d29zDZeXcXBwoHLlymzfvl1vCRKRvyQb40WrUImIiIiIPCW5tTUAunfvzokTJ8xX+cpf2/Xr18mWLVuSBWkjIyP5+9//zrfffvvcNw69rs6dO3Pjxg2WLVuWqvWKiPwZtOaKiIiIiKRYixYt6NSpk0XZ1atX2b17N6VLl06jqCS1zZs3jzJlyhATE2NRvmbNGjJkyJDqa6KcO3eOnTt3mtOSRET+ajRyRURERERSbNKkSYSFhdGoUSMqVarEzZs3CQ8P5/Lly4SHh+Pi4pLWIUoq+P3332nSpAmOjo60bNmSzJkzs2PHDjZs2ECPHj3o27dvqpxn27ZtrFy5kr179wKwYcMGi4V6RUT+KpRcEREREZEUS0hIYOHChYSHh3P+/HkyZcpE2bJl6dOnD66urmkdnqSiQ4cOERoaytGjR7l79y7FihXj448/pmXLlql2jr1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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig4b = df9b.T.plot.barh(figsize=(14.4, 6), stacked=False, cmap=sns.light_palette(\"Navy\", as_cmap=True, reverse=False))\n", + "fig4b.grid(False)\n", + "fig4b.grid(axis='x', color='whitesmoke')\n", + "fig4b.set_title('Use of IDL vs. Python', y=1.19)\n", + "fig4b.set_xlabel('Percentage in each category')\n", + "fig4b.axvline(0, color='lightgray', lw=1.75)\n", + "fig4b.set_xlim(0.0, 101.0)\n", + "fig4b.invert_yaxis()\n", + "fig4b.legend(bbox_to_anchor=(0., 1.02, 0.98, .102), loc='lower left', mode=\"expand\", borderaxespad=0., ncol=1)\n", + "fig4b.spines['top'].set_visible(False)\n", + "fig4b.spines['right'].set_visible(False)\n", + "fig4b.spines['bottom'].set_visible(False)\n", + "fig4b.spines['left'].set_visible(False)\n", + "fig4b.figure.savefig(\"Figure4b.png\",bbox_inches='tight',dpi=300)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Scientific Stack vs. Expertise" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "# Observational (Space-Based)\n", + "space = df['Which of these areas of solar physics do you work in? Check all that apply.'].str.contains('Space-Based')\n", + "list_of_all_values_space= df['Which of the following have you personally utilized in your work within the last year?'][space].tolist()\n", + "total_space = len(list_of_all_values_space)\n", + "user_counts_space, proprietary_count_space, free_count_space, idl_vs_python_space = count_all_the_software_tools(list_of_all_values_space)\n", + "user_percentages_space = (np.array(user_counts_space)/total_space) * 100.\n", + "\n", + "# Observational (Ground-Based)\n", + "obs = df['Which of these areas of solar physics do you work in? Check all that apply.'].str.contains('Ground-Based')\n", + "list_of_all_values_obs= df['Which of the following have you personally utilized in your work within the last year?'][obs].tolist()\n", + "total_obs = len(list_of_all_values_obs)\n", + "user_counts_obs, proprietary_count_obs, free_count_obs, idl_vs_python_obs = count_all_the_software_tools(list_of_all_values_obs)\n", + "user_percentages_obs = (np.array(user_counts_obs)/total_obs) * 100.\n", + "\n", + "# Instrumentation\n", + "inst = df['Which of these areas of solar physics do you work in? Check all that apply.'].str.contains('Instrumentation')\n", + "list_of_all_values_inst= df['Which of the following have you personally utilized in your work within the last year?'][inst].tolist()\n", + "total_inst = len(list_of_all_values_inst)\n", + "user_counts_inst, proprietary_count_inst, free_count_inst, idl_vs_python_inst = count_all_the_software_tools(list_of_all_values_inst)\n", + "user_percentages_inst = (np.array(user_counts_inst)/total_inst) * 100.\n", + "\n", + "# Numerical Simulations\n", + "sim = df['Which of these areas of solar physics do you work in? Check all that apply.'].str.contains('Numerical Simulations')\n", + "list_of_all_values_sim = df['Which of the following have you personally utilized in your work within the last year?'][sim].tolist()\n", + "total_sim = len(list_of_all_values_sim)\n", + "user_counts_sim, proprietary_count_sim, free_count_sim, idl_vs_python_sim = count_all_the_software_tools(list_of_all_values_sim)\n", + "user_percentages_sim = (np.array(user_counts_sim)/total_sim) * 100.\n", + "\n", + "# Theory\n", + "theory = df['Which of these areas of solar physics do you work in? Check all that apply.'].str.contains('Theory')\n", + "list_of_all_values_theory = df['Which of the following have you personally utilized in your work within the last year?'][theory].tolist()\n", + "total_theory = len(list_of_all_values_theory)\n", + "user_counts_theory, proprietary_count_theory, free_count_theory, idl_vs_python_theory = count_all_the_software_tools(list_of_all_values_theory)\n", + "user_percentages_theory = (np.array(user_counts_theory)/total_theory) * 100." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Observational (Space-Based)Observational (Ground-Based)InstrumentationNumerical SimulationsTheory
IDL79.27272780.47337384.94623773.68421165.714286
SolarSoft67.27272766.27218966.66666753.80117041.904762
Python65.45454565.08875765.59139871.92982569.523810
SunPy41.45454540.82840236.55914039.18128732.380952
Shell Scripting39.63636439.64497047.31182846.78362642.857143
C17.09090918.93491118.27957018.12865518.095238
C++12.72727313.60946717.20430121.63742715.238095
Fortran29.45454533.13609520.43010859.06432759.047619
IRAF2.9090914.7337285.3763441.7543861.904762
Perl8.0000007.6923089.6774197.6023394.761905
Javascript9.81818210.05917213.9784954.6783636.666667
Julia2.1818182.3668642.1505384.6783634.761905
MATLAB17.45454517.75147916.12903225.14619927.619048
Java4.3636365.3254449.6774191.1695910.952381
R5.0909094.1420124.3010753.5087726.666667
SQL12.00000012.42603615.0537637.0175448.571429
Ruby0.7272730.0000001.0752690.0000000.000000
HTML / CSS23.63636420.71005924.73118322.80701820.952381
Spreadsheets (e.g. Excel)37.09090937.86982247.31182832.16374331.428571
Mathematica8.00000011.2426049.67741914.61988323.809524
MPI14.90909115.9763319.67741935.67251536.190476
Github (or similar)44.72727343.78698246.23655951.46198845.714286
\n", + "
" + ], + "text/plain": [ + " Observational (Space-Based) \\\n", + "IDL 79.272727 \n", + "SolarSoft 67.272727 \n", + "Python 65.454545 \n", + "SunPy 41.454545 \n", + "Shell Scripting 39.636364 \n", + "C 17.090909 \n", + "C++ 12.727273 \n", + "Fortran 29.454545 \n", + "IRAF 2.909091 \n", + "Perl 8.000000 \n", + "Javascript 9.818182 \n", + "Julia 2.181818 \n", + "MATLAB 17.454545 \n", + "Java 4.363636 \n", + "R 5.090909 \n", + "SQL 12.000000 \n", + "Ruby 0.727273 \n", + "HTML / CSS 23.636364 \n", + "Spreadsheets (e.g. Excel) 37.090909 \n", + "Mathematica 8.000000 \n", + "MPI 14.909091 \n", + "Github (or similar) 44.727273 \n", + "\n", + " Observational (Ground-Based) Instrumentation \\\n", + "IDL 80.473373 84.946237 \n", + "SolarSoft 66.272189 66.666667 \n", + "Python 65.088757 65.591398 \n", + "SunPy 40.828402 36.559140 \n", + "Shell Scripting 39.644970 47.311828 \n", + "C 18.934911 18.279570 \n", + "C++ 13.609467 17.204301 \n", + "Fortran 33.136095 20.430108 \n", + "IRAF 4.733728 5.376344 \n", + "Perl 7.692308 9.677419 \n", + "Javascript 10.059172 13.978495 \n", + "Julia 2.366864 2.150538 \n", + "MATLAB 17.751479 16.129032 \n", + "Java 5.325444 9.677419 \n", + "R 4.142012 4.301075 \n", + "SQL 12.426036 15.053763 \n", + "Ruby 0.000000 1.075269 \n", + "HTML / CSS 20.710059 24.731183 \n", + "Spreadsheets (e.g. Excel) 37.869822 47.311828 \n", + "Mathematica 11.242604 9.677419 \n", + "MPI 15.976331 9.677419 \n", + "Github (or similar) 43.786982 46.236559 \n", + "\n", + " Numerical Simulations Theory \n", + "IDL 73.684211 65.714286 \n", + "SolarSoft 53.801170 41.904762 \n", + "Python 71.929825 69.523810 \n", + "SunPy 39.181287 32.380952 \n", + "Shell Scripting 46.783626 42.857143 \n", + "C 18.128655 18.095238 \n", + "C++ 21.637427 15.238095 \n", + "Fortran 59.064327 59.047619 \n", + "IRAF 1.754386 1.904762 \n", + "Perl 7.602339 4.761905 \n", + "Javascript 4.678363 6.666667 \n", + "Julia 4.678363 4.761905 \n", + "MATLAB 25.146199 27.619048 \n", + "Java 1.169591 0.952381 \n", + "R 3.508772 6.666667 \n", + "SQL 7.017544 8.571429 \n", + "Ruby 0.000000 0.000000 \n", + "HTML / CSS 22.807018 20.952381 \n", + "Spreadsheets (e.g. Excel) 32.163743 31.428571 \n", + "Mathematica 14.619883 23.809524 \n", + "MPI 35.672515 36.190476 \n", + "Github (or similar) 51.461988 45.714286 " + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df9c = pd.DataFrame({'Observational (Space-Based)': list(user_percentages_space), \n", + " 'Observational (Ground-Based)': list(user_percentages_obs), \n", + " 'Instrumentation': list(user_percentages_inst),\n", + " 'Numerical Simulations': list(user_percentages_sim),\n", + " 'Theory': list(user_percentages_theory)},\n", + " index=software_names)\n", + "df9c" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Conclusions:\n", + "* Overall, respondents listed 42 different software tools and the average respondent used 5 tools in the past year.\n", + "* 73% of all participants use IDL and 66% of all participants use Python. \n", + " * 27% use IDL exclusively, and 21% use Python exclusively.\n", + " * 17% of graduate and undergraduate students use IDL exclusively; 33% of faculty, staff scientists, or researchers use IDL exclusively.\n", + " * 36% of graduate and undergraduate students use Python exclusively; 14% of faculty, staff scientists, or researchers use Python exclusively.\n", + "* We used almost exactly the same question as the Astrophysics community survey (see Figure 10 in Momcheva & Tollerud, 2015). \n", + " * 79% of solar physics graduate students use Python; 80$\\pm$5 % of astrophysics graduate students use Python.\n", + " * 60% of solar physics graduate students use IDL; 41$\\pm$5 % of astrophysics graduate students use IDL.\n", + " * In the astrophysics community, Python is the most popular programming language across the entire community as well as within every individual career category.\n", + " * In the solar physics community, Python is the most popular programming language only among graduate students; IDL and Python are at parity for postdocs, and IDL is more popular than Python for faculty, staff scientists, researchers, software developers, and instrument developers.\n", + "* Fortran is still quite popular (used by 35% of the community).\n", + "* Adoption of version-control tools like Github is consistent across the board (at slightly more than 40%), with the exception of the 'Software and Instrument Developers' category, 60% of whom use version control. Actually, the option we gave is ambiguous. In retrospect, we should have provided β€œGit, Github, or similar\" instead of β€œGithub (or similar)\" as an option in Question 9.\n", + "* Other tools used by 5% of the community or less include Julia, R, Java, IRAF, Ruby, Maple, CASA, COBOL, CUDA, OMP, ANA, C# & .NET, ZEMAX, FreeCAD, LuaJIT, OpenMP, PhP, Octave, and origin." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Question 10: Have you cited software papers in your published research?\n", + "πŸ”² **Yes \n", + "πŸ”² Sometimes \n", + "πŸ”² No** " + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "total_number = df['Have you cited software papers in your published research?'].count()\n", + "yes_numbers = len(df.loc[df['Have you cited software papers in your published research?'] == 'Yes'])\n", + "no_numbers = len(df.loc[df['Have you cited software papers in your published research?'] == 'No'])\n", + "some_numbers = len(df.loc[df['Have you cited software papers in your published research?'] == 'Sometimes'])" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig5 = data10_percentages.plot.barh(figsize=(14.4, 3), stacked=False, legend=False, cmap=sns.light_palette(\"Navy\", as_cmap=True, reverse=True), xerr=data10_percent_err, ecolor='silver', capsize=4)\n", + "fig5.grid(False)\n", + "fig5.grid(axis='x', color='whitesmoke')\n", + "fig5.set_title('Have you cited software papers in your published research?')\n", + "fig5.set_xlabel('Percentage of total respondents (n='+str(total_number)+')')\n", + "fig5.axvline(0, color='lightgray', lw=1.75)\n", + "fig5.set_xlim(0.0, 101.0)\n", + "fig5.spines['top'].set_visible(False)\n", + "fig5.spines['right'].set_visible(False)\n", + "fig5.spines['bottom'].set_visible(False)\n", + "fig5.spines['left'].set_visible(False)\n", + "fig5.figure.savefig(\"Figure5.png\",bbox_inches='tight',dpi=300)" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Most 73.35164835164835 Β± 4.489047977573679 solar physicists have cited scientific software in their research.\n" + ] + } + ], + "source": [ + "total_sometimes_cite_software = data10_numbers['Participants']['Yes'] + data10_numbers['Participants']['Sometimes']\n", + "percentage_total_sometimes_cite_software = (total_sometimes_cite_software / len(df))*100.\n", + "percentage_err_total_sometimes_cite_software = (np.sqrt(total_sometimes_cite_software) / len(df))*100.\n", + "print(\"Most\",percentage_total_sometimes_cite_software,\"Β±\",percentage_err_total_sometimes_cite_software,\"solar physicists have cited scientific software in their research.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Question 11: If 'No’ for the previous question: Why haven’t you cited software in your research?\n", + "\n", + "πŸ”² **I am not sure how to appropriately cite software \n", + "πŸ”² I do not think it is necessary \n", + "πŸ”² I do not think software belongs in citations** " + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [], + "source": [ + "total_number = df['Why haven’t you cited software in your research?'].count()\n", + "not_sure_numbers = len(df.loc[df['Why haven’t you cited software in your research?'] == 'I am not sure how to appropriately cite software'])\n", + "not_necessary_numbers = len(df.loc[df['Why haven’t you cited software in your research?'] == 'I do not think it is necessary'])\n", + "not_interested_numbers = len(df.loc[df['Why haven’t you cited software in your research?'] == 'I do not think software belongs in citations'])\n", + "not_sure_percentage = (not_sure_numbers/total_number)*100.\n", + "not_necessary_percentage = (not_necessary_numbers/total_number)*100.\n", + "not_interested_percentage = (not_interested_numbers/total_number)*100." + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Participants that answered Question 10 with No
I am not sure how to appropriately cite software46
I do not think it is necessary27
I do not think software belongs in citations13
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" + ], + "text/plain": [ + " Participants that answered Question 10 with No\n", + "I am not sure how to appropriately cite software 46\n", + "I do not think it is necessary 27\n", + "I do not think software belongs in citations 13" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data11 = {'Participants that answered Question 10 with No': [not_sure_numbers, not_necessary_numbers, not_interested_numbers]}\n", + "data11_numbers = pd.DataFrame(data11, index=['I am not sure how to appropriately cite software', 'I do not think it is necessary', 'I do not think software belongs in citations'], columns = ['Participants that answered Question 10 with No'])\n", + "data11_numbers" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Participants that answered Question 10 with No
I am not sure how to appropriately cite software53.488372
I do not think it is necessary31.395349
I do not think software belongs in citations15.116279
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" + ], + "text/plain": [ + " Participants that answered Question 10 with No\n", + "I am not sure how to appropriately cite software 53.488372\n", + "I do not think it is necessary 31.395349\n", + "I do not think software belongs in citations 15.116279" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data11_percentages = data11_numbers.divide(total_number)*100.\n", + "data11_percentages" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Participants that answered Question 10 with No
I am not sure how to appropriately cite software7.886430
I do not think it is necessary6.042038
I do not think software belongs in citations4.192501
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" + ], + "text/plain": [ + " Participants that answered Question 10 with No\n", + "I am not sure how to appropriately cite software 7.886430\n", + "I do not think it is necessary 6.042038\n", + "I do not think software belongs in citations 4.192501" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data11_percent_err = (np.sqrt(data11_numbers)).divide(total_number)*100.\n", + "data11_percent_err" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig6 = data11_percentages.plot.barh(figsize=(14.4, 3), stacked=False, legend=False, cmap=sns.light_palette(\"Navy\", as_cmap=True, reverse=True), xerr=data11_percent_err, ecolor='silver', capsize=4)\n", + "fig6.grid(False)\n", + "fig6.grid(axis='x', color='whitesmoke')\n", + "fig6.set_title('Why haven’t you cited software in your research?')\n", + "fig6.set_xlabel('Percentage who answered \"No\" to Question 10 (n='+str(total_number)+')')\n", + "fig6.axvline(0, color='lightgray', lw=1.75)\n", + "fig6.set_xlim(0.0, 101.0)\n", + "fig6.spines['top'].set_visible(False)\n", + "fig6.spines['right'].set_visible(False)\n", + "fig6.spines['bottom'].set_visible(False)\n", + "fig6.spines['left'].set_visible(False)\n", + "fig6.figure.savefig(\"Figure6.png\",bbox_inches='tight',dpi=300)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.571428571428571 plus or minus 0.990536064687909 percentage of the total community does not think software belongs in citations.\n" + ] + } + ], + "source": [ + "print((not_interested_numbers)/(len(df))*100.,\"plus or minus\",(np.sqrt(not_interested_numbers))/(len(df))*100.,\"percentage of the total community does not think software belongs in citations.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Conclusions:\n", + "* Most (73Β±4%) of solar physicists have cited scientific software in their research, although only 42 Β± 3% do so routinely.\n", + "* Roughly a quarter (27$\\pm$3\\%) never cites scientific software in their research. When asked why, about half (53$\\pm$8\\%) responded that they do not know how to appropriately cite scientific software.\n", + "* 14 people do not believe software belongs in citations. This is 4Β±1% of the total sample." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Question 12: On which of these have you run software for solar-physics research?\n", + "\n", + "πŸ”² **Laptop / Desktop computer \n", + "πŸ”² Shared workstation \n", + "πŸ”² Local Cluster \n", + "πŸ”² Regional or National Cluster \n", + "πŸ”² GPU \n", + "πŸ”² Commercial cloud** " + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [], + "source": [ + "def count_all_the_hardware(list_of_all_values):\n", + " \"\"\"\n", + " \n", + " Input: list_of_all_values, list of lists containing all the responses to the question:\n", + " \"On which of these have you run software for solar-physics research?\"\n", + " \n", + " \"\"\"\n", + " ld_count = 0\n", + " work_count = 0\n", + " lc_count = 0\n", + " rnc_count = 0\n", + " gpu_count = 0\n", + " cc_count = 0\n", + " for i in range(len(list_of_all_values)):\n", + " single_row_list = list_of_all_values[i].split(\", \")\n", + " if \"Laptop / Desktop computer\" in single_row_list:\n", + " ld_count += 1\n", + " if \"Shared workstation\" in single_row_list:\n", + " work_count += 1\n", + " if \"Local Cluster\" in single_row_list:\n", + " lc_count += 1\n", + " if \"Regional or National Cluster\" in single_row_list:\n", + " rnc_count += 1\n", + " if \"GPU\" in single_row_list:\n", + " gpu_count += 1\n", + " if \"Commercial cloud\" in single_row_list:\n", + " cc_count += 1 \n", + " hardware_names = [\"Laptop / Desktop computer\", \"Shared workstation\", \"Local Cluster\", \"Regional or National Cluster\", \"GPU\", \"Commercial cloud\"]\n", + " user_counts = [ld_count, work_count, lc_count, rnc_count, gpu_count, cc_count]\n", + " return user_counts " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute Resources vs. Career" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [], + "source": [ + "# All Participants\n", + "list_of_all_values_all = df['On which of these have you run software for solar-physics research?'].tolist()\n", + "user_counts_all = np.array(count_all_the_hardware(list_of_all_values_all))\n", + "user_percentages_all = (user_counts_all/total_responses) * 100.\n", + "\n", + "# Faculty, Staff Scientist, or Researcher\n", + "fssr = df['How would you describe the stage of your career?'] == 'Faculty, Staff Scientist, or Researcher'\n", + "list_of_all_values_fssr = df['On which of these have you run software for solar-physics research?'][fssr].tolist()\n", + "user_counts_fssr = np.array(count_all_the_hardware(list_of_all_values_fssr))\n", + "user_percentages_fssr = (np.array(user_counts_fssr)/(total_numbers['Total Numbers']['Faculty, Staff Scientist, or Researcher'])) * 100.\n", + "\n", + "# Postdocs\n", + "postdoc = df['How would you describe the stage of your career?'] == 'Postdoc'\n", + "list_of_all_values_pd = df['On which of these have you run software for solar-physics research?'][postdoc].tolist()\n", + "user_counts_pd = np.array(count_all_the_hardware(list_of_all_values_pd))\n", + "user_percentages_pd = (np.array(user_counts_pd)/(total_numbers['Total Numbers']['Postdoc'])) * 100.\n", + "\n", + "# Graduate or Undergraduate Students\n", + "gs = df['How would you describe the stage of your career?'] == 'Graduate or Undergraduate Student'\n", + "list_of_all_values_gs = df['On which of these have you run software for solar-physics research?'][gs].tolist()\n", + "user_counts_gs = np.array(count_all_the_hardware(list_of_all_values_gs))\n", + "user_percentages_gs = (np.array(user_counts_gs)/(total_numbers['Total Numbers']['Graduate or Undergraduate Student'])) * 100.\n", + "\n", + "# Software or Instrument Developers\n", + "us = df['How would you describe the stage of your career?'] == 'Software or Instrument Developer'\n", + "list_of_all_values_us = df['On which of these have you run software for solar-physics research?'][us].tolist()\n", + "user_counts_us = np.array(count_all_the_hardware(list_of_all_values_us))\n", + "user_percentages_us = (np.array(user_counts_us)/(total_numbers['Total Numbers']['Software or Instrument Developer'])) * 100." + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Commercial cloud4.9450554.8780493.7735855.9523814.545455
\n", + "
" + ], + "text/plain": [ + " All Participants \\\n", + "Laptop / Desktop computer 95.879121 \n", + "Shared workstation 39.560440 \n", + "Local Cluster 51.098901 \n", + "Regional or National Cluster 14.010989 \n", + "GPU 8.516484 \n", + "Commercial cloud 4.945055 \n", + "\n", + " Faculty, Staff Scientist, or Researcher \\\n", + "Laptop / Desktop computer 95.609756 \n", + "Shared workstation 42.439024 \n", + "Local Cluster 45.365854 \n", + "Regional or National Cluster 19.024390 \n", + "GPU 10.731707 \n", + "Commercial cloud 4.878049 \n", + "\n", + " Postdoc Graduate or Undergraduate Student \\\n", + "Laptop / Desktop computer 98.113208 94.047619 \n", + "Shared workstation 33.962264 32.142857 \n", + "Local Cluster 58.490566 60.714286 \n", + "Regional or National Cluster 11.320755 7.142857 \n", + "GPU 1.886792 5.952381 \n", + "Commercial cloud 3.773585 5.952381 \n", + "\n", + " Software or Instrument Developer \n", + "Laptop / Desktop computer 100.000000 \n", + "Shared workstation 54.545455 \n", + "Local Cluster 50.000000 \n", + "Regional or National Cluster 0.000000 \n", + "GPU 13.636364 \n", + "Commercial cloud 4.545455 " + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hardware_names = [\"Laptop / Desktop computer\", \"Shared workstation\", \"Local Cluster\", \"Regional or National Cluster\", \"GPU\", \"Commercial cloud\"]\n", + "\n", + "df12 = pd.DataFrame({'All Participants': list(user_percentages_all), \n", + " 'Faculty, Staff Scientist, or Researcher': list(user_percentages_fssr), \n", + " 'Postdoc': list(user_percentages_pd),\n", + " 'Graduate or Undergraduate Student': list(user_percentages_gs),\n", + " 'Software or Instrument Developer': list(user_percentages_us)},\n", + " index=hardware_names)\n", + "df12" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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AIGBjx47l5ZOkHzhwgJeekJDABAIB27lzZ4XHiLHSekgSC2OMxcXFMYFAwJYvX87LFxERwQQCAVu9evU345EmICCACQQCru5grLTO6tOnD9u9ezdjjLF79+4xoVDIJk6cyNWZjDF25coVJhQKubrt2bNnTCAQsE2bNnHLpe1/fHw8EwgELDAwkLde//79eeV/i0AgYJqamrw6+v79+0xLS4tNmzaNl69snZmamsoEAgHbsGEDY4yxgoICZmhoyNzc3HjbGDNmDOvXrx9jjLGrV6/yvksYK63vxowZw2bPns2lfc93go6ODluyZAlv23/++Sezs7Njubm5FR4DSV0tqTe+Tv+76+aq1q8V1Tv79u1jAoGAHT9+nBfz8OHDmampKSsuLq5yXV9WcXExmzx5crl6g5DqoCEMhBBSA9j/dXesVatWpXnl5OR46wBA27ZteW8TUFNTQ+PGjfH69esKy9HT00O9evWQkpICoPQpSU5ODkaOHIl3797hwYMHAEqfOrZv3573FK1NmzbQ1tbmPrdo0QKqqqoVbq+kpARxcXGwsLDAf/7zHy69YcOG2L9/PxYsWMClKSoq8rq5Kisro127dt/cl9evX+Pq1asYPHgwbwKo2rVrY+zYscjPz0dycnKF61eXlZUV939ZWVloaWlx8ZWUlCAqKgqdO3eGnJwc3r59i7dv3+Ldu3fo06cPCgoKkJSUBABQV1fHhQsXsGvXLm59R0dHhIWFoXXr1gBKn/QJBAKoqalxZb19+5Z7KhwTE1NpvAMHDuR9FovFKCws5LqnL168GNHR0VBRUeHy5Obmck86P3/+DKB0CA1jDKdPn+byRUREoFGjRjAxMfnheAcMGABZWVmcOnWKSwsPD4eSkhJ69OgBAIiOjoaGhka5ni4TJkwAAF737x/1o0/j+vfvz/vcsWNHFBUVlRs+8jV1dXU8fPgQmzdv5rrAW1hYIDw8HAYGBgBKj4GysjKcnZ1560p64EjrYv8t5ubm+PjxI27cuAGgtCeStrY2DAwMuO7knz59Qnp6OtejacCAATh//jzMzMy4cgoKCrj/S64ZibLH8syZM5CRkYGFhQXvOtHS0oKamhpiY2OrtQ+Sffbw8OCl9+/fH23bti13XVTl3ErqkmXLluH69esASuusyMhI7lW2MTExYIzB3d2d1y1cV1cXpqamiIuLQ1FRUbmydXV1cfHiRd5T/eLiYq7L/6dPn3j5O3fuXGG3c2lMTU15dbSGhga6deuG2NhY3rCCTp068epMSW8rSX0kLy+Pvn374vz589ywgbdv3yIlJYWrB5s0aQIZGRn4+/sjISEBBQUFkJGRwY4dO7ghDmVV9TtBXV0dERERCAkJ4YZRTJ06FUePHq3SHBiSXiKSbdZE3VzV+lWi7LUZGxsLVVVVXj0uIyODNWvWYN++fbzrorK6vqx169YhMjISY8aMKVefEFJVNISBEEJqgKqqKuTl5fHmzZtK80reDNCkSRPe+mUpKCh8c94AOTk5mJiYcA0IKSkpaNy4Mezs7LB27VpcvHgR7dq1Q1JSUrl5Gb7umixRp04dboLCsnJycvD582feD0UJgUDA+6yiolKuIaVOnTrfPDaZmZkAShtSytLQ0ABQOkb3Zym7/1/v+7t37/Dx40dERUXx5l/4mmQc7ezZszF+/HisWLECK1euhLa2NiwtLeHg4MANT3n69Cny8/OlTqz5dVnVjRf4/xNKysjI4N27d/D398edO3fw9OlTPH/+nGukklxHbdu2hUgkwunTpzFmzBjk5+cjOjoadnZ2XMPWj8TbtGlTGBoaIjIyEnPmzEFJSQlOnz6Nnj17cm8dycjIQLdu3cqtq6amhvr163PXws8g7TqvjrJ/lwoKCgDAm6uhrIkTJ+LKlSvw8fGBj48P2rdvD0tLS9jb23M3LhkZGWjVqlW58coKCgpo1apVtY+Bqakp5OTkkJKSAh0dHVy4cAFmZmZQU1PDuXPn8OnTJ1y4cAGFhYW8IVEyMjIICAjA5cuX8fTpUzx9+pS7psrWPWWPxdOnT8EYQ/fu3aXGVN0JEjMyMlC/fn00bty43DINDQ1u/HtF8UjTr18/nD17FhEREYiIiICamhosLCxga2vL3ZhKGnkqqnsSExMrvHmTk5PDwYMHkZqaiidPnnB/OwDKjaEvG+/Hjx+5vBINGjTgrrH27duX216bNm0QExODnJwcrrzK6gYAGDRoEIKDgxEVFYUhQ4bg9OnTKCoqgrW1NYDSm+1Zs2Zh/fr1cHNzg5KSEoyNjTFgwAD0799fasN4Vb8TFi9ejKlTp2LevHn4/fffoaenh969e2PIkCGoV69euXXL+nr/aqpurmr9Ki1moPT7rXXr1uUakMoORZC2rrTzKfHs2TPs3r0b/fr1w5w5c6TuAyFVQQ0IhBBSA2RkZKCvr49r167hy5cvFY5xZf83JrlVq1a8BgRZ2e/rQGZhYYHIyEi8fPkSFy5cQOfOndGwYUMIBAKkpaVBJBLh3bt35X7kV3d7khumqjxB+559Kftj+2uSH2c/c3Kob8Uo2de+ffvC0dFRap5WrVoBADQ1NREZGYmEhATExMQgISEBmzZtQlBQEIKDg6GhoYHi4mIYGBhg0qRJUsv6+jqoSGXHPSIiAjNnzkSTJk3QtWtXmJubQyQSITExEf7+/ry81tbWWLlyJTIzM3Ht2jV8/vyZu5GQ7P+PxDtw4EAsWLAAV69eRX5+Pl69esV7qlbZua7sPH/r5r2sql6LFZVZnSfGEurq6ggLC8OFCxdw7tw5JCQkICAgAEFBQQgMDIShoeEPH4Oy6tWrB319faSkpMDBwQF37tzB9OnToaamhqKiIly5cgWJiYlo0aIFOnToAKB0fgAnJycUFhbCzMwMAwYMQMeOHcEYk/pqxbI3kSUlJahbty42b94sNaaqjPP/WnWPSVV6e8nLy2PTpk24c+cOzp49i/j4eISEhODIkSOYMWMG3N3dq1z3lJ1k9e3bt7C3t0d2djZMTU1haWkJTU1NtGjRAvb29uXKKhvvH3/8gdDQUF7a7t27YWRkxG2zLMl1+vV1XZVr3MDAAM2bN8epU6cwZMgQnDp1CiKRiHfzP3bsWAwcOBBnz55FXFwckpKScO7cORw7dgzbt2+vMJbK/kaMjY0RExPD/UtKSsLKlSuxc+dOhISEVNoQ9PVxq6m6uTr1K1D+nBQXF1e5LqlOnZOUlITCwsIK4yekqqgBgRBCaoiNjQ1SU1MRHBzMmxTpa+fOncOzZ8+47to/SvIk9/z580hPT4eXlxcAwNDQEGfPnkX79u1Rr149ruv092rYsCHq1KmDp0+fllu2Y8cOvHr1CnPnzv3u8iVPYh4+fFhu2aNHjwDgb3u3taqqKhQVFVFUVMR165d4/vw5bt68CUVFRRQXF+P27dtQVlZGz5490bNnTwClPzanTZuGw4cPY+7cuWjRogU+ffpUrqz379/j/PnzUp/gVZe3tzf+85//4OjRo1BSUuLSv37bh8SAAQOwevVqnDt3DpcuXULr1q2hp6fHLf/RePv27YulS5ciOjoaeXl5UFFR4b0/vkWLFtw5/dqrV6+Qm5uLZs2aAfj/P8K/7lYP4JtDYSrzK8os686dOwBKb5wkTzYvXbqEkSNHYs+ePTA0NESLFi1w5coVFBYW8m4UCwoKkJGRweu2XVXm5ubw8/NDcnIyZGVlYWBgACUlJdSvXx8XL15EYmIir/fBtm3b8OHDB5w6dQpt2rTh0qVdM9K0aNECiYmJEIlEvMkNgdKJ/SS9LapKUt7r16/L9UJ49OgRd11Ux/Pnz/H8+XN07twZQqEQkyZNQlZWFkaOHIkdO3bA3d2dG9r18OFD6OrqltuukpISGjRoUO7NEvv370dGRgZ27tzJe4Kdnp5epdjc3NwwaNAgXtrXw9ik1bVPnjyBiooKryt9VcjIyGDAgAHYtWsXnj9/jkuXLvEmOs3JycHt27fRqVMnuLi4wMXFBZ8/f8bcuXMRGRmJO3fulHvjTVW+E6ZPn45bt25BXV0dVlZWsLKyQklJCYKCgrBmzRqEh4dzQ0mqoqbq5urUr9I0b96cqxe+FhcXh4iICMyaNavKx+Brkl590nrPEFIdNAcCIYTUEFtbW+jr68Pb2xuJiYnllt+6dQu///47WrZsyc0U/aOaNGmCjh07Yu/evcjJyeHGXhoaGiIrKwtHjx7lujf/CDk5OW488Ndd2N+/f48dO3bg2bNnP1S+mpoaRCIRjh8/znstVkFBAYKCgqCgoMC7Ca0Kyc1iVV4f+TU5OTmYm5sjLi4Ot2/f5i1btWoVJk6ciHfv3qG4uBgjRozAihUreHkkNyGS7VtaWuL27duIi4vj5duyZQumTJlS7vV+3yMnJwfNmzfn/bh98eIFzpw5A4D/hF3yFE3yRLbsmNsfjbd+/fqwsLBAXFwc4uLi0LdvX95Nco8ePfDgwYNyXZADAgIAgOstI+lm/PU5yM3NLRdXdfyKMsuaMmUKZs+ezTvmWlpakJeX510Tubm55V7Vun//fnz69KnCYQESsrKy5a5rc3Nz5OXlITAwEJqamlBWVuYaEk6ePIknT57wys3JyYGioiLvDQYFBQU4ePAggMp7ekjmOdmyZQsvPTo6GlOmTKnyzVXZ8so+0Y2KisKjR48qPSbSbN26FaNGjcLLly+5NHV1dTRp0oQ7F5K5ObZt28brjXDjxg0kJyfDwsJC6lNhyTwYXw81YIxh7969ACB13oSvtW/fHiYmJrx/DRo04JZHR0fzhrLcvXsXiYmJ6NOnT5X3/2uSty2sXbsWjDHe/B5JSUkYOXIkb+4NJSUlbiiCtN4eVflOePfuHYYNG8Y7p7Kystw8DdXtrVZTdXN16ldpzM3N8fr1a5w9e5aXvmvXLsTGxnJvUaquUaNGITEx8Ye/3wmhK4gQQmqIrKwsNm/ejPHjx8PNzQ19+vSBkZERatWqhatXr+LEiRNo1qwZ/Pz8qj0++FvMzc3h7+8PFRUVrnuyZMKuZ8+eSe2O/D1mzJgBe3t72Nvbw9nZGcrKyjh06BA+f/6MqVOn/nD5CxYswMiRIzF06FA4OTmhbt26OH78OG7cuIEFCxaUe8pZGUnX2AMHDuD169e8bvqVmTlzJi5cuABnZ2c4OzujefPmiI2NRUxMDIYNG8YdZ1dXV2zZsgUTJ05Et27dkJ+fj+DgYCgqKmLIkCEASieFO3PmDCZOnAhHR0d06NABly5dQlhYGMzNzWFubl6t/ZLG3NwcERERWLhwIcRiMTIyMnDo0CHk5eUBKD+hm7W1NebNm8f9/2s/I96BAwdiypQpAIDly5dLLX/q1KlwcnJCmzZtkJKSgjNnzqBPnz7cU/JevXph+fLlWLp0KTIzM6GgoIBDhw7xfsRX168os6yxY8diwYIFGDVqFPr16wfGGMLCwvDlyxcMHz4cAGBvb4/Q0FCsWrUKd+/ehUgkwvXr1xESEgI9PT2pXeC/pqqqitTUVBw6dAhmZmZo3rw5NDU10aRJE1y7do33mk5DQ0PExMSgTp06XPd4oPSaiY6OhoeHB/r164ePHz/i2LFj3BPlstdMWRYWFujZsycCAwORmZkJY2NjZGZmYt++fWjevDnGjh1breMmKW/37t14+fIljIyMuNdKtmrVqtzkilXh7OyMsLAwODs7Y9iwYWjQoAFSUlKQmprK9dbq0KEDXF1dsWfPHowePRq9evXCq1evsGfPHtSvXx8zZsyQWra5uTn27NkDDw8PDB06FIWFhTh16hSuX78OWVnZSo9fZWRkZODk5ARXV1cUFhZi165dUFVVxeTJk7+rPE1NTXTo0AEREREwMjJC06ZNuWU9evRA27ZtMX/+fNy4cQOtW7fGw4cPsW/fPhgbG0udjwGo/DuhadOmsLa2xv79+5GXlwd9fX3k5ORg7969aNy4cblJSquiJurm6tavZTk6OuLo0aOYNm0anJ2d0bZtW8TGxiIpKQkrVqyo0nAcae7evYunT5+id+/eP7UOI/8+1IBACCE1qHHjxti3bx+OHTuGkJAQbNq0CUVFRWjdujWmTJkCR0fHKk0cVR2SBoSvZ/lWVVVF+/btcf/+/Z9ygwqUTigWHAYzDUsAACAASURBVByM9evXY/v27ZCVlYWOjg5Wr17N/Wj7Efr6+jhw4AA2bdqEwMBAlJSUQFNTE76+vuVm7K8KY2Nj9O/fHzExMUhJSanWk7vWrVvj0KFD2LRpE/eDuFWrVpg3bx6vy62XlxdUVFRw9OhRrF69GrVq1UKnTp2wdu1abvJHFRUVBAcHY9OmTTh9+jSCg4PRvHlzTJgwAe7u7t89/8XXFi9eDCUlJURHRyMsLAzq6uqwsbFB79694eTkhJSUFGhpaXH5+/Tpg8WLF6N9+/a8d8r/rHh79OgBZWVlKCsrl+uOLyl/w4YNiIiIwIcPH9CqVSvMnj0bo0aN4vKpqqpi27Zt8Pb2xqZNm9CwYUM4ODigXbt2mDZt2ncdp19RZln29vaQl5fH7t27sX79epSUlEAkEmHbtm3cDbyCggJ27twJX19fnDp1CsePH4e6ujo8PDwwfvz4SudAmDlzJry9vbFs2TIsW7YMNjY2AEqHNB09epR3zCW9koyMjLgJ2YDSm5oPHz7g8OHDWL58ORo3bgw9PT1s3rwZjo6OSElJ4Z2PsmRkZLBx40Zs374dx44dQ3R0NFRVVdGnTx9MmTJF6mSI3yIpb9u2bVx5jRo1wrBhwzB58uRqNyACgFAoRFBQEHx9fREYGIjc3Fy0adMGv//+O2/G+vnz56Nt27Y4ePAgVq1ahQYNGqB3797w8vKSOtEdUFrvLl++HIGBgdw62traCA4Oxu+//44LFy5UO96v9e/fH61atcL27dtRUlICU1NTzJo1q0pzplTE2toa69evL9frSElJCYGBgdi0aRNOnDiB169fQ01NDcOHD//m+PqqfCcsW7YMrVq1Qnh4OMLDw6GoqAhjY2NMmzatShNhllUTdXN169ey6tSpgz179mDDhg0IDw/Hx48foaGhgQ0bNnxXI4pEcHAwQkNDce7cOWpAID9Ehn1rNhhCCCGEkF+soKAAJiYmGDZs2HeP7yXk30ooFMLW1harVq36qeUGBATAx8cHiYmJvOEShJB/N5oDgRBCCCE1SvKUzc7OrqZDIYSgtFEvJCQEvXr1osYDQggPDWEghBBCSI0IDAxEeno64uPj0aNHD66rMCGkZrx8+RIrV67E/fv38eTJE6xdu7amQyKE/MNQAwIhhBBCakRxcTESExOhq6tbbvJEQsjfr0GDBkhLS0NRUREWLVrEvQGBEEIkaA4EQgghhBBCCCGEVIrmQCCEEEIIIYQQQkilqAGBEEIIIYQQQgghlaIGBEIIIYQQQgghhFSKJlEkpIZ9+PAB2dnZKCwsrOlQCCGEEEIIIf/l5OXl0aRJE9SvX/+nl00NCITUoA8fPuDly5do0aIFFBUVISMjU9MhEUIIIYQQQv5LMcaQl5eHzMxMAPjpjQg0hIGQGpSdnY0WLVpASUmJGg8IIYQQQgghP0RGRgZKSkpo0aIFsrOzf3r51IBASA0qLCyEoqJiTYdBCCGEEEII+R+iqKj4S4ZIUwMCITWMeh4QQgghhBBCfqZfdY9BDQiEEEIIIYQQQgipFDUgEEJIBRhjNR3CD/lvj58QQgghhPyzUAMCIf9ABYXFNR3CT43By8sLQqEQBw8eLLdMKBTCz88PABASEgKhUIisrKwKy7K0tIRQKOT9E4vF6N+/PwICAlBSUvJTYvb398eOHTu4zz4+PtDS0qry+tXN/7OVjZ8QQgghhJAfRa9xJOQfSEG+FqxnhNVoDCe8B/+Uct69e4fo6GgIBAIEBwfD0dHxh8u0tLSEh4cH9zkvLw/nzp2Dt7c3Pnz4gJkzZ/7wNjZu3Ijx48dzn+3t7WFubl7l9aub/2crGz8hhBBCCCE/ihoQCCG/1IkTJ1C7dm3MnDkT7u7u+Ouvv6Cjo/NDZaqqqkJPT4+XZmxsjAcPHmD//v2YMmUK5OXlf2gbZamrq0NdXf2X5SeEEEIIIeSfjoYwEEJ+qZCQEJiamqJbt25o0qQJgoODf9m2tLS08OnTJ7x//x4A8PbtWyxatAg9evSASCSCoaEhJk+ejMzMTG4dV1dXzJkzBxMnToSuri48PT0hFApRXFyMzZs3QygUApA+JOHYsWOwsbGBrq4uLC0tsWnTJhQXF0vN7+rqivnz52Pz5s3o2rUrOnfujOnTp+Pt27e8Mg8ePAg7Ozvo6elBR0cHtra2iIyM5JaHhIRALBYjPT0d9vb2EIvF6NGjBwIDA7k80uLPz8/H4sWLYW5uDpFIhH79+tEQB0IIIYQQUi3UgEAI+WVu3bqFW7duYfDgwZCVlcXgwYMRERGB3NzcX7K9x48fQ0lJCY0aNQJjDG5ubkhJScHMmTOxY8cOTJo0CUlJSVi8eDFvvZMnT0JFRQVbt27FyJEjERwcjFq1amHo0KEVNnjs27cPc+bMgY6ODnx9fTFq1Chs27YN3t7eFcZ35swZhIeHY+nSpZg3bx6SkpLg5ubGzduwe/duLF26FH369IG/vz/WrVsHOTk5zJgxAy9fvuTKKSoqwvTp02FtbY1t27ahU6dOWL16Nc6fPw8AUuNfsWIF4uPjMXfuXOzYsQM9e/bEmjVrEBoa+iOHnBBCCCGE/IvQEAZCyC9z9OhRNGrUCBYWFgAAW1tbbNu2DWFhYXB2dv7uchljKCoq4v7/5s0bhIeH49y5cxgzZgxkZGSQlZWFunXrYsGCBejUqRMAwMjICE+fPsWRI0d45dWuXRuLFi2CgoICL11dXb3cUAkAKCkpga+vL/r27YulS5cCAMzMzPDhwwckJSVV+PaDvLw8BAYGolmzZgBKh2J4enoiPj4e3bt3R0ZGBtzc3ODp6cmt06JFC9jZ2SE9PR39+/fntj958mQMGTIEANCpUyecPXsWMTExMDY25mL+Ov7U1FSYmppiwIAB3LFQUlJCw4YNq3rYCSGEEELIvxw1IBBCfomCggKcOHEC/fr1w+fPnwEAampq0NbWRnBw8A81IBw9ehRHjx7lpSkoKMDe3h5eXl4ASm+e9+zZA8YYMjIy8OTJEzx8+BDp6ekoLCzkrdu+fftyjQff8ujRI7x58wZ9+vThpU+aNAmTJk2qcD0DAwOu8QAAunfvDgUFBaSlpaF79+747bffAAAfPnzAw4cP8eTJE1y4cAEAysUsaRSR7Luqqiry8vIq3LaRkREOHjyIrKwsWFhYwMLCAhMnTqzyPhNCCCGEEEINCISQXyImJgY5OTk4ePCg1Nc3XrlyRerT/aro2bMn94YBGRkZKCkpoWXLluUaAY4fP47169fjxYsXUFFRQceOHVGnTp1yPQSUlJSqtf2cnBwAQKNGjaq1XpMmTXifZWRkoKqqig8fPgAAnj59ioULF+L8+fOQl5dHu3btoKmpCQDlYlZUVOR9lpWV/eYrLOfPnw91dXUcP34cy5Ytw7Jly6Cvr4/Fixdz2yCEEEIIIeRbqAGBEPJLhISEoE2bNlwXf4mioiJ4enoiODj4uxsQGjZsCLFY/M08aWlpmDNnDkaOHInRo0ejadOmAIA1a9bgypUr37VdiXr16gFAuQkQX79+jXv37vF6B3xN0vAgIRl+oaqqipKSEri7u6N27do4cuQIOnbsCDk5Ody/fx9hYT/+Sk8FBQWMHz8e48ePx/PnzxETEwM/Pz/MmjULJ06c+OHyCSGEEELI/z6aRJEQ8tNlZ2cjISEBVlZWMDIy4v0zNTVFjx49EBERwT15/xUuX77MzRUgaTwoLi5GcnLyN5/US8jKVlw9tmvXDioqKoiOjualBwcHY8KECRWul56eztvn6OhoFBYWomvXrnj37h0ePXoEBwcHiMViyMmVtu/Gx8cDKN8DoTrxf/nyBX379uXe1NC8eXM4OzvDysoKL168qFa5hBBCCCHk34t6IBBCfrqwsDAUFxfDyspK6nIbGxtERkb+lCfrFdHR0QEALFu2DDY2Nnj//j327t2L27dvgzGG/Px81KlTp8L169evj/T0dFy8eBGdO3fmLZOTk8OkSZPwxx9/oGHDhrC0tMTdu3cREBCAsWPHonbt2lLL/PTpE9zd3eHh4YHXr19j3bp1MDU1RdeuXQGUTpi4e/duNGnSBMrKykhISMDu3bsBgJtHoqrKxq+jo4PNmzdDXl4eQqEQjx49QmhoKPr27VutcgkhhBBCyL8XNSAQQn66kJAQaGpqQkNDQ+pyc3NzqKqqVviKxJ/ByMgICxcuRFBQEMLDw9G4cWMYGRlh5MiRmDhxItLS0mBmZlbh+u7u7vD19cW4ceNw+vTpcstdXV2hqKiIwMBAHDx4EM2bN4eXlxdGjx5dYZmGhobQ19fHrFmzICcnh4EDB2LmzJnccj8/P/zxxx+YPXs2FBQU0L59e2zZsgUrVqzApUuXMHz48Crvf9n4lyxZgoYNGyIwMBCvXr1Co0aNMHToUEydOrXKZRJCCCGEkH83GVbdfrGEkJ/m1q1b6NixY7n0gsJiKMjXqoGI/lkx/C9xdXVFrVq1sHPnzpoOhRBCCCGE/AtUdK/xI2gOBEL+gf4JN+7/hBgIIYQQQggh/xzUgEAIIYQQQgghhJBK0RwIhBDyN9izZ09Nh0AIIYQQQsgPoR4IhBBCCCGEEEIIqRQ1IBBCCCGEEEIIIaRS1IBACCGEEEIIIYSQSlEDAiGEEEIIIYQQQipFDQiEEEIIIYQQQgipFDUgEEIIIYQQQgghpFLUgEAIIYQQQgghhJBKUQMCIf9AJUUFNR3CPyKGX4kxVtMhAPjnxPFP8jOOCR1XQgghhJCfT66mAyCElCcrp4CHfwyp0RjazT/63evOnTsXoaGhFS4PCgqCiYnJd5dfXSEhIZg3bx7i4uKgrq6OBw8eYP78+Th48OBP31Zubi527NiB06dPIzMzEwoKCujQoQOGDh0KOzs7yMjIAAA+fPiAZcuWwdHREQYGBtXaxtq1a3H48GEUFBRgxYoV6NSpE2bMmIG//voLDRo0QExMDOTl5cut9+bNG2zduhUxMTHIysqCkpIStLS04OLigl69elV5+8nJyRg9ejSCg4Ohp6dXrdgrc/jwYTx69AizZ8/mPi9YsACJiYlQU1OrdP0fOa4/S1FREbS1tculy8nJQVlZGSKRCFOnToVYLK6B6H6d6p4rQgghhPz3oQYEQsgvoa6ujo0bN0pd1r59+785Gr7IyEhcvnz5p5dbUlKCcePG4fnz5xg3bhw0NDSQn5+P2NhYzJ8/H/fv38ecOXMAALdu3cLx48cxbNiwam3j7t272L59OxwdHTFw4EC0b98eAQEBuHr1KtatW4emTZtKbTz49OkThg8fjlq1asHT0xOtWrXCx48fceLECUycOBELFy6Es7NzlWLQ0dFBcHAwOnToUK3Yq2LLli0wNjbmPvfs2RMdOnSAiopKldb/3uP6KwwfPhyDBw/mPhcUFODevXvYsmULxowZg8jISKiqqtZghIQQQggh1UMNCISQX0JBQeGnP53+p7tw4QLS09Oxb98+dO7cmUvv0aMHZGVlsXv3bowbN+6HbhpzcnIAANbW1tw2cnJyoK6ujn79+lW43qlTp/D48WPExsaiWbNmXHqvXr3g4eGBDRs2wMnJCbKylY9sU1ZW/tvOraqq6n/tTXazZs3KHSdDQ0M0b94cnp6eOHv27D+ioYMQQgghpKpoDgRCSI35/Pkz1q5diz59+kAkEqFTp04YO3Ysbt++zcsXFxcHR0dH6OnpoVu3bli+fDk+ffoEAPDx8YGWlla5soVCIfz8/Mql+/j4cD0jhEIhfHx84OXlBUtLy3Lj5qdPn44hQ6o+lOTNmzcASnsilOXi4oKpU6cCKB0CMGLECACAs7MzRo0aBaC06/uWLVtgZWUFsVgMfX19DB8+HGlpaQCAP//8E66urtx6vXv3hrm5OUJCQvDs2bMK9/nr2IqLi8stmzBhAjw9PVFYWMilpaenY9SoUdDX14eJiQnmzp2Lt2/fcvELhUJcuXKFy3/79m2MHTsW+vr66Ny5M6ZNm4aXL19yyyXrpKamYsSIEdDR0YGZmRnWr1/PHS9zc3NkZmbiyJEjEAqFyMrKwuHDhyEUCvHq1SsAwOvXrzF9+nSYmJhAR0cHNjY2CAsL++Zxrar09HSMHDkSXbp0gaGhIaZNm4bnz59zyw8fPgx9fX3s27cPxsbGMDIyQkZGRrW2AQD16tUDAG44CwC8ffsW8+fPh7GxMXR0dDB8+PByvWTi4+Nhb28PPT09GBoaYuLEiXj06BEvT2RkJGxtbSEWi9GtWzesXbsWBQX8+UxOnToFR0dH6OvrQyQSYcCAAQgODuaWS85VcHAwLCwsYGBggPT0dABATEwMhg0bBj09PZibm+OPP/5AXl4er/y0tDTY29tDLBbD0tISu3bt4i3Py8vDqlWr0K1bN4jFYtja2iI2NpaXx9zcHKtXr4aLiwt0dHSwfPnyahxhQgghhPwq1IBACPllioqKyv37+iZ99uzZOHbsGDw8PBAYGIh58+bhzp07mDlzJpcvJiYGHh4eaNKkCTZu3IipU6fi+PHj+O23374rJnt7e+6pb3BwMOzt7TFkyBBkZmZyN+pA6VwGUVFRsLOzq3LZXbp0gaKiIiZPngxvb29cvHgRX758AQBoaGhwvQ90dHSwcOFCAMDSpUvx+++/AwBWrVqFgIAADB8+HDt27MCSJUvw5s0beHl54cuXL3BycuKtt2nTJmzduhUWFhZQV1dHcHBwhQ0eZmZmkJWVhYuLC/z8/PDXX3+hqKgIAKCrq4uxY8eidu3aAIBr165xN+Lr1q3DggULkJKSggkTJkgt+8GDB3BycsKnT5+wdu1aLFmyBDdv3oSLiwtyc3N5eSU3/wEBAejXrx/8/f25BoCtW7dCTU0NlpaWCA4OltrzYMaMGXj8+DGWLl0Kf39/CAQCzJ49G2lpaRUe16qIj4+Hs7MzFBUVsWbNGsyfPx/Xrl2Do6Mj13ACAPn5+Thw4ABWrVqFefPmoWXLlhWWWVJSwrv2P336hLS0NCxbtgwNGjRA9+7duTJHjhyJhIQEzJw5Exs3boSysjJGjhyJGzduAAAeP36MSZMmQVdXF1u3bsXSpUtx//59eHp6cn8rx44dg5eXFzQ1NeHn54dx48Zh//793HwSAHDmzBlMnToV+vr68PPzg4+PD5o1a4aFCxdy25LYsmULFixYgAULFkAkEiEqKgrjx49Hs2bNsHHjRnh5eeHYsWNYsGABb71FixbBxsYG/v7+EIlEWLFiBZKTkwGUTm45YcIEHDlyBOPGjYOvry8EAgHGjx+PmJgYXjm7d+9Gly5d4OvryxsKQgghhJCaQ0MYCCG/xNOnT6VOJLd48WI4OTnhy5cvyMvLw++//851vTc0NERubi5WrVqFd+/eQVVVFT4+PhCJRNi0aRNXBmMMQUFB5W5Oq0JdXR3q6uoAwHUvV1NTQ9OmTREWFoYuXboAKH1KyxiDlZVVlctu2rQptmzZgvnz5yMgIAABAQGQl5eHvr4+Bg0aBDs7O9SqVQvKysrcPBAaGhrQ0NAAAGRnZ2PmzJm8uQjk5OQwbdo0PHjwAFpaWrz1OnbsCKC0m39lQ0a0tbXh7e2NZcuWYePGjdi4cSMUFRVhaGiIIUOGoG/fvlzeLVu2oEmTJggICICCggKA0mELy5Ytk/rE3cfHB/Xq1UNQUBAUFRUBAJ07d0bv3r2xf/9+uLu7c3mdnJzg6ekJAOjatSuioqIQExMDW1tbaGlpQUFBAaqqqhXuS1paGqZOncpN+mhoaIhGjRpxExRKO65VsX79egiFQmzZsoXrGdCpUyf0798fQUFBmDFjBoDSRoFJkybBwsKi0jL//PNP/Pnnn7y02rVro0uXLlizZg2aNGkCoHSSz3v37iE0NJQ7p+bm5rCzs8Off/6J7du34+rVq/jy5QvGjx+PRo0aASi93mJjY5GXl4c6depg3bp16NWrF1auXAkA6NatG5o2bQovLy+MHj0aurq6ePjwIezt7bm5OIDSOS1MTExw8eJF3t+sq6srevfuzX328fGBnp4eNmzYwKWVlJRg9+7d+Pz5M5c2d+5cruFNT08P586dQ0pKCkxMTBAfH4/k5GT4+vpy59Dc3Bzv37/HmjVr0KNHD66ctm3bYsqUKZUeZ0IIIYT8fagBgRDyS6irq2Pz5s3l0lu0aAGg9EZqx44dAICXL1/i0aNHePz4MfcUsrCwEPn5+bh58yamTZvGK2Po0KEYOnToT4u1Vq1asLGxwYEDB7Bw4UIoKCggNDQUPXr0qPLkfRLGxsaIiopCWloakpKSuHkRUlNTceLECWzfvp27KS9L0kjy5s0b7nhER0cDAG94wfcaMGAAevXqhZSUFCQnJyM1NRXx8fGIi4vDwIED4e3tDQC4dOkSBgwYwIvT3NwcZ8+eBVDaOPS18+fPo0ePHpCXl+d6NTRq1AgikQjJycm8BoROnTrx1lVXVy/XBf5bDA0NsWHDBly/fh3dunWDhYUF72b4e+Tm5uLWrVuYMWMGb1hBq1atoK+vj9TUVF5+yU1+ZVxcXGBjYwPGGG7evIl169bByMgIa9euhZKSEpcvJSUFzZs3R4cOHbjjBwDdu3dHUFAQioqKoK+vDwUFBdjZ2aFv374wNzeHoaEh9PX1AZROrvnq1StYWlryyjA3N4ecnBySkpKgq6vLNd7k5ubi0aNHePr0Ka5duwYA5YY6fL2fnz59wu3btzFr1ixeHgcHBzg4OPDSvp7/Q0lJCQ0bNsSHDx+4fZWXl0e3bt14cfbs2RMLFixAVlYW18CnqalZpeNMCCGEkL8PNSAQQn4JBQWFSl9Tl5CQgBUrVuDhw4eoW7cuNDU1uRsrxhjev38PxtjfMomenZ0d/P39ER0dDW1tbaSnp8Pf3/+7ypKVlYWhoSEMDQ0BlE5yuH79egQHByMsLAz29vZS1/vrr7+wZMkSXL9+HYqKimjfvj034WHZ+Rm+l4KCAszNzWFubg6gtPFm6dKlOHnyJIYMGQITExO8f/+ee8pdGcl5Cg0NlfrqzrK9ACQ9FCRkZGSkzhlRkY0bN2LLli04ffo0Tp8+DVlZWZiZmWHp0qW8ySGr4/379wAg9dWDjRs3xp07d3hpX9/8f0vTpk25vwEdHR20bNkSbm5umDFjBrZs2cLle/fuHTIzM6X22JHE17p1a+zZswcBAQE4dOgQdu3ahfr168PV1RWTJ0/mJtf87bffpA7vyc7OBlA618Lvv/+O6OhoyMrK4j//+U+5Rh1p+ykpvyrXRdlzLCsry53jnJwcFBYWQkdHR+q62dnZXANCVY8zIYQQQv4+1IBACKkRT58+xcSJE9G7d28EBASgZcuWkJGRwb59+5CQkACgtNs8UHqD9bXc3FxcvnwZenp6Um9AJRMsVkebNm1gYGCAU6dO4enTp2jcuDHMzMyqVcbkyZPx+fNnrmeFhIqKChYtWoTw8HDcv39f6rofPnyAm5sbxGIxIiIi0LZtW8jKyuLcuXM4c+ZMtfenrKFDh0JTU7PcZHRNmzbFsmXLEBUVhfv378PExATKysq8cf9A6eSLCQkJ5W78ZGRkoKysjF69ekl9DaRkXoWfpX79+pgzZw7mzJmDBw8e4Ny5c/Dz88Py5cvh6+v73WUC4CZq/NqrV6/QsGHDH4pZwszMDA4ODggODsaRI0e4XjT16tWDQCDAihUrvhmfnp4e/Pz8UFBQgEuXLuHAgQPw9fWFlpYW17NnwYIFUod/SBrhpk6dioyMDOzevRu6urpQUFBAbm4uDh8+/M3YJRM/lr0uJH+Lkp4QlVFWVoaKigq2b98udXm7du2qVA4hhBBCagZNokgIqRHXr1/Hly9f4OnpiVatWnFdxyWNByUlJVyvBEk3fomoqCi4ubnh48ePUFZWBmMMWVlZ3PJLly59c9u1atWSmj5kyBDEx8fj9OnTGDRoUIX5KtKyZUskJyfjr7/+Krfs5cuXyMvLg0AgkBrD/fv38f79e4waNQoaGhrc6xTj4+MBSH+zQ3U0b94cERERUucwkMzkL4nNwMAA8fHxvC7mFy9ehIeHB548eVJu/S5duuDBgwcQiUQQi8UQi8Xo2LEjtm7diqSkpGrF+a3XSGZmZvKGUmhoaMDd3R1du3bFixcvAFR8br+lXr166NixIyIiIng9PTIyMnDlyhUYGBhUu8yKzJw5Ew0bNoS3tzfXrd/Q0BAZGRlQV1fnjp9YLEZUVBT2798POTk57Ny5Ez179kRBQQEUFBRgbGyMJUuWAACysrLQvn17qKio4Pnz57wyGjRoAG9vb+4cp6enw8rKCl26dOGGqFTlGqtfvz46dOhQbqLDyMhIjBs3rsqNdoaGhsjJyUHt2rV5cV6+fBn+/v5Veo0oIYQQQmoOfVMTQmqEtrY25OTksHbtWiQnJyMmJgaTJ0/mXucmGRfv5eWFK1euYObMmUhISMDhw4exatUq2NjYoHnz5txkdvPnz8f58+dx9OhRLF68GHXr1q1w25KnqSdPnuTdUEsmc7xx40a5ty+8ffsWV65c+ebEjePGjUPr1q0xcuRIrFu3DgkJCbh48SL27t0LFxcXaGtrw9ramhdDbGwsbt++DQ0NDdStWxe+vr6Ii4tDQkICfvvtNxw6dIh3PL7XjBkzULt2bdjb28PPzw/JyclISUlBQEAAJk6ciF69eqFr164ASl/rmJWVhfHjxyM2NhYnT57Eb7/9BmNjY6lPtydNmoTbt29jwoQJiI6ORlxcHDw8PBAbG1vl+QIk6tevj5s3byI1NZV7g4VEixYt0KxZMyxbtgwhISFITU3Fjh07kJiYyJ27sscVqNq5mzZtGu7cucPt8/HjxzFmzBg0aNCAeyPFz1C/fn1MmzYNb9++5ea8GDp0KBo3bozRo0fj+PHjSElJwR9//IGtW7dyjWtdu3bFy5cv4eXlxbs+6tSpAwsLC8jLy8PLywu7du3C6tWrkZSUhPDwcIwb5HyRXAAAIABJREFUNw7379/nzoNYLEZoaChOnDiBCxcuwNfXF7/99htkZGQqvca8vLyQlpaG2bNnIyEhAYcOHcKaNWtgZ2eHpk2bVmn/LS0toaenBw8PDxw6dIiLYfXq1WjQoEG54Q+EEEII+YdhhJAac/PmTanpxYVf/uZIfm4Mc+bMYb169ao036lTp5iVlRUTi8XMzMyMTZo0iaWmpjKhUMgOHjzI5Tt37hyztbVlIpGIWVhYMG9vb5afn88tDw0NZX369GHa2tps0KBBLDExkfXt25f5+voyxhg7evQoEwgE7MWLF4wxxl6+fMmGDBnCtLW12ZIlS3gxubu7s6FDh5aL9dChQ0wgELCLFy9+c5/ev3/PvL292cCBA5menh4TiUSsX79+bMOGDezTp09cvuLiYjZ16lQmEomYjY0NY4yx5ORkZmtry3R0dJiJiQkbO3Ysu3TpEtPT02MbNmxgjDGWkpJSLo6qHu+srCy2dOlS1qdPH6arq8t0dXXZoEGDWGBgICsoKODlvXjxInNycmJisZiZmpqyRYsWsffv3zPGGEtKSmICgYBdvnyZy3/16lU2evRopqenxzp16sSGDx/OkpOTueXS1mGMMUdHRzZmzBju8/Hjx5mxsTETi8Xs6tWr3HHPzs5mjDGWnZ3N5syZw0xNTZm2tjbr3bs38/f3ZyUlJRUe16qeu4SEBObg4MDEYjEzNDRk06dPZ5mZmdzysrFUpLCwkAkEAubv7y91eXFxMbO1tWVaWlrszp07jLHSa3L27NnMyMiIicViZmVlxfbu3VsuvmHDhrFOnToxPT095uLiwtLS0nh5wsLC2ODBg5lIJGJdu3ZlXl5e7PHjx9zyp0+fsnHjxjEDAwNmYPD/2LvzqC6q/4/jT0BQBBcQwTKXkr4fAQFxQXMFd80yzRYXEncF9wVX3HM3UxBx3/2671aW5lbuZi5l/lTck8wF3FAR+P3hYb5+BP1oYli+Hud4Dp87M3fec2c6nfuee++UTPnwww9T1q1blxIcHJzSuHHjlJSUx9+rlJSUlO+++874bzEgICDliy++SLl79+4T26dixYop4eHhxu8bN26kDB06NKVChQrGPZw0aVJKYmLiY48RERGRZ/e4vsbzsEpJyaCVuUTkmR09evSZ39DKi3Pr1i0qVapEr1690qwsDw/e4gcHB1tcHFJePrp3IiIi8qp5EX0NLaIoIq+88+fPs2rVKrZv3469vb0xzeBhx44d4+jRo8Y6AfLPoXsnIiIikjGUQBCRV561tTXz5s3DwcGBcePGpTsP28XFhVmzZmX4VwXkxdO9ExEREckYSiCIyCvv9ddfZ/fu3U/cJ0+ePH9TNJLRdO9EREREMoa+wiAiIiIiIiIiFimBICIiIiIiIiIWKYEgIiIiIiIiIhYpgSAiIiIiIiIiFimBICIiIiIiIiIWKYEgIiIiIiIiIhYpgSAi/wopKSmZHYKIiIiIyL+aEggiL6F7SYmZHcJzxRAUFITJZDL7V6xYMapVq8bo0aO5e/duhsX5xx9/0LZtWy5cuPDE/VasWIHJZCI2NjbDzi0iIiIi8irJktkBiEhadja2fLy4fabGsOSTyc91vLe3N/379zd+3717l7179zJp0iQuXrzI+PHjnzdEAHbt2sWWLVsIDw/PkPpERERERCR9SiCIyAvh6OhI8eLFzcrKlClDbGwsy5Yto0+fPri6umZSdCIiIiIi8qw0hUFE/laenp6kpKRw8eJF7t+/z+zZs3n33Xfx8fGhatWqTJ48maSkJGP/s2fP0q5dO8qUKYOvry+ffPIJW7duBR5MSwgLCwOgatWq9O7dG4Dk5GSioqIICAjA19eXkJAQ4uPj08SydetWPv30U/z8/HjnnXfo378/165dM9vn559/pnnz5pQoUYJ33nmHsLAwrly58qKaR0RERETkpaUEgoj8rU6fPg1AgQIF6NevH2PHjqV27dpMnjyZDz74gEmTJhnTEZKTk2nbti0JCQmMHj2aqKgocufOTfv27Tl79iwBAQF07NgRgMjISEJCQgAYM2YMkyZNomHDhkRGRpI7d27GjRtnFsfy5ctp06YNBQsWZMKECXTt2pXNmzfz2WefkZCQAMCvv/5K06ZNSUpKYvTo0YSHh7Nv3z7atWv3N7WWiIiIiMjLQ1MYROSFSElJ4f79+8bva9eusW3bNhYtWkStWrW4cuUKq1atIiwsjJYtWwJQvnx5smXLxtixYwkODsbJyYmYmBhCQkKoXLkyAD4+PkRGRnL37l0KFixIgQIFAPDw8OCNN97g+vXrzJs3jxYtWtChQwcAKlasyKVLl9i+fTvwIDHxxRdfEBAQwOjRo40YTSYTH3/8MStWrKBJkyZER0eTJ08epk+fjp2dHQC5c+dmwIABnDlzhkKFCr34hhQREREReUloBIKIvBC7du3Cy8vL+FehQgXCw8MJCAhg0KBB7N27F4C6deuaHff+++8DsHfvXlxcXHB3dyc8PJxevXqxdu1akpOT6dOnD2+//Xa65/35559JTEykatWqZuW1a9c2/j516hSXL1/m3XffNdvH19eXQoUKsXv3bgD2799P5cqVjeQBQLly5di4caOSByIiIiLyytEIBBF5IXx8fBgwYAAAVlZWZMuWjfz582Nvbw9grEmQJ08es+NSf9+4cQMrKytmzpzJ5MmT+e6771i1ahW2trZUq1aNwYMHkytXrjTnTa3X2dnZrDxv3rzG33FxcWnKHj7/zZs3jf0erUdERERE5FWlEQgi8kI4ODjg7e2Nt7c3xYoVw93d3UgeAOTMmRMgzYKEf/75JwBOTk4AuLm5MWjQIH744QdWrVpFy5Yt+fbbb5k4cWK650097vLly2blqUkDwEg8pJ7r0fOn1uHo6MjVq1fNticnJ7NlyxYtpCgiIiIirxwlEEQkU5QuXRqAdevWmZWn/i5ZsiSHDh2iXLlyHDp0CCsrKzw8POjatSv/+c9/uHjxIgA2NjZmx/v5+ZEtWza++eYbs/LNmzcbf7/11lu4uLiwfv16s30OHTrEuXPnKFGihBHDDz/8QGJiorHPTz/9RNu2bTl16tTzXL6IiIiIyD+OpjCISKb4z3/+w/vvv8/48eNJSEjAz8+PAwcOEB0dzfvvv4+7uzv37t0je/bshIWF0bFjR1xcXNixYwdHjx6lefPmAOTIkQOA7777jkqVKlGkSBFCQkL48ssvyZYtG/7+/mzZssUsgWBtbU2XLl3o378/YWFh1K1blz/++IMJEybw5ptvUr9+fQBCQkL49NNPadeuHU2bNuX27dt88cUX+Pv7G0kGEREREZFXhVVKSkpKZgch8qo6evQoHh4eacrvJSViZ2ObCRFlTAxBQUHY2Ngwe/bsJ+53//59oqOjWbFiBZcuXeL111/nww8/pFWrVsbIgjNnzjBu3Dj27dvH9evXKVy4MJ999hkff/wxAAkJCYSGhrJnzx4qVKhAdHQ0APPmzWPOnDn88ccf+Pn5Ubt2bQYNGsTWrVvJly8fAOvXr2fatGmcOHGCXLlyUbVqVbp27WpMYYAHCymOHz+eQ4cOkTNnTqpUqUL37t3TXX9BRERERORl8bi+xvNQAkEkE72I/6hFREREREReRF9DayCIiIiIiIiIiEVKIIiIiIiIiIiIRUogiIiIiIiIiIhFSiCIiIiIiIiIiEVKIIiIiIiIiIiIRUogiIiIiIiIiIhFSiCIiIiIiIiIiEVKIIiIiIiIiIiIRUogiIiIiIiIiIhFSiCIiDyjlJSUzA5BXlKZ9Wz8E57Jf0KMIiIi8mRKIIi8hJLv3cvsEDIkhuPHjzNw4ECqV6+Or68vpUqVIigoiFWrVr3wzkRQUBDBwcEZXu/Jkydp1KhRhtf7vHr37k316tUfu7169er07t37uc9z/vx5TCYTq1evfu66MovJZCIqKirD6928eTO9evXKkLoSExOZPXs29evXp3jx4vj5+VG/fn1mzpzJvYf+27x37x4jR45k7dq1GXLeF9U2Bw4coG3bthler4iIiPy9smR2ACKSlrWdHT/W+zBTYyi/evlzHb9mzRr69euHyWSiTZs2FCpUiJs3b7Jx40b69OnDgQMHGDx4cAZF+/fZsGEDBw4cyOww5CU0Z84ckpKSMqSuvn37snnzZtq0aYOXlxdJSUns27eP8ePHs3//fiZNmgTA1atXmTVrFiNGjMiQ874oy5Yt48SJE5kdhoiIiDwnJRBEJMOdPHmS/v37ExgYyPjx47GxsTG2ValSBU9PT4YOHUq9evUoUaJEJkYq8vK5cOECa9asYfjw4Xz44f8SiZUqVcLZ2ZkRI0Zw6NAhfHx8MjFKEREReRVpCoOIZLjp06djY2PDoEGDzJIHqRo1akT16tW5c+cO8L9h8bNnz6ZmzZoUL17cGJK9YcMGGjVqhJ+fH8WKFaN27dosXLjQrL7ff/+dDh06ULJkScqXL8+sWbPMtj9u2P2jw/5v377NmDFjqFGjBsWKFaNEiRK0bNmS3377DYCIiAgmTJgAPBjqHRERAUBycjLR0dFUq1aNYsWKUatWLZYuXWqxneLi4hg6dChVqlTB29ubBg0a8O2335rtYzKZmDRpEg0aNMDHx4epU6darPdpVKlShcjISEaOHEm5cuXw9fWlZcuWnDlzxmy/b7/9lvfffx8fHx/q169vtMXDLly4QJcuXShdujTFixenZcuWZm+bn3R/N27cSL169fDx8eG9997jxx9/xNPTkxUrVgCwYsUKvL29WbRoEeXKlaNMmTKcPXuWpKQkpkyZQt26dfHx8aF48eI0atSI3bt3m8W2Z88ePvnkE3x9falZsyY7duww275ixQpMJhOxsbFp2qdfv37G73PnztGzZ08qVKiAl5cX5cqVo3fv3sTHxwMPpszs3LmTPXv2YDKZjDiuXbtG//79eeedd/Dx8aFRo0bs37//iffmypUrwIPn6lHvv/8+3bp1I2fOnJw/f57KlSsD0KdPH6pUqWLE8uj0nd27d2Mymdi3b99Tt83Txm8ymVi0aBF9+vShdOnS+Pn50blzZ+M6evfuzbJly7hw4QImk8m4tyIiIvLPoxEIIpLhNm3aRNmyZXF2dk53u42NDZGRkWnKIyMj6devH9myZaN06dJs2rSJTp06ERwcTKdOnbhz5w4LFy5k8ODBFCtWDB8fH27fvk3Tpk3JkiULQ4cOxdramokTJ3L27FlKlSr1THGHhYVx4MABunXrRoECBThz5gwTJkygR48erF27lo8++og///yTxYsXs3jxYvLlywfAoEGDWLFiBe3bt8fX15cff/yR8PBw7ty5Q1BQULrnSkhIoHHjxly/fp3OnTvj6urK2rVr6dixI6NGjeKDDz4w9o2OjqZ79+4ULFiQQoUKPdM1Pcns2bMpWbIkI0aMID4+ns8//5w+ffoYCZrvv/+eTp068d5779GzZ0+OHj1Kz549zeq4evUqjRo1Inv27AwaNIisWbMyffp0GjduzMqVK8mfP7+x76P3d8eOHXTs2JFatWrRvXt3jh49SseOHdNMA0hMTGTu3LmMGDGCa9euUbBgQUaMGMGSJUvo0aMHb7/9Nn/88QeTJk2ic+fObN68GXt7e3755RdatGhB2bJlmThxIufPn6dbt27P3E4JCQk0bdoUV1dXBg0ahKOjIwcOHCAyMpJs2bIxaNAgBg4cSO/evUlKSmLgwIG4u7tz9+5dgoODuXLlCt26dSNv3rwsWrSI4OBgFixY8NgRBEWLFsXNzY1hw4Zx9OhRAgMD8fPzw9HREWdnZ2MtgXv37jF58mTat29P+/btqVGjxlNf09O0zbPEP3bsWKpXr86XX37JmTNnGDlyJHZ2dowZM4aQkBDi4+M5fPgwkZGRFCxY8JnvgYiIiLwclEAQkQwVHx9PfHw8hQsXTrPt/v37Zr+trKzMRijUqVOH+vXrG79PnjxJgwYN6NOnj1Hm5+dHmTJl2LNnDz4+PqxcuZKLFy+ybt06ihQpAoCvr+8TFxRMz927d0lISCA8PJxatWoB4O/vz82bNxk5ciTXrl0jX758RtKgePHiAJw6dYolS5YQFhZGixYtAKhQoQJJSUlMmDCBhg0bYm9vn+Z8K1as4OTJkyxdutToiFWuXJn4+HjGjBnDe++9Z7SNv7//C1kQMnfu3ERFRRnnOXv2LBEREdy4cYMcOXIwadIkfH19GTNmDAAVK1YEYNy4cUYdc+bMIT4+niVLlhhtU6FCBapXr87kyZMZNmyYse+j97dz5854eXkxfvx44MEQfWtra8aOHWsWZ0pKCqGhocbbdoBLly7RrVs3mjRpYpRlzZqVjh07cvz4cXx8fJgyZQp58+Zl8uTJ2NraAuDk5ETXrl2fqZ1iYmLInz8/o0eP5o033gCgbNmyHDx4kL179wLg7u6Oo6MjSUlJxrOxZMkSjh07xtKlS/H29jausWHDhowfPz7NSJlUdnZ2TJs2jV69erFgwQIWLFiAjY0NXl5e1K5dmyZNmpA1a1bs7Ozw9PQEoGDBgsbfT+Np2mb16tVPHX/RokWNdRjKly/P4cOH2bhxoxGbs7MzdnZ2RtuIiIjIP5MSCCKSodIbdg1w+PBhGjZsaFbm7+/PvHnzjN8eHh5m29u0aQPArVu3OHXqFGfPnuXw4cPAg7fSAPv27aNQoUJG8gDgtddee+aOStasWZkxYwYAf/zxB6dOneL06dNs3rzZ7HyP2rVrFykpKQQGBpolSKpUqcKcOXM4dOgQZcqUSXPc3r17KVSoUJq30O+99x7btm0jJiaGt99+G0jbLumxsrJ65n18fX3NEjipCYDbt29ja2vLL7/8kuatdO3atc0SCDt37sTLywsXFxfj+rNkyUL58uXTDIl/+Dru3bvHgQMH6NKli9k+derUSZNAgAcd1IelJh2uXr1KTEwMZ86cSXOv9u/fT9WqVY0OMkCNGjXSnVbzJF5eXixcuJDk5GROnz7NmTNnOHHiBDExMU88bufOnbi5ueHh4WH2bAQGBjJlyhTu3buHnZ1duseaTCZWrVrF4cOH+eGHH9i9ezcHDhzg0KFDLF++nPnz5+Pk5PRM1/Gwp2mbZ4n/0bVM8uXLR0JCwl+OT0RERF5OSiCISIZycnIie/bs/P7772bl7u7uLFu2zPg9ZMiQNMdmz57d7PfVq1cZOHAgGzduxMrKikKFChnTElI/AxkfH5/uVIm8efNy7dq1Z4p9+/btDB8+nJiYGBwcHChatKgR0+M+OxkXFwdgjFp41KVLl9Itj4+Px8XFJU15atmNGzeMskfbJT329vZmn/d7VGJiYpqRENmyZTP7bW39YFmclJQU4uPjSUlJSdNJdXV1NfsdFxfHmTNn8PLySnPOhzunj15HXFwcSUlJaerPmzdvuvE7ODiY/T58+DCDBw/m8OHD2Nvb4+7uzuuvv27ED+k/G1myZPlLHe9Zs2YRHR1NXFwcLi4uFCtWDHt7e27fvv3YY+Li4oiNjU23beDB+gJubm5PPK+3tzfe3t60b9+ehIQEZs2axYQJE5gxYwY9evR45utI9TRt8yzxp/csPS6ZKCIiIv9cSiCISIarUqUKW7Zs4fbt20an0d7e3hgGDQ86hJY+edejRw9OnTrF7Nmz8fPzw87OjoSEBJYsWWLs4+TkxJEjR9Icm9qxh/+9eX+0Q/Nw5+/s2bOEhoZSvXp1pk6dyhtvvIGVlRULFixg+/btj40xR44cAMyfPz9NJwowhrw/KmfOnBw9ejRNeWrC4Vk7uS4uLly7do3ExMQ0Hff79+9z+fLldBMWj5M7d26sra2NhfBSPdyuAI6OjpQtW/aZO7N58uTB1taWq1evmpU/er703Lx5k1atWuHh4cH69et56623sLa2ZuvWrWzYsMHsGh6tLzU5kir12Xj0Wbx165bx99q1axk5ciRhYWHUr1/f6Hh37tyZX3/99bFx5siRgyJFijBq1Kh0tz/uHo8aNYrNmzfzzTffmJXb29sTEhLChg0bLH4S8dHreTTR8TRt81fjFxERkX8vfYVBRDJc69atuXfvHuHh4ekO/b9+/Tp//PGHxXr2799PrVq1KFOmjDFUetu2bcD/kgFly5blzJkzZp3xq1ev8vPPPxu/HR0dAbh48aJRlpiYyKFDh4zfR44c4e7du7Rr144CBQoYHcvU5EHq+R4d/p46IiI+Pt54W+zt7c3FixeZOHHiY4dx+/v7c+bMGbMYANavX0/evHmfebFEf39/7t69y6ZNm9Js27JlC4mJielOpXicrFmz4ufnx4YNG8xGX3z//fdpznvq1CmKFClidv1Llixh/fr1j63fxsYGPz+/NPGmzpt/kpiYGOLi4ggODsbd3d0YOZH6bKTG+84777B582bjax/w4H4+/Eym92ycPHnSLFGyf/9+nJycaNmypZE8uHXrFvv37zdLSj36bJQuXZrff/8dV1dXs7bZtGkT8+bNS5PoSVWoUCFOnTrFV199lWbbrVu3uHTpEiaTCfjfqJGHOTo6pvmqxKNfTniatvmr8afnWaeNiIiIyMtJIxBEJMMVLVqU0aNH07dvXz788EM++ugj3n77be7evcuePXtYtmwZCQkJZgvgpcfHx4c1a9bg4eGBm5sbP/30E1OnTsXKysromNerV4+5c+fSvn17unbtioODA5MnTzbr2OXKlQs/Pz/mzJlDgQIFyJUrF3PnzuXOnTtGJ8jLy4ssWbIwZswYgoODuXv3LitWrGDLli0AxvlSRxysW7eO4sWLU7RoUerWrUvfvn05d+4cHh4eHD9+nPHjx+Pl5WUMq39U/fr1mTdvHiEhIXTu3Bk3NzfWrVvHtm3bGDZsWLodwycpVaoUgYGB9OnTh5iYGPz8/Lh79y4HDx5k9uzZ1KpVi5IlSz5Tnd26daNZs2Z06tSJjz76iJiYGKZMmWK2T/PmzVm1ahUtWrQgODiYnDlzsmrVKlavXm0sqvc4HTp0oFmzZnTv3p0PPviAkydPMnHiRCD9jnGqN998E0dHR6KiorCyssLa2poNGzawfPly4H9v20NDQ9m4cSOtW7emRYsWXL58mQkTJph1fMuUKUO2bNkYPnw4nTt35tatW0ycOJHcuXMb+/j4+PDf//6X0aNHExAQQGxsLDNnzuTy5ctm0wBy5MjBvn372LlzJ56enjRo0ID58+fTvHlz2rZti5ubG1u2bGHWrFl06NDhsetWNGjQgDVr1hAWFsbu3bupXLkyOXPm5PTp08ydOxd7e3uaNWsGPEgWWFlZsXPnTooUKYKvry+BgYF8//33jBw5ksDAQPbt28eqVavMzvE0bfNX409Pjhw5uHz5Mlu3bsXDwyPNVBgRERH5Z9AIBBF5IWrXrs2aNWsoV64cCxcupH379nTp0oWdO3fy6aef8u2339K0adMn1jFy5Ei8vb0ZMmQIoaGhbNq0icGDB1OhQgXjjaqdnR1z5syhVKlSDBs2jL59+/LOO+8QGBiYpq5ixYrRv39/+vTpg5eXl9EJgwdvfceNG8fvv/9Ou3btGDBgAADz5s3DysqKffv2AVCtWjW8vb3p3bs3M2fONOoOCgpi/vz5tGrVihkzZtCwYUMmT5782GvLnj078+fPp2LFiowdO5YOHToQExNDREQEH3300bM3OBAREUG7du34+uuvCQkJoWvXrsanML/44otnrq9UqVJMmzaNixcv0qFDB5YsWcLw4cPN9nFzc2PRokW4uroSHh5OSEgIJ06c4IsvvqBBgwZPrL9MmTKMHz+eo0eP0r59e1asWGF8ceNJ6z7kyJGDqKgokpKS6NSpE7169eLixYvMnz8fBwcH49koXLgw8+fPB6BLly5MnjyZXr16kStXLqOunDlzEhERQVJSEqGhoUyYMIHQ0FCKFStm7FO/fn1CQ0NZv349rVq1IiIiglKlSjFkyBCuXLnCqVOnAGjcuDG2tra0bt2aH3/8EQcHBxYsWICvry8jR46kTZs2bN++nfDwcDp27PjY67Ozs2PWrFl07tyZX375hbCwMIKDg4mKisLf359ly5YZiYvs2bPTvHlzNm7cSJs2bbh//z4ffvghrVu3Zu3atbRp04YDBw4YiZlUT9M2fzX+9NSvX5/8+fMTGhrKmjVrnulYEREReXlYpTxuZTAReeGOHj2a7gr7yffuYf2Y1dn/Li9DDPLvtmnTJvLnz2/2hYWtW7fSpk0bVq9enebLCyIiIiLy9B7X13gemsIg8hJ6GTruL0MM8u+2detWNm3aRI8ePShQoADnzp1j4sSJ+Pv7K3kgIiIi8hJSAkFERDJFnz59sLOzY+LEifz555/kyZOH6tWr06VLl8wOTURERETSoSkMIpnoRQwrEhEREREReRF9DS2iKCIiIiIiIiIWKYEgIiIiIiIiIhYpgSAiIiIiIiIiFimBICIiIiIiIiIWKYEgIiIiIiIiIhYpgSAiIiIiIiIiFimBICIiIiIiIiIWKYEg8hK6n5iU2SG8FDFYkpKSktkh/Gv9m9r233QtIiIiIpkpS2YHICJpZbG1YUj3dZkaw4BxdZ/r+GPHjhEdHc2ePXuIj48nd+7clCpVinbt2lG0aNFnqispKYnw8HC+/vprrKysmDJlCmfPnuX48eP07t37ueL8JzOZTHTu3JmQkJAMq/PGjRt8/vnnfPjhh5QuXTrD6s0sBw4cYPLkyUydOvWx+6xYsYI+ffqYldnZ2eHm5kZAQAAhISE4Ozu/6FDT9SLusYiIiMhfpQSCiGS43377jUaNGlGiRAnCw8NxdnYmNjaWuXPn8vHHHzN37lyKFy/+1PX9+OOPLF++nJCQEMqVK4eXlxd9+/alZMmSL/AqXk3Hjh1j5cqV1K9fP7NDyRDLli3jxIkTT7Xv5MmTcXZ2JiUlhYSEBH755RemTZvGtm3bWLRoUaYlEUREREReFkogiEiGmzNnDnny5GHq1KntswjWAAAgAElEQVTY2NgY5VWrVqV27dpERUU98Y3wo+Li4gBo0KABBQoUyPB4RQA8PT3Jly+f8btcuXJUqFCBjz/+mLFjxzJ8+PBMjE5EREQk82kNBBHJcFeuXCElJYXk5GSzcgcHB/r27Uvt2rXNyletWkX9+vUpXrw4lSpVYtSoUdy5cweA3r1707NnTwCqVatGUFAQVapU4ezZs6xcuRKTycScOXMwmUwcO3bMqHPhwoWYTCa++uoro2zHjh2YTCbOnTsHwIYNG2jUqBF+fn4UK1aM2rVrs3DhQmP/3bt3YzKZWLx4MQEBAZQsWZK9e/cCsHfvXpo0aYKvry9lypShf//+XL9+3WLbPOlaU6+3RYsWhIeH4+fnR7169Z56Dn+VKlWIjIxk5MiRlCtXDl9fX1q2bMmZM2eMfa5evUr37t0pX748Pj4+1KtXj1WrVhnX26RJEwA+++wzgoKCAAgKCqJXr16Ehobi6+tLu3btjLbZt2+fWQxBQUEEBwcbv1Pbr0ePHvj5+VG2bFkiIyO5efMmffr0oWTJkpQvX54xY8aYXee1a9fo378/77zzDj4+PjRq1Ij9+/ebnctkMrFo0SL69OlD6dKl8fPzo3Pnzly5csVoy2XLlnHhwgVMJhMrVqx4qnZ8mIeHBzVr1mTNmjUkJCQY5U+6/xcuXKBo0aIsWrTIrK6LFy9StGhRli9fDsCdO3cYNWoUlSpVwtvbmw8++IBNmzY9MZ7Y2FjCwsKoWLEivr6+NGnShD179hjbz58/bzz3rVq1wtfXl6pVqzJnzhyzepKTk4mOjqZatWoUK1aMWrVqsXTpUrN90rvvIiIi8mpTAkFEMlylSpU4f/48n376KQsWLODkyZPGtlq1apkNj584cSK9e/emdOnSREZG0rx5cxYtWkS7du1ISUkhJCSEjh07AhAZGcnAgQOJjIwkX758VK5cmcWLF9OgQQPs7OzYuXOnUe+uXbsAjA4/wLZt2yhSpAgFChRg06ZNdOrUCR8fH6KiooiIiOCNN95g8ODBHDp0yOx6Jk2aRN++fenfvz++vr7s3buX5s2b4+DgwIQJEwgLC2PLli20bNmS+/fvP7ZdLF1rqt27d/Pnn38SFRVFly5dsLKyeuq2nz17NqdOnWLEiBEMHTqUI0eOmM3v79mzJydPnmTw4MFMnToVT09PevXqxe7du/Hy8mLIkCEADBgwgIEDBxrHrVu3jty5cxMdHU2zZs2eOh6AUaNG4eTkRFRUFIGBgURERNCwYUPs7e2JjIykevXqTJ8+nW+//RaAu3fvEhwczJYtW+jWrRsTJ04kV65cBAcHp7k3Y8eOBeDLL7+kZ8+ebN68mZEjRwIQEhJClSpVyJs3r5EE+ivKlStHYmIihw8fBrB4//Pnz0+pUqVYv369WT3r168na9as1KxZk5SUFDp06MCSJUto2bIlkyZNwsPDg9DQUDZu3JhuHJcuXaJhw4YcPHiQsLAwxo8fT7Zs2WjevLnZsw8wcOBAXF1diYiIIDAwkOHDhzN37lxj+6BBg4iMjKR+/fpER0cTGBhIeHg48+bNM6vnee67iIiI/PtoCoOIZLgmTZrw559/MmvWLKND6uzsTIUKFQgKCsLHxwd4MDVh2rRpNG7cmL59+wJQoUIF3Nzc6Nq1K1u3biUgIMCYtuDh4cEbb7wBPFjkztnZ2VhLwd/fn127dhEcHExKSorRIX44gbB9+3ajE3ny5EkaNGhg1rn28/OjTJky7Nmzx4gRoGnTptSoUcP4PW7cOIoUKUJ0dDTW1g/ysJ6entSvX5+vvvqK999/P02bPO21Aty/f58hQ4bg6ur6zG2fO3duoqKijKkjZ8+eJSIighs3bpAjRw727NlDaGgo1apVM9otd+7c2Nra4ujoSJEiRQBwd3fH3d3dqDdr1qwMHDgQOzs74EGS42l5eXnRr18/AIoWLcqKFSvIkycPAwYMAKBs2bKsXbuWn3/+mZo1a7J69WqOHTvG0qVL8fb2Bh4kpRo2bMj48eOZNWuWUXfRokUZMWIEAOXLl+fw4cNGB7xgwYI4OztjZ2f3TGtuPCpPnjwAXL58GXi6+1+vXj0GDBjApUuXjPv41VdfUaVKFRwdHfnxxx/Zvn07EydOpGbNmsY1Xr9+nTFjxhj352GzZs3i+vXrLF26lNdeew2AgIAA6tWrx9ixY42RDQC+vr7GlItKlSpx6dIloqOjCQoK4vTp0yxZsoSwsDBatGgBPHgWk5KSmDBhgpHcgbT3XURERF5tGoEgIhnOysqKrl27sn37dsaNG0fDhg1xcHBgzZo1fPzxxyxYsACAgwcPcu/ePd59912z42vVqoWtre0zdVIrVarEnj17uH//Pr/99hvx8fG0atWKEydOcPXqVX7//XdOnDhBYGAgAG3atGHEiBHcunWLI0eO8NVXXzFlyhQAEhMTzep++KsRCQkJHDx4kICAAJKTk7l//z7379/n7bff5vXXX2fHjh3pxvcs1+ri4vKXkgfwoOP48LoTqXP6b9++DUCZMmWIiIigU6dOLF26lMuXL9OrVy9KlCjxxHrd3d3/cify4WSMk5MTNjY2ZmVWVlbkypXLmAKwc+dO3Nzc8PDwMNo3OTmZwMBA9u7dy71794xjH407X758ZlMNMtrT3v/U+/r1118DcPr0aX755RcjubRz505sbGyoVKmSUcf9+/epUqUKp0+f5vz582nOvW/fPkqWLGkkDwCsra2pU6cOv/zyCzdv3jTK69Y1/4pKjRo1uHLlCjExMezatYuUlBQCAwPTnPvGjRtmozye576LiIjIv49GIIjIC5MrVy7q1q1rdGZ+/fVXwsLCGDVqFHXr1iU+Ph6AvHnzmh1nbW2Ns7OzWYfIkoCAAIYPH87hw4f5+eefMZlMBAQEYGNjw759+7h27Rq5cuUyOpxXr15l4MCBbNy4ESsrKwoVKkSpUqUA0qw5kD17duPv69evG/PHo6Oj08Rx6dKldON7lmt9+HzPKlu2bGnqh/9d0/jx44mOjubrr79mw4YNWFtbU65cOYYMGUL+/PkfW+/zxOTg4PBM9cXFxREbG4uXl1e6269du4abmxuQ/vU+uvbG80q9p66urk99/3PkyEFgYCBfffUVzZo1Y926dTg7O1OxYkXgwTUmJSU9dmTEpUuXjNE2qeLj4ylcuHCafV1cXEhJSeHWrVtGWWr7pEodRXH9+nVjUdJatWo98Xrh+e67iIiI/PsogSAiGSo2NpaGDRvSuXNnPvroI7Ntnp6edOnShdDQUM6fP0+uXLkA+PPPPylYsKCxX3JyMlevXsXJyempz1uoUCEKFy7Mjh07OHz4MP7+/mTPnh1vb2/27NlDbGwsFStWNN7O9+jRg1OnTjF79mz8/Pyws7MjISGBJUuWPPE8Dg4OWFlZ0aJFizSLQaZuT09GXuvzyJEjBz179qRnz57ExMSwadMmoqKiGDp0aLod4sdJXZfh0c76rVu3yJkz53PHWKRIEUaNGpXu9r+rrVLt2rULe3t7vLy8SEpKeur7X69ePdq3b8/Fixf5+uuvqVOnDlmyPPjfbo4cOciRI4fZdIyHvfnmm2nKcubMaUyjeFhqh9/Jycn4+9q1a2b7pB6XJ08ecuTIAcD8+fPTJGCANIkLERERkVSawiAiGSpv3rzY2NiwcOFC7t69m2Z7TEwM9vb2FCxYEF9fX+zs7NIsNvfNN9+QmJhIyZIlH3ue1DfrD6tcuTI7duzgwIEDlClTBvjf2gi7du0yW0Rv//791KpVizJlyhhDtLdt2wak7RQ/zNHREU9PT06fPo23t7fx78033+TLL7/k4MGD6R73PNeaUWJjY6lcuTLffPMNAG+99RatW7emXLlyXLx4EcBs+sOTODo6AhjHwYM35A8vmPlXlS5dmt9//x1XV1ezNt60aRPz5s3D1tb2qet62ut5nGPHjrFhwwY++OAD7O3tn+n+V6xYEScnJ6ZPn86JEyeoV6+e2TXeuHGDLFmymNVz6NAhJk+enO7CmaVLl2b//v3ExsYaZcnJyXzzzTd4e3ubTTXYsmWL2bEbNmwgf/78FCxY0BhpEx8fb3buixcvMnHixBc6BURERET+2TQCQUQylI2NDQMGDKBjx458+OGHNGnShCJFipCQkMCPP/7IggUL6Natm/EWtGXLlkRHR5MlSxYqV67M8ePHiYiIwN/f3xjunZ6cOXPy66+/GgseZsuWjYCAAObMmYOVlZXRSSpTpgxTpkwx5pun8vHxYc2aNXh4eODm5sZPP/3E1KlTsbKystiB6ty5M+3ataN3797UqVOHe/fuMW3aNP7v//6PXr16pXtM7ty5//K1ZpR8+fKRP39+hg0bxs2bNylYsCBHjhxh69athISEABijB7Zs2UKuXLnM1n94mMlk4rXXXiMiIsJ46z5lyhRj8b3n0aBBA+bPn0/z5s1p27Ytbm5ubNmyhVmzZtGhQ4dn+ipFjhw5uHz5Mlu3bsXDw+OJa0v8+uuvRuf89u3bHDlyhJkzZ1KwYEG6du1q7Pe099/W1pZ3332X//73vxQuXNhs3YeAgABKlChBu3btCAkJoXDhwvz0009MmjSJunXrpjuSpXnz5qxevZpmzZrRsWNHHBwcWLhwISdPnmTq1Klm+65btw4XFxfKlSvH999/z3fffceYMWOAB2t61K1bl759+3Lu3Dk8PDw4fvw448ePx8vLi9dff/2p21dEREReLUogiLyE7icmMWBcXcs7vuAYstj+tbe3VatWZcmSJcyYMYPo6GiuXLlC1qxZ8fT05Msvv6R69erGvl26dMHFxYX58+ezcOFCXFxc+OSTT+jYsWO6owxSNW/enGHDhtGyZUvmzJlDiRIlKFWqFNmzZ6dgwYLkzp0beLDInq2tLb6+vsY0AoCRI0cydOhQ4ysRhQsXZvDgwaxZs4b9+/c/8foqV67M9OnTiYyMpGPHjmTNmhVvb2/mzp3Lf/7zn8ce91evNSNFREQwduxYJkyYwLVr13jttdfo2LEjrVu3Bh4Mna9bty4LFizghx9+YO3atenWY2Njw8SJExk+fDhdu3bFxcWFZs2aERMTw9mzZ58rRgcHBxYsWMC4ceMYOXIkt27dokCBAoSHh9O0adNnqqt+/fp8//33hIaG0qVLF1q1avXYfdu3b2/8bWdnR4ECBfj4449p3bq1kfCCZ7v/9erVY/78+bz33ntm5dbW1kybNo0JEyYQGRlp3It27drRtm3bdONzdXXlv//9L2PHjmXgwIEkJydTrFgxZs2aZYy4SdWlSxd++OEH5s+fT8GCBfniiy/MFvAcOXIk0dHRzJ8/nz/++AMXFxcaNmxIp06dLDeqiIiIvLKsUh5dLUxE/jZHjx7Fw8Mjs8MQkX+J8+fPU7VqVUaPHm02ZUJERERePS+ir6E1EERERERERETEIiUQRERERERERMQirYEgIiLyL/HGG29w7NixzA5DRERE/qU0AkFERERERERELFICQSSTaR1TERERERHJSC+qj6EEgkgmsrW1JSEhIbPDEBERERGRf5GEhARsbW0zvF4lEEQykaurKxcuXOD27dsaiSAiIiIiIs8lJSWF27dvc+HCBVxdXTO8fqsU9VpEMtX169e5dOkSiYmJmR2KiIiIiIj8w9na2uLq6krOnDkzvG4lEERERERERETEIk1hEBERERERERGLlEAQEREREREREYuUQBARERERERERi5RAEBERERERERGLlEAQEREREREREYuUQBDJRL/99ltmhyCvqISEhMwOQV5RevYks+jZk8yiZ08yy4t49pRAEBF5BekLvpJZ9OxJZtGzJ5lFz55klhfx7CmBICIiIiIiIiIWKYEgIiIiIiIiIhYpgSAiIiIiIiIiFimBICIiIiIiIiIWKYEgIiIiIiIiIhYpgSAiIiIiIiIiFimBICIiIiIiIiIWKYEgIiIiIiIiIhYpgSAiIiIiIiIiFimBICIiIiIiIiIWKYEgIiIiIiIiIhYpgSAiIiIiIiIiFimBICIiIiIiIiIWKYEgIiIiIiIiIhYpgSAiIiIiIiIiFimBICIiIiIiIiIWKYEgIiIiIiIiIhYpgSAiIiIiIiIiFimBICIiIiIiIiIWKYEgIiIiIiIiIhYpgSAiIiIiIiIiFimBICIiIiIiIiIWKYEgIiIiIiIiIhYpgSAiIiIiIiIiFimBICLyCsqePXtmhyCvKD17kln07Elm0bP36rqXmJTZIWS4LJkdgMirzMrKive6r87sMEREREREJIOtHVcvs0PIcBqBICIiIiIiIiIWKYEgIiIiIiIiIhYpgSAiIiIiIiIiFimBICIiIiIiIiIWKYEgIiIiIiIiIhYpgSAiIiIiIiIiFimBICIiIiIiIiIWKYEgIiIiIiIiIhYpgSAiIiIiIiIiFimBICIiIiIiIiIWKYEgIiIiIiIiIhYpgSAiIiIiIiIiFimBICIiIiIiIiIWKYEgIiIiIiIiIhZlyewARF5pKSmsHVcvs6MQEREREZEMkHz/HtZZ7AC4l5iEna1NJkeUsZRAEMlMVlbEfP5hZkchIiIiIiIZ4K1+y42//23JA9AUBhERERERERF5CkogiIiIiIiIiIhFSiCIiIiIiIiIiEVKIIiIiIiIiIiIRUogiIiIiIiIiIhFSiCIiIiIiIiIiEVKIIiIiIiIiIiIRUogiIiIiIiIiIhFSiCIiIiIiIiIiEVKIIiIiIiIiIiIRUogiIiIiIiIiIhFSiCIiIiIiIiIiEVKIIiIiIiIiIiIRVkyOwCRV1lKSgpv9Vue2WGIiIiIiMhL6F5SInY2tpkdhkEJBJFMZGVlxceL22d2GCIiIiIi8hJa8snkzA7BzD92CkNKSkpmh/CPoHZ6dmozERERERGRtDIkgRAUFERwcHBGVGXRjRs36N27N/v27ftbzpeqZcuWzJkzJ91tERERmEwms3++vr7Url2bCRMmcPv27QyPZ/fu3ZhMpie2w+bNm+nVq1eGn/vfbPny5YwaNSqzwxAREREREXnp/OOmMBw7doyVK1dSv379v+2cd+7cYe/evfTv3/+x+9jY2LBw4ULgwRvsmzdv8tNPPzFz5kx27NjBnDlzyJYt298VMgBz5swhKSnpbz3nP110dDQlS5bM7DBEREREREReOv+4BEJm2L17N3nz5uXNN9984n7Fixc3+12xYkWKFy9O27ZtmTFjBqGhoS8yTBEREREREZEX5m9bAyEpKYkpU6ZQt25dfHx8KF68OI0aNWL37t3GPhEREVSvXp2NGzdSo0YNihcvTtOmTTly5AjwoCPfpEkTAD777DOCgoKMY1etWkX9+vUpXrw4lSpVYtSoUdy5c8fY3rt3b4KDg1m0aBGVKlXCz8+PNm3acPbsWYuxb9u2jUqVKv2l665cuTJ+fn4sWbLErPy7776jQYMGeHt7U6FCBUaNGsW9e/eM7Xfu3GHQoEFUqlSJYsWKUatWLWbMmPHY89y5c4cmTZpQoUIFYmJiCAoKYufOnezZsweTyWS0c2xsLGFhYVSsWBFfX1+aNGnCnj17jHrOnz+PyWTiq6++olWrVvj6+lK1atXHTt94WExMDKGhoZQuXRp/f39CQkLM2jcuLo6hQ4dSpUoVvL29adCgAd9++61ZHSaTicWLF9OjRw/8/PwoW7YskZGR3Lx5kz59+lCyZEnKly/PmDFjjLUKUqdz/Pjjj3z66af4+PhQp04d1q1bZ9T7uCkfD0+/qVKlCmfPnmXlypWYTCbOnz8PwIULF+jSpQulS5emePHitGzZkhMnTqRps9mzZ1OzZk2KFy/O2rVrLbaXiIiIiIjIP8nflkAYPXo00dHRNGrUiOnTpzN06FCuXbtG586dSUhIMPa7fPky/fr1Izg4mHHjxnH79m0+++wzYmNj8fLyYsiQIQAMGDCAgQMHAjBx4kR69+5N6dKliYyMpHnz5ixatIh27dqZLYh35MgRJk2aRPfu3fn88885efIkn332mcU1Cp4ngQBQrlw5YmNjuXDhAgBr166lQ4cOvP3220yaNIl27dqxePFiunfvbhwzfPhwtm3bRu/evZkxYwZVq1Zl9OjRrFy5Mk399+7do0OHDsTExDB79mzeeustBg4ciLe3N56enixevBgvLy8uXbpEw4YNOXjwIGFhYYwfP55s2bLRvHlzdu7caVbnwIEDcXV1JSIigsDAQIYPH87cuXMfe41//PEHn3zyCefOnWPIkCGMHDmS8+fPExwczO3bt0lISKBx48Zs2LCB9u3bExkZyVtvvUXHjh1ZtWqVWV2jRo3CycmJqKgoAgMDiYiIoGHDhtjb2xMZGUn16tWZPn16muRD165dKVGiBJGRkXh4eNC9e3c2btz41PcpMjKSfPnyUblyZRYvXoyrqytXr16lUaNG/PbbbwwaNIixY8dy69YtGjdubNzPh49v164dI0aM4J133nnq84qIiIiIiPwT/G1TGC5dukS3bt2MEQQAWbNmpWPHjhw/fhwfHx8Abt++zeeff06dOnWAB9MCUt+A9+rViyJFigDg7u6Ou7s7cXFxTJs2jcaNG9O3b18AKlSogJubG127dmXr1q0EBAQADxZgnDFjBr6+vgAUKVKEevXqsXLlSrO4Hnb69GliY2MpU6bMX772PHnyAA+SI6+//jpjx44lMDDQbLG+fPnyERoayv79+ylZsiR79uyhfPnyRjuUKVOG7Nmz4+TkZFZ3cnIyPXr04PDhw8ydOxd3d3ejfRwdHUlKSjKmVkyaNInr16+zdOlSXnvtNQACAgKoV68eY8eOZfny5Ua9vr6+DB8+HIBKlSpx6dIloqOjCQoKwsrKKs01zp49m/v37zNr1izjet98801atGjBr7/+yrFjxzh58iRLly417nXlypWJj49nzJgxvPfee9jY2ADg5eVFv379AChatCgrVqwgT548DBgwAICyZcuydu1afv75Z2rWrGnEULt2bcLCwoyYT58+zeTJk6lWrdpT3SdPT0/s7OxwdnY22mzOnDnEx8ezZMkS8uXLBzx4vqpXr87kyZMZNmyYcXydOnX+1rU5RERERERE/k5/2wiE8ePHExQUxNWrV9m3bx/Lly9nzZo1ACQmJhr72dramnUK8+TJQ8mSJdm/f3+69R48eJB79+7x7rvvmpXXqlULW1tbsykSBQoUMJIH8GC4fKFChZ74JYNt27ZRqlQpsmfP/mwX/BgxMTHExsZSpUoV7t+/b/yrWLEitra27NixA3iQMFiyZAmtW7dm/vz5nDt3jtDQUCMZkmrkyJFs2LCBFi1aYDKZnnjuffv2UbJkSSN5AGBtbU2dOnX45ZdfuHnzplFet25ds2Nr1KjBlStXiImJSbfu/fv3U6JECSN5AA8SCJs3b6ZUqVLs3buXQoUKGcmDVO+99x6XL182q/fhfZycnLCxsTErs7KyIleuXFy/fj1NXY/G/Msvv5iNcHlWO3fuxMvLCxcXF+NeZcmShfLlyxv3KpWHh8dfPo+IiIiIiMjL7m8bgXD48GEGDx7M4cOHsbe3x93dnddffx3AbJpBnjx5jDfRqZydnY356I+Kj48HIG/evGbl1tbWODs7m3WKXV1d0xyfJ0+eNB3Rhz3v9AV4MLwfwM3NzRj2Hh4eTnh4eJp9L126BEC/fv3Ily8fa9asYejQoQwdOhQ/Pz8GDRpE0aJFjf3PnDlDqVKlmDlzJg0bNjTrwD8qPj6ewoULpyl3cXEhJSWFW7duGWVubm5m+6TW+7i2iouLo1ChQk88t4uLS7rnhgejQ1I5ODik2e9pEjiP3l9nZ2dSUlLM6n5WcXFxnDlzBi8vrzTbbG1tnzlGERERERGRf6q/JYFw8+ZNWrVqhYeHB+vXr+ett97C2tqarVu3smHDBrN9UxMCD7ty5cpjO8a5cuUC4M8//6RgwYJGeXJyMlevXjUb8h8XF5du3Y++FU91584d9uzZQ+/evS1f5BPs3r2b/Pnzky9fPqMDnrog4KNS47Wzs6N9+/a0b9+e33//nc2bNxMVFUXPnj3NFuj7/PPP8fPzo1atWgwfPpxx48Y9No6cOXNy+fLlNOWpSQsnJyfj72vXrpntk3rc4+6Do6MjV69eTVP+ww8/UKRIEXLmzMnRo0efeO7nFRcXZ/YMXLlyBRsbG3Lnzm1Mu0hOTjY75tatW+TMmfOxdTo6OlK2bFl69Ojx3PGJiIiIiIj8k/0tUxhiYmKIi4sjODgYd3d3rK0fnHbbtm2A+QiEhIQEswX9Ll++zP79+ylbtixAmtEJvr6+2NnZsX79erPyb775hsTERLNO+unTpzl9+rTx+7fffuPMmTNG3Y/avXs3efLkMdYV+Cu2b9/OgQMHaNSoEfBg3QVnZ2cuXLiAt7e38c/JyYmxY8dy8uRJ7t69S82aNZk5cyYAr7/+Ok2aNOHdd9/l4sWLZvW7uLjg5uZG586dWbduHVu3bjW2PdpWpUuXZv/+/cTGxhplycnJfPPNN3h7e2NnZ2eUb9myxezYDRs2kD9/frMO+sNKlizJgQMHzJI0Fy5coFWrVuzevRt/f3/OnDnDoUOHzI5bv349efPmfeLohae1efNms9/ffvstJUqUwM7ODkdHRwCz9ouPj+fkyZNmx6Q+m6n8/f05deoURYoUMbtfS5YsSfPMiYiIiIiI/Jtl2AiEixcvMnv27DTlnp6eeHh44OjoSFRUFFZWVlhbW7NhwwZj0b5Hv4LQq1cvunXrhqOjI5GRkTg6OhqfbEx9W7xlyxZy5cpF0aJFadmyJdHR0WTJkoXKlStz/PhxIiIi8Pf3p2LFika9ycnJtG/fni5dunD//n3GjRuHu7t7mrnzqbZv3252vCU///wzgDFs/qeffmL27NmULl2aZs2aAQ869V26dGHw4MFYW1tTqVIl4uLiiIiI4MaNG3h6epI1a1Z8fHyIjIzE1tYWkz+QedsAACAASURBVMnEqVOnWLlypdn6EA8LCgpi5cqVDB48mHXr1pE9e3Zy5MjBvn372LlzJ56enjRv3pzVq1fTrFkzOnbsiIODAwsXLuTkyZNMnTrVrL5169bh4uJCuXLl+P777/nuu+8YM2bMY689te6WLVvSrl07rKysjC8t1KhRA4B58+YREhJC586dcXNzY926dWzbto1hw4al6bj/FTNmzCBbtmx4enqyfPlyfvvtN+OZNJlMvPbaa0RERBhTJKZMmYK9vb1ZHTlz5uTXX39lz549+Pj40Lx5c1atWkWLFi0IDg4mZ86crFq1itWrVzNixIjnjllEREREROSfIsMSCKdPn063Q/XZZ5/h7+9PVFQUo0ePplOnTjg4OODh4cH8+fNp3fr/2bvzeC/n/H/8j9NyorKkMNMYxnoUnWRUQ7RojCQZy9ij7MukDJNIyJqINhWyhzB2ZsZ8ZsYyDEkzE/OZwaCxfAhjyVK0nd8f/Xp/nSmuEG/V/X67ndutcy2v63ld55q5uR7X6/W6jsjUqVPTqVOnJAsfsE855ZQMGzYs7733Xtq3b59Ro0aVurhvuOGG6dGjR2644YY88sgjueeee9K/f/80a9YsEydOzI033phmzZpl3333Td++fWs9mH7/+9/PAQcckCFDhmTOnDnp3LlzTj311Fpv3j/t4YcfLs3qX2T+/PnZd999S7+vuuqq+cEPfpBjjz02vXv3rnWMfffdN40bN86ECRNy4403pnHjxmnbtm1+8YtflOZyGDJkSJo0aZKrrroqb731Vpo2bZq99947/fv3X+Lx69atmzPPPDP7779/Ro4cmVNOOSUHHHBApk2bliOOOCLDhg1L9+7dc9NNN+Wiiy7KGWeckQULFmTLLbfM1VdfvdhXJvr3759HHnkkEydOzPrrr5+LL754sYkqP6158+a54YYbcuGFF2bAgAFp0KBBtttuuwwYMKA0N8DEiRMzfPjwXHTRRZk9e3Y222yzjB49uhQwfFWnnHJKbr311lx66aXZbLPNMmHChLRr1650fUaNGpXzzjsvJ5xwQpo1a5ZDDjkkL774Yl5++eVSG3369Mk555yTww47LNdee2223nrrTJo0KRdffHEGDx6cuXPnZqONNiq8HgAAACuaippPjx8os9GjR2fcuHH5xz/+sczbHjhwYKZOnZr/+Z//WeZtr0heffXVdO3aNcOGDcvuu+9e7nKWyuTJk3PwwQfnhhtuyDbbbFPucr6QZ599NoP/NqLcZQAAAN9Ct+w77kvvO2vWrGU+0fs39hUGYAlqar7S/ykAAABfnwVz5qTOZ/RY/ybMmT83lXXrF2/4DREgQDlVVOTR3fcqdxUAAMASdLjrtrIe/9sUHiTfsiEMsLJ59tln858Bp5a7DAAAYAnKHSB8FV/HEIZv5DOOAAAAwPJNgAAAAAAUEiAAAAAAhQQIAAAAQCEBAgAAAFBIgAAAAAAUEiAAAAAAhQQIAAAAQCEBAgAAAFBIgAAAAAAUEiAAAAAAhQQIAAAAQCEBAgAAAFCoXrkLgJVZTU1NOtx1W7nLAAAAVjD161Uu8zYFCFBGFRUVOevEe8tdBgAAsII5fXiPZd6mIQwAAABAIQECAAAAUEiAAAAAABQSIAAAAACFBAgAAABAIQECAAAAUEiAAAAAABQSIAAAAACFBAgAAABAIQECAAAAUEiAAAAAABQSIAAAAACFBAgAAABAoYqampqachcBK6tnnnkmm2++ebnLAAAAVjBz58xL/cp6y7RNPRAAVkKzZs0qdwmspNx7lIt7j3Jx71Euc+fNWeZtChAAAACAQgIEAAAAoJAAAQAAACgkQAAAAAAKCRAAAACAQgIEAAAAoJAAAQAAACgkQAAAAAAKCRAAAACAQgIEAAAAoJAAAQAAACgkQAAAAAAKCRAAAACAQgIEAAAAoJAAAQAAACgkQAAAAAAKCRAAAACAQgIEAAAAoJAAAQAAACgkQAAAAAAKCRAAAACAQgIEAAAAoJAAAQAAACgkQAAAAAAKCRAAVkINGzYsdwmspNx7X685c+eXuwQAVmD1yl0ArMwqKiqy24l3lbsMAFYQ9wzfvdwlALAC0wMBAAAAKCRAAAAAAAoJEAAAAIBCAgQAAACgkAABAAAAKCRAAAAAAAoJEAAAAIBCAgQAAACgkAABAAAAKCRAAAAAAAoJEAAAAIBCAgQAAACgkAABAAAAKCRAAAAAAArVK3cBsFKrqck9w3cvdxUALMGCeXNSp15lucv4QubMnZ/K+nXLXQYAKygBApRTRUVePHevclcBwBJsNOi2cpfwhQkPAPg6GcIAAAAAFBIgAAAAAIUECAAAAEAhAQIAAABQSIAAAAAAFBIgAAAAAIUECAAAAEAhAQIAAABQSIAAAAAAFBIgAAAAAIUECAAAAEAhAQIAAABQSIAAAAAAFBIgAAAAAIXqlbsAWJnV1NRko0G3lbsMAFjuzJk/N5V165e7DICVigAB/ktNTU0qKiq+kWNVVFRkn5uP+UaOBQArklv2HVfuEgBWOoYwrMB69eqV3r17l7uMJMnkyZNTVVWVJ598snDbadOm5cQTT0ynTp1SXV2dnXbaKWeddVbeeOONWttVVVVl7Nixy7TOBx54ICeffPIybRMAAGBFIEDgW+Xaa6/N/vvvn5kzZ+aXv/xlrrjiivTp0ycPPvhg9tprr/z73//+2o//+uuvf63HAAAAWB4JEPjWmDp1aoYOHZpDDjkkEyZMSI8ePdK+ffsccMABuemmmzJv3ryceeaZ5S4TAABgpSRAIHfeeWf22GOPbLXVVunYsWMuuOCCfPzxx7W2eeihh7Lffvtlq622yg477JBzzjknH330UWn95MmTc+ihh6Zt27bZcsst07Vr14wZMyYLFixY6jquvPLKrLnmmunfv/9i69Zdd90MHDgw2267bebNm7fY+ttvvz1VVVWZMWNGreU77rhjBg0aVPr93nvvTc+ePVNdXZ1tt902J510UmloRK9evfLYY4/liSeeSFVVVSZPnpwkeffdd3Paaadl2223TXV1dfbff/9MnTq11nGqqqpy6aWXZs8990x1dXUuv/zypT5vAACA5YEAYSU3atSoDBw4MG3bts2YMWPSp0+fTJo0KUcffXRqamqSLJwX4Kijjso666yTkSNHpn///rn77rtz6qmnJkn+93//N4ceemiaNm2aESNGZNy4cfnhD3+Y0aNH57e//e1S1VFTU5NHHnkk2267bRo0aLDEbX7605/mqKOOSr16X27uz6lTp2bAgAH5yU9+kgkTJmTgwIF5/PHHc9JJJyVJzjjjjLRq1SotW7bMzTffnC222CKffPJJevfunQcffDC/+MUvMmrUqKyxxhrp3bt3nnrqqVrtjx8/Pj179syIESPStWvXL1UjAADAt5WvMKzE3nvvvVxxxRU54IADSmHA9ttvn3XXXTcnnHBCHnrooXTu3DmjR4/OlltumVGjRpX2rampydVXX50PP/wwzz33XLbffvsMGzas9PWCDh065I9//GOmTJmS7t27F9by7rvv5pNPPknz5s2/npPNwgBhlVVWyZFHHpnKysokyZprrpmnn346NTU12WSTTdK4cePMnz8/W221VZLklltuybPPPptbb701rVq1SpJ07Ngxe++9dy655JJcffXVpfbbtWv3rZm0EgAAYFnTA2ElNm3atMyZMye77rprreXdunVL/fr1M3ny5Hz88cf5xz/+kZ122qnWNnvvvXfuu+++NG7cOHvssUcuu+yyzJkzJ88880x+97vfZdSoUZk/f37mzp27VLXUrVs3STJ//vxlc3JL0LZt28yePTs9evTI8OHD8+STT2b77bfPz3/+88/8bONjjz2WddddNy1atMi8efMyb968LFiwIF26dMmUKVMyZ86c0rYtWrT42moHAAAoNz0QVmIzZ85Mkqy99tq1ltepUydrrbVWPvzww8ycOTM1NTVZa621PrOdjz/+OGeffXbuuuuuzJs3L+utt17atGmTevXqlYZBFFljjTXSqFGjvPbaa5+5zYcffpgkady48VK1+d/atGmTyy+/PNdcc02uvvrqXH755WnWrFmOPvro9OrVa4n7vPfee5kxY0a22GKLJa5/9913s+666yZJGjZs+KXqAgAAWB4IEFZia6yxRpLkrbfeyvrrr19avmDBgrzzzjtp0qRJ6WH93XffrbXvhx9+mL/+9a/ZaqutMmzYsPzud7/LyJEjs+2225YepLfddtsvVM/222+fyZMn55NPPlniPAjXXHNNxo4dm9/85jfZYIMNaq1b1IPgv3swfHqixyTZYYcdssMOO2T27Nl5/PHHc9111+Wcc85JmzZtsuWWWy52zNVWWy0bb7xxLrjggiXW3KRJky90jgAAAMsrQxhWYq1bt05lZWXuu+++Wst/+9vfZu7cufnhD3+YRo0aZfPNN88f//jHWtv8/ve/z+GHH54PPvggU6dOzbbbbpuuXbuWwoO///3veeedd77QVxj69OmT9957LyNHjlxs3WuvvZYbbrgh1dXVi4UHyf/rlfD666+Xlr3wwgt57733Sr9feOGF2XvvvVNTU5NVV101Xbp0ycknn5wkpa83LBpKsUjbtm3z2muvZZ111kmrVq1KP3/4wx9y/fXXp379+kt9fgAAAMszPRBWcK+//nquueaaxZa3bNky7dq1y2GHHZbx48enXr166dSpU/71r39l9OjRadeuXXbYYYckyfHHH5/jjjsuJ510UnbffffMmDEjw4cPz09/+tM0b9481dXV+e1vf5ubb745G264YZ555pmMGzcuFRUVmT179lLX2qZNmxx33HEZM2ZMXnzxxey+++5Zc80188wzz+TKK69MnTp1cuGFFy5x3/bt22eVVVbJeeedl379+uWjjz7KqFGjsuaaa5a22W677XLllVdm4MCB6dmzZ+bOnZsJEyakSZMmadeuXZKFPQ6efPLJPPbYY2nZsmX23HPPTJw4MX369MlRRx2VddddNw8++GCuvvrqz507AQAAYEUjQFjB/fvf/87555+/2PKDDz447dq1S//+/dOsWbNMnDgxN954Y5o1a5Z99903ffv2TZ06CzuodO3aNWPHjs2YMWNy7LHHpmnTptlnn31y3HHHJUkGDhyYuXPn5uKLL86cOXOy3nrr5Zhjjsnzzz+fhx566Av1Qujbt2+22GKL3HDDDTn33HPz/vvv57vf/W66deuWI488Muuss84S91t99dUzevToDB8+PMcdd1y+973v5ec//3nuvPPO0jYdOnTIxRdfnAkTJpQe/rfZZptcd911WX311ZMkBxxwQKZNm5Yjjjgiw4YNS/fu3XPDDTdk+PDhGTp0aD766KN8//vfz+DBg3PQQQct9XkBAAAs7ypqlnaWO2CZe/bZZzP4byPKXQYALHdu2XfcF9p+1qxZJjymLNx7lMvXce/pgQDlVFPzhf8DCAD44j7rP6IXzJmTOpWV33A1AMsnAQKUU0VFHt19r3JXAQArrQ533VbuEgCWG77CAAAAABQSIAAAAACFBAgAAABAIQECAAAAUEiAAAAAABQSIAAAAACFBAgAAABAIQECAAAAUEiAAAAAABQSIAAAAACFBAgAAABAIQECAAAAUEiAAAAAABQSIAAAAACF6pW7AFiZ1dTUpMNdt5W7DACWE/Pmzk+9+nXLXcYKZcGcOalTWVnuMgCWCwIEKKOKioqcdeK95S4DgOXE6cN7lLuE5dasWbPSsGHDxZYLDwCWniEMAAAAQCEBAgAAAFBIgAAAAAAUEiAAAAAAhQQIAAAAQCEBAgAAAFBIgAAAAAAUEiAAAAAAhQQIAAAAQCEBAgAAAFBIgAAAAAAUEiAAAAAAhQQIAAAAQCEBAgAAAFCoXrkLgJVZTU1NTh/eo9xlALCcmDd3furVr1vuMgBYSemBALASmjVrVrlLYCXl3vtqhAcAlJMAAQAAACgkQAAAAAAKCRAAAACAQgIEAAAAoJAAAQAAACgkQAAAAAAKCRAAAACAQgIEAAAAoJAAAQAAACgkQAAAAAAKCRAAAACAQgIEAAAAoJAAAQAAACgkQAAAAAAKCRAAAACAQgIEAAAAoJAAAQAAACgkQAAAAAAKCRAAAACAQgIEAAAAoJAAAQAAACgkQAAAAAAKCRAAAACAQgIEAAAAoJAAAWAl1LBhw3KXwEpkztz55S4BAFgG6pW7AFiZVVRUZLcT7yp3GQBfq3uG717uEgCAZUAPBAAAAKCQAAEAAAAoJEAAAAAACgkQAAAAgEICBAAAAKCQAAEAAAAoJEAAAAAACgkQAAAAgEICBAAAAKCQAAEAAAAoJEAAAAAACgkQAAAAgEICBAAAAKBQvXIXACu1mprcM3z3clcB8I1p2LDhMmtrwbw5qVOvcpm1BwB8PgEClFNFRV48d69yVwGwXNpo0G3lLgEAViqGMAAAAACFBAgAAABAIQECAAAAUEiAAAAAABQSIAAAAACFBAgAAABAIQECAAAAUEiAAAAAABQSIAAAAACFBAgAAABAIQECAAAAUEiAAAAAABQSIAAAAACFBAgAAABAoXrlLgBWZjU1Ndlo0G3lLgNYjsyZPzeVdeuXu4xvhQXz5qROvcpylwEAKw0BApRRRUVF9rn5mHKXASxHbtl3XLlL+EpmzZqVhg0bLpO2hAcA8M0yhIHlWk1NzTe6HwAAwMpKgLCUnn322Zxwwgnp0KFDttxyy2y//fbp379/nnnmmVrb9erVK7179y5PkQV23HHHDBo0qNxlJPnqtcyZMydDhw7NPffc84X2++CDDzJw4MA8+eSTpWXf5r8ZAADAt4UAYSk888wz2W+//fL+++9n8ODBueqqqzJgwIC8+uqr2WefffK3v/2t3CWudN55551cffXVmTdv3hfa79lnn80dd9yRBQsWlJadccYZGTx48LIuEQAAYIViDoSlcO2116Zp06a5/PLLU7du3dLyrl27ZpdddsnYsWNz+eWXl7FCvopNNtmk3CUAAAB86+mBsBTefvvt1NTU1HprnSSNGjXKqaeeml122aXW8pqamlx22WXp3Llzqqurs99+++Xvf/97rW3uv//+7L///mnTpk223HLL7LLLLrnxxhtL6ydPnpyqqqrcfPPN6dy5c374wx9mypQpSZIpU6bkwAMPTOvWrdO+ffucdtppef/992u1/8wzz6RPnz5p06ZNunTpkrvvvvtzz/Haa69Ny5Yta7Vz4YUXpqqqKtOmTSstu/XWW1NdXZ3Zs2cnSR566KHst99+adOmTbbddtucdtppeffdd0vbjx49Ot26dcuoUaPStm3bdOzYMR999NFix584cWKqqqpy7bXXJkk+/vjjnHnmmenYsWO23HLLdOvWLVdeeWWS5NVXX02nTp2SJKecckp23HHHUjuTJk3Knnvuma222irV1dXZY489cv/995eu6YEHHpgkOfjgg9OrV68kiw9h+PjjjzNy5MjsvPPOadWqVbp3756bb765Vr077rhjxowZk6FDh2a77bZL69atc9hhh+Wll1763OsMAACwvBIgLIWOHTvm1VdfzX777ZcbbrghL7zwQmldt27dsscee9Ta/oknnsgf//jHDB48OMOGDcsbb7yRY445JvPnz0+S/OEPf8jxxx+f6urqjB07NqNHj856662XIUOG5KmnnqrV1qWXXppTTz01p512Wlq3bp0pU6akT58+adSoUUaOHJkBAwbkwQcfzGGHHVbqzv/GG2/koIMOygcffJALL7ww/fr1y0UXXZQ33njjM8+xc+fOmT9/fp544onSssceeyxJSsFFkjz88MNp3759Vl111dx222058sgjs/7662fkyJE54YQT8sADD+Tggw8uBQxJ8sorr+TRRx/NiBEjcsopp6RRo0a1jn3nnXfmnHPOyYABA3LIIYckSc4777w8/PDDGThwYK688sp07do1w4YNyx133JF11lkn48YtnIX8mGOOyZgxY5Ik1113Xc4666z85Cc/yWWXXZaLLroo9erVy4knnpg33ngjW2yxRc4666wkyemnn54zzjhjsetQU1OTI444Itdee23233//jBs3Ltttt13OOOOMXHrppbW2veaaazJ9+vScf/75Ofvss/P3v/89p5xyymdeYwAAgOWZIQxL4cADD8xbb72Vq6++uvQAutZaa2X77bdPr169Ul1dXWv7VVZZJVdccUVWX331JAsn7jvttNMyffr0bLLJJnnhhRey55571nrYbNOmTdq3b58nnniiVnsHHXRQfvKTn5R+Hz58eDbeeOOMHz8+deoszH9atmyZPfbYI7/+9a/Ts2fPXHPNNZk/f36uuOKKNGnSJEmy4YYbZp999vnMc9xggw3ygx/8II899lh+/OMf5/33388///nPbLHFFpkyZUoOP/zwzJs3L4899lhOOOGELFiwIBdffHE6d+6cYcOGldqpqqrKPvvsk9tvv730tn/evHk55ZRTstVWWy123N/97nc59dRT079//xx22GGl5U888UQ6dOiQ7t27J0nat2+fhg0bpkmTJqmsrEzLli2TJOuvv37p36+++moOP/zwHH300aV2vve972XPPffMX/7yl+yyyy7ZeOONkywctrCkoQsPPfRQnnjiiYwcOTLdunVLkmy//faZN29exo8fnwMOOKB0Tddcc82MHTu2NKzl5ZdfzujRo/PBBx9ktdVW+8xrDQAAsDwSICyFioqKnHDCCTn00EPzpz/9KY899lgmT56cu+++O/fcc08GDx5celhOks0226wUHiTJeuutlySl4QFHHnlkkuSjjz7K9OnT8/LLL+fpp59OksydO7fWsTfffPPSv2fPnp1p06blyCOPzIIFC0pDKjbddNM0b948f/7zn9OzZ89MnTo1W2+9delBN0lat26d5s2bf+55durUKY888kiShQ/wq622WvbZZ59cdNFFWbBgQf7617/mgw8+SJcuXTJ9+vT85z//ya677lqrjdatW2eDDTaoNVwgSVq0aLHY8Z566qncddddWX/99Ws99CcLA4NJkyZlxowZ6dSpUzp16pTjjjvuc+s/9dRTkyy8zi+++GJeeumlTJ48Ocni1/WzTJkyJfXr168V2iTJbrvtlptuuinTpk1L586dS+f66TkxvvOd7yRZ+I1zAQIAALCiMYThC1hjjTXSo0ePnHvuufn973+fO+64I5tsskkuuOCCzJw5s7TdqquuWmu/RT0FFj3wv/POO+nbt2+22Wab7LPPPhk9enQ+/PDDJAu70H9aw4YNS/9+//33s2DBgowfPz5bbLFFrZ//+7//y5tvvpkkmTlzZtZaa63F6l977bU/9/w6d+6cF154IW+++WYef/zxbLPNNmnXrl0++OCD/POf/8yf/vSnbLbZZmnevHnee++9z2yzadOmpfNJkrp166ZBgwaLbffcc8+lQ4cOmT59em699dZa6wYNGpT+/fvn1Vdfzdlnn50f//jH2W+//Rb7bOanvfzyy+ndu3fatm2bgw46KFdeeWVpWMd/X9fPMnPmzDRt2rT0N1ukWbNmSRb2JllklVVWqbXNon2W9lgAAADLEz0QCsyYMSN77713+vXrl5/97Ge11rVs2TL9+/fPcccdl1dffTVrrLHGUrV50kknZfr06bnmmmvSpk2bVFZWZvbs2bnllls+d79GjRqloqIihx566GITNy5anyRNmjTJ22+/vdj6RQ/9n2WbbbZJw4YN8+c//zmTJ0/OXnvtlY022ihrr712pkyZkj/96U/p0qVLkpTO9a233lqsnbfeeiutW7f+3GMlCwOLyy67LD//+c9z4YUXpkuXLqUH9crKyhxzzDE55phj8tprr+WBBx7I2LFj88tf/jL33HPPYm0tWLAgRx55ZBo0aJBf/epXadGiRerVq5fnn38+d911V2Eti6y++up5++23s2DBglohwqLz/HSvDgAAgJWJHggF1l577dStWzc33nhjPvnkk8XWv/jii1l11VWz/vrrL3WbU6dOTbdu3dK+fftUVlYmWTg5YZLFvvTwaY0bN07Lli3z73//O61atSr9bLjhhhkxYkTpawk/+tGPMnXq1FoP988//3xeeeWVz62rsrIy2223Xe6///48//zzad++fZKFwwl++9vf5p///Gep+/5GG22UZs2a5b777qvVxlNPPZVXXnklW2+9deF1WBQWDBo0KHPnzs0555yTJPnkk0+y884756qrrkqSNG/ePAceeGB23XXXvP7660myWA+Bd999N9OnT88+++yTVq1apV69hdnYouu6qFfAp4ccLEm7du0yd+7c/O53v6u1/N577039+vUXm+8CAABgZaEHQoG6devm9NNPT9++fbPXXnvlwAMPzMYbb5zZs2fn0UcfzQ033JBf/OIXX2jMe3V1de6+++60aNEi6667bv7yl7/k8ssvT0VFRa2vFyxJv379cvTRR2fgwIHp3r175syZkyuuuCLPPfdcTj755CTJIYcckl/96lc59NBD07dv38ybNy+XXHJJ6tevX1hb586dc9ppp2WNNdZIVVVVkoUBwuDBg9OkSZPSRIh16tRJ//79c9ppp2XAgAHp0aNH3njjjYwcOTIbbrjhYl+m+Dzf/e538/Of/zzDhg3LT3/609LnL8eMGZP69eunqqoq06dPzx133JGdd945ycIwpaKiIo899lg23njjtG7dOt/73vdy3XXXZZ111knjxo3zpz/9Kdddd12ShfMSJCnNTfHggw9mjTXWqDXHRLLwixtt27bNoEGDMmPGjGy66aZ56KGHMmnSpBx99NG15rYAAABYmQgQlkLXrl1zyy235Morr8z48ePz9ttvp0GDBmnZsmVGjBiRnXba6Qu1N3To0Jx99tmlLzr84Ac/yJAhQ3L33Xdn6tSpn7tvp06dMmHChIwZMyZ9+/ZNgwYNClp7DgAAIABJREFU0qpVq1x33XXZbLPNkizsZn/TTTfl3HPPzcknn5xGjRrl8MMPz69//evC2jp27Jhk4XCGRW/5F/VE6NixY603/z/72c/SsGHDXHHFFTn22GOzxhprpGvXrjnhhBNqzd2wNA455JDceeedGTJkSNq2bZshQ4akSZMmueqqq/LWW2+ladOm2XvvvdO/f/8kC+eG6NOnTyZNmpSHH344jz76aMaOHZtzzz03AwYMSGVlZTbZZJOMGzcu5513XqZOnZoDDjggG264YXr06JEbbrghjzzyyGLDIerUqZPLLrssI0aMyIQJEzJz5szS32fffff9QucEAACwIqmoMeMblM2zzz6bwX8bUe4ygOXILfuOK3cJX8msWbO+cMgMy4J7j3Jx71EuX8e9pwcClFNNzXL/MAB8OQvmzEmd/38enC9izvy5qaxbPCQNAGBZEyBAOVVU5NHd9yp3FUAZdLjrti+1n/AAACgXX2EAAAAACgkQAAAAgEICBAAAAKCQAAEAAAAoJEAAAAAACgkQAAAAgEICBAAAAKCQAAEAAAAoJEAAAAAACgkQAAAAgEICBAAAAKCQAAEAAAAoJEAAAAAACtUrdwGwMqupqUmHu24rdxkA35iGDRuWuwSWoXlz56de/brlLgOAb4gAAcqooqIiZ514b7nLAIAv5fThPcpdAgDfIEMYAAAAgEICBAAAAKCQAAEAAAAoJEAAAAAACgkQAAAAgEICBAAAAKCQAAEAAAAoJEAAAAAACgkQAAAAgEICBAAAAKCQAAEAAAAoJEAAAAAACgkQAAAAgEICBAAAAKBQvXIXACuzmpqanD68R7nLAIAvZd7c+alXv265ywDgG6IHAsBKaNasWeUugZWUe2/FIjwAWLkIEAAAAIBCAgQAAACgkAABAAAAKCRAAAAAAAoJEAAAAIBCAgQAAACgkAABAAAAKCRAAAAAAAoJEAAAAIBCAgQAAACgkAABAAAAKCRAAAAAAAoJEAAAAIBCAgQAAACgkAABAAAAKCRAAAAAAAoJEAAAAIBCAgQAAACgkAABAAAAKCRAAAAAAAoJEAAAAIBCAgQAAACgkAABAAAAKCRAAAAAAAoJEAC+hDlz55e7BAAA+EbVK3cBsDKrqKjIbifeVe4y+BLuGb57uUsAAIBvlB4IAAAAQCEBAgAAAFBIgAAAAAAUEiAAAAAAhQQIAAAAQCEBAgAAAFBIgAAAAAAUEiAAAAAAhQQIAAAAQCEBAgAAAFBIgAAAAAAUEiAAAAAAhQQIAAAAQCEBAgAAAFCoXrkLgJVaTU3uGb57uatgBbRg3pzUqVdZ7jIAAFiBCBCgnCoq8uK5e5W7ClZAGw26rdwlAACwgjGEAQAAACgkQAAAAAAKCRAAAACAQgIEAAAAoJAAAQAAACgkQAAAAAAKCRAAAACAQgIEAAAAoJAAAQAAACgkQAAAAAAKCRAAAACAQgIEAAAAoJAAAQAAAChUr9wFwMqspqYmGw26rdxlrPDmzJ+byrr1y13GN2rBvDmpU6+y3GUAALACESBAGVVUVGSfm48pdxkrvFv2HVfuEr5xwgMAAJY1QxhWcDU1NeUuYbnjmgEAACyuMEDo1atXqqqqav1svvnm2XrrrbPnnnvmrrvu+loKGzhwYHbaaaevpe0it99+e6qqqjJjxoxv5HijR49OVVVVbrjhhiWu7927d3r16vWF2nzjjTdy1FFH5f/+7/9Ky3bccccMGjToK9X6ZS3t33P+/PmZNGlS9ttvv7Rr1y4//OEPs9dee+WWW27JvHnzStt9HX+jOXPmZOjQobnnnnuWWZsAAAAriqUawtCqVaucdtpppd/nz5+fGTNm5JprrsmAAQOy5pprplOnTsu0sGOPPTYfffTRMm3z2+6iiy5K586d873vfe8rt/X444/nwQcfzODBg0vLxowZk9VWW+0rt/11mTVrVo466qj8/e9/z/7775+jjz46derUyaOPPpohQ4bkz3/+c4YPH566det+Lcd/5513cvXVV+f888//WtoHAABYni1VgNC4ceNstdVWiy3v2LFjtt1229x+++3LPEBYf/31l2l733YVFRWpU6dOBg8enKuuuuprOUbLli2/lnaXlaFDh2batGmZOHFiqqurS8s7duyYDTfcMGeccUa6dOmS3XffvYxVAgAArJy+0hwIDRo0SGVlZSoqKkrLFixYkPHjx+fHP/5xttxyy3Tr1i233nprrf1qampy2WWXpUuXLqmurs6hhx6aO++8M1VVVXn11VeTLN7lfd68ebnmmmuy6667prq6Ol27ds24ceMyf/780ja9evXK6aefnvHjx6dTp05p1apV9ttvvzz99NO1jn///fdn//33T5s2bbLllltml112yY033viFzv3jjz/OyJEjs/POO6dVq1bp3r17br755lrb7Ljjjhk6dGh69eqV6urqDBky5DPbq1OnTn75y1/m0Ucfza9+9avPPfb8+fNz2WWXpUePHqmurs5WW22V/fffP5MnT06ysHv/gAEDkiRdu3bNwIEDS/V8egjDe++9l7PPPjs77rhjWrVqlT333DO/+93vah2rqqoqkyZNyimnnJK2bdumTZs26devX95+++2lrmdpvPPOO7ntttuyzz771AoPFtlnn31y8MEHZ80111zi/r169Urv3r1rLZs8eXKqqqry5JNPJln4NzvzzDPTsWPH0r155ZVXJkleffXVUgh2yimnZMcddyy1M2XKlBx44IFp3bp12rdvn9NOOy3vv/9+af3tt9+eVq1aZdKkSdluu+3Svn37vPzyy0t97gAAAMuDpeqBUFNTU2v8+fz58/Pqq69m7Nix+eijj2q9ET7zzDNz++2355hjjknr1q3z6KOPZvDgwfn4449L4/hHjx6d8ePH56ijjsrWW2+d3/zmN7W62i/JoEGDct999+Xoo49OmzZt8pe//CWXXnppXnnllZx33nml7X79619nk002yeDBg1NTU5MLLrgg/fr1y+9///vUqVMnf/jDH3L88cend+/eOf744/Pxxx/nxhtvzJAhQ7Llllsu8eF1SdfjiCOOyP/+7//m+OOPzyabbJIHH3wwZ5xxRv7zn//kuOOOK217/fXX5/DDD8+RRx6ZNdZY43Pb3XffffPrX/86F1xwQXbYYYesu+66S9xu2LBhueWWW3LSSSdl0003zRtvvJFLL700/fr1ywMPPJDOnTunb9++GT16dMaMGZOqqqrF2pg9e3YOOOCAvP/+++nXr1/WWWed3HPPPenbt28uuOCC/PSnPy1te9FFF2WnnXbKiBEj8tJLL2Xo0KGprKzMhRdeuFT1rLrqqoXX9LHHHsu8efM+sydLnTp1vvL8Deedd14eeeSRDBw4ME2bNs3DDz+cYcOGZa211squu+6acePG5ZhjjskxxxyTn/zkJ0kWhgd9+vTJdtttl5EjR+btt9/OJZdckmeffTY33XRT6tVb+D+huXPn5rrrrsv555+fd999d6XrQQMAAKz4lipAePzxx7PFFlvUWlZRUZGqqqqMHDkyXbp0SZJMnz49t9xySwYMGJBDDz00SbL99ttn/vz5GTlyZPbee+/U1NTkyiuvzCGHHJJ+/folSXbYYYe89dZbefjhh5d4/H/961+58847M2DAgBx22GFJkg4dOmSVVVbJRRddlN69e2ezzTZLsjDcmDBhQho3bpwk+eijj3LyySfnueeey+abb54XXnghe+65Z0455ZRS+23atEn79u3zxBNPLFWA8NBDD+WJJ57IyJEj061bt9J5zps3L+PHj88BBxyQJk2aJEm+//3v54QTTliay5yKioqcc8456dmzZ84888yMG7fkT8+9+eab+cUvfpEDDzywtKxBgwbp27dv/vWvf6W6ujrf//73kyQtWrTIeuutt1gbt99+e1544YXceuutpXPu1KlTZs6cmQsvvDC77bZbaa6BzTffvDQvQIcOHfL000/n97///Reqp8jrr7+eJGnevHnhtl/WE088kQ4dOqR79+5Jkvbt26dhw4Zp0qRJKisrS0M81l9//dK/hw8fno033jjjx49PnToLO+y0bNkye+yxR37961+nZ8+eSRaGSscdd9wyH8oDAADwbbFUAUJ1dXVOP/30JAtn9x85cmTmzZuXSy65JBtttFFpu8cffzw1NTXp0qVLrR4LO+64Y6699to89dRTmT9/fj7++OPsvPPOtY7RvXv3zwwQpkyZkiTp0aNHreU9e/bMRRddlClTppQChKqqqlJ4kKT0Fn/WrFlJkiOPPDLJwmBh+vTpefnll0tDHObOnbs0lyNTpkxJ/fr1S2+pF9ltt91y0003Zdq0aencuXOShQ/wX8T666+f/v375/zzz8+999672DknySWXXJJkYbf/F198MS+99FIeeOCBL3wOG2ywwWIP97vttlsefvjhvPjii9l0002TJFtvvXWtbb7zne9k9uzZy7SeRW/yFyxYsFTbfxnt27fPpEmTMmPGjHTq1CmdOnWq1Vvkv82ePTvTpk3LkUcemQULFpRq23TTTdO8efP8+c9/LgUIycKgBQAAYEW1VAFCo0aN0qpVqyQLv8iw1VZbpWfPnjn00ENz++23Z6211kqycEx9ktJb+f/25ptvluZLWLTPIs2aNfvM48+cOTNJ0rRp01rLF/3+wQcflJatssoqtbZZ9NZ40cPfO++8kzPOOCO///3vU1FRkQ022CDbbLNNkoVvkZfGzJkz07Rp01Lb/30On66nYcOGS9Xmpx188MH5zW9+k3POOSfbbbfdYuuffvrpDBkyJE8//XRWXXXVbLLJJqU391/kHJZ0zZd0Dku6pp9+0F8W9Sza/rXXXisFF//tjTfeyNprr73YdV9agwYNyne+853cfffdOfvss3P22WenTZs2OfPMM5f48P/++++X5vQYP378YuvffPPNWr83atToS9UFAACwPFiqAOG/NWvWLKeffnr69euXc889N8OHD0+S0icCJ06cuNhDZ5Kst956ef7555Mkb7/9dq1x4p+elO+/rb766qVtPj0vwFtvvZUkpeECS+Okk07K9OnTc80116RNmzaprKzM7Nmzc8sttyx1G6uvvnrefvvtLFiwoNbD7Je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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig7a = df12.sort_values(by='All Participants', ascending=False).plot.barh(figsize=(14.4, 12), stacked=False, width=0.75)\n", + "fig7a.grid(False)\n", + "fig7a.grid(axis='x', color='whitesmoke')\n", + "fig7a.set_title('On which of these have you run software for solar-physics research?', y=1.2)\n", + "fig7a.set_xlabel('Percentage in each category')\n", + "fig7a.set_xlim(0.0, 101.0)\n", + "fig7a.axvline(0, color='lightgray', lw=1.75)\n", + "fig7a.invert_yaxis()\n", + "fig7a.legend(bbox_to_anchor=(0., 1.01, 0.99, .101), loc='lower left', mode=\"expand\", borderaxespad=0., ncol=1)\n", + "fig7a.spines['top'].set_visible(False)\n", + "fig7a.spines['right'].set_visible(False)\n", + "fig7a.spines['bottom'].set_visible(False)\n", + "fig7a.spines['left'].set_visible(False)\n", + "fig7a.figure.savefig(\"Figure7a.png\",bbox_inches='tight',dpi=300)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute Resources vs. Expertise" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [], + "source": [ + "# Observational (Space-Based)\n", + "space = df['Which of these areas of solar physics do you work in? Check all that apply.'].str.contains('Space-Based')\n", + "list_of_all_values_space= df['On which of these have you run software for solar-physics research?'][space].tolist()\n", + "total_space = len(list_of_all_values_space)\n", + "user_counts_space = np.array(count_all_the_hardware(list_of_all_values_space))\n", + "user_percentages_space = (np.array(user_counts_space)/total_space) * 100.\n", + "\n", + "# Observational (Ground-Based)\n", + "obs = df['Which of these areas of solar physics do you work in? Check all that apply.'].str.contains('Ground-Based')\n", + "list_of_all_values_obs= df['On which of these have you run software for solar-physics research?'][obs].tolist()\n", + "total_obs = len(list_of_all_values_obs)\n", + "user_counts_obs = count_all_the_hardware(list_of_all_values_obs)\n", + "user_percentages_obs = (np.array(user_counts_obs)/total_obs) * 100.\n", + "\n", + "# Instrumentation\n", + "inst = df['Which of these areas of solar physics do you work in? Check all that apply.'].str.contains('Instrumentation')\n", + "list_of_all_values_inst= df['On which of these have you run software for solar-physics research?'][inst].tolist()\n", + "total_inst = len(list_of_all_values_inst)\n", + "user_counts_inst = count_all_the_hardware(list_of_all_values_inst)\n", + "user_percentages_inst = (np.array(user_counts_inst)/total_inst) * 100.\n", + "\n", + "# Numerical Simulations\n", + "sim = df['Which of these areas of solar physics do you work in? Check all that apply.'].str.contains('Numerical Simulations')\n", + "list_of_all_values_sim = df['On which of these have you run software for solar-physics research?'][sim].tolist()\n", + "total_sim = len(list_of_all_values_sim)\n", + "user_counts_sim = count_all_the_hardware(list_of_all_values_sim)\n", + "user_percentages_sim = (np.array(user_counts_sim)/total_sim) * 100.\n", + "\n", + "# Theory\n", + "theory = df['Which of these areas of solar physics do you work in? Check all that apply.'].str.contains('Theory')\n", + "list_of_all_values_theory = df['On which of these have you run software for solar-physics research?'][theory].tolist()\n", + "total_theory = len(list_of_all_values_theory)\n", + "user_counts_theory = count_all_the_hardware(list_of_all_values_theory)\n", + "user_percentages_theory = (np.array(user_counts_theory)/total_theory) * 100." + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Observational (Space-Based)Observational (Ground-Based)InstrumentationNumerical SimulationsTheory
Laptop / Desktop computer96.36363697.63313697.84946295.90643393.333333
Shared workstation41.45454544.97041448.38709741.52046838.095238
Local Cluster49.09090952.66272245.16129062.57309960.000000
Regional or National Cluster9.81818212.4260365.37634426.31578925.714286
GPU9.0909097.6923083.22580611.69590613.333333
Commercial cloud5.8181824.7337287.5268823.5087724.761905
\n", + "
" + ], + "text/plain": [ + " Observational (Space-Based) \\\n", + "Laptop / Desktop computer 96.363636 \n", + "Shared workstation 41.454545 \n", + "Local Cluster 49.090909 \n", + "Regional or National Cluster 9.818182 \n", + "GPU 9.090909 \n", + "Commercial cloud 5.818182 \n", + "\n", + " Observational (Ground-Based) Instrumentation \\\n", + "Laptop / Desktop computer 97.633136 97.849462 \n", + "Shared workstation 44.970414 48.387097 \n", + "Local Cluster 52.662722 45.161290 \n", + "Regional or National Cluster 12.426036 5.376344 \n", + "GPU 7.692308 3.225806 \n", + "Commercial cloud 4.733728 7.526882 \n", + "\n", + " Numerical Simulations Theory \n", + "Laptop / Desktop computer 95.906433 93.333333 \n", + "Shared workstation 41.520468 38.095238 \n", + "Local Cluster 62.573099 60.000000 \n", + "Regional or National Cluster 26.315789 25.714286 \n", + "GPU 11.695906 13.333333 \n", + "Commercial cloud 3.508772 4.761905 " + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df12a = pd.DataFrame({'Observational (Space-Based)': list(user_percentages_space), \n", + " 'Observational (Ground-Based)': list(user_percentages_obs), \n", + " 'Instrumentation': list(user_percentages_inst),\n", + " 'Numerical Simulations': list(user_percentages_sim),\n", + " 'Theory': list(user_percentages_theory)},\n", + " index=hardware_names)\n", + "df12a" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig7b = df12a.sort_values(by='Numerical Simulations', ascending=False).plot.barh(figsize=(14.4, 12), stacked=False, width=0.75)\n", + "fig7a.grid(False)\n", + "fig7a.grid(axis='x', color='whitesmoke')\n", + "fig7b.set_title('On which of these have you run software for solar-physics research?', y=1.2)\n", + "fig7b.set_xlabel('Percentage of total respondents (n=364)')\n", + "fig7b.set_xlim(0.0, 101.0)\n", + "fig7b.axvline(0, color='lightgray', lw=1.75)\n", + "fig7b.invert_yaxis()\n", + "fig7b.legend(bbox_to_anchor=(0., 1.01, 0.99, .101), loc='lower left', mode=\"expand\", borderaxespad=0., ncol=1)\n", + "fig7b.spines['top'].set_visible(False)\n", + "fig7b.spines['right'].set_visible(False)\n", + "fig7b.spines['bottom'].set_visible(False)\n", + "fig7b.spines['left'].set_visible(False)\n", + "fig7b.figure.savefig(\"Figure7b.png\",bbox_inches='tight',dpi=300)" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Percentage of people who only responded with laptop/desktop: 28.846153846153843\n" + ] + } + ], + "source": [ + "personal_only_numbers = len(df.loc[df['On which of these have you run software for solar-physics research?'] == 'Laptop / Desktop computer'])\n", + "print(\"Percentage of people who only responded with laptop/desktop:\",(personal_only_numbers/total_responses)*100.)" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [], + "source": [ + "no_laptop = ~df['On which of these have you run software for solar-physics research?'].str.contains('Laptop')\n", + "yes_laptop = df['On which of these have you run software for solar-physics research?'].str.contains('Laptop')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Indeed, 15 people do not use their personal laptops or desktops at all:" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Which of these areas of solar physics do you work in? Check all that apply.How would you describe the stage of your career?What country is your institution in?Do you self-identify as one or more underrepresented minorities in solar physics? This question is optional.Do you self-identify as a unrepresented gender identity in Solar Physics? This question is optional.Do you use software in your research?Have you had formal training in programming?Which of the following statements is most applicable to you?Which of the following have you personally utilized in your work within the last year?Have you cited software papers in your published research?Why haven’t you cited software in your research?On which of these have you run software for solar-physics research?Breadth
100Observational (Space-Based), Numerical Simulat...Faculty, Staff Scientist, or ResearcherUnited StatesNoNoYesNoSomewhere in between.IDL, Python, Fortran, Github (or similar)YesNaNShared workstation4
111Observational (Space-Based), Observational (Gr...Faculty, Staff Scientist, or ResearcherUnited KingdomNoNoYesYes, a little (e.g. online classes, books, wor...Somewhere in between.IDL, SolarSoft, Python, Shell scripting, FortranSometimesNaNShared workstation5
158Observational (Space-Based), Observational (Gr...Faculty, Staff Scientist, or ResearcherSlovakiaNaNNaNYesYes, a lot (e.g. CS courses at an undergraduat...Somewhere in between.IDL, SolarSoftYesNaNShared workstation2
182Observational (Space-Based), Observational (Gr...Faculty, Staff Scientist, or ResearcherJapanYesNoYesYes, a little (e.g. online classes, books, wor...I mostly use software written by others.IDL, SolarSoftNoI am not sure how to appropriately cite softwareShared workstation2
191TheoryFaculty, Staff Scientist, or ResearcherIndiaNaNNaNYesYes, a lot (e.g. CS courses at an undergraduat...Somewhere in between.C, Fortran, R, MathematicaSometimesNaNLocal Cluster4
204Observational (Space-Based), Numerical Simulat...Graduate or Undergraduate StudentGermanyNoNoYesYes, a lot (e.g. CS courses at an undergraduat...I write mostly my own software.Python, MathematicaNoI am not sure how to appropriately cite softwareLocal Cluster2
205TheoryFaculty, Staff Scientist, or ResearcherGermanyNoYesYesNoSomewhere in between.IDL, SolarSoftNoI am not sure how to appropriately cite softwareShared workstation, Local Cluster2
209Numerical SimulationsGraduate or Undergraduate StudentGermanyNoNoYesYes, a lot (e.g. CS courses at an undergraduat...I write mostly my own software.Python, MATLABNoI do not think it is necessaryLocal Cluster2
212Numerical Simulations, TheoryGraduate or Undergraduate StudentGermanyNaNNaNYesNoSomewhere in between.RYesNaNShared workstation, Local Cluster1
217Numerical Simulations, TheoryGraduate or Undergraduate StudentUnited StatesNoNoYesYes, a lot (e.g. CS courses at an undergraduat...Somewhere in between.IDL, FortranSometimesNaNRegional or National Cluster2
237Observational (Space-Based)Graduate or Undergraduate StudentAustriaNoNoYesYes, a lot (e.g. CS courses at an undergraduat...I write mostly my own software.IDL, SolarSoft, Python, SunPy, Shell scripting...SometimesNaNShared workstation, Local Cluster8
279Observational (Space-Based), InstrumentationFaculty, Staff Scientist, or ResearcherUnited StatesNaNNaNYesNoI write mostly my own software.IDL, PythonYesNaNShared workstation2
281Observational (Space-Based)Faculty, Staff Scientist, or ResearcherBelgiumNaNNaNYesYes, a lot (e.g. CS courses at an undergraduat...Somewhere in between.IDL, SolarSoftSometimesNaNShared workstation, Local Cluster2
308Observational (Space-Based), Numerical Simulat...PostdocGermanyNoNoYesNoSomewhere in between.PythonYesNaNLocal Cluster1
312Observational (Space-Based), Observational (Gr...Faculty, Staff Scientist, or ResearcherUnited StatesNoNoYesYes, a lot (e.g. CS courses at an undergraduat...Somewhere in between.IDL, SolarSoftNoI am not sure how to appropriately cite softwareShared workstation, Local Cluster2
\n", + "
" + ], + "text/plain": [ + " Which of these areas of solar physics do you work in? Check all that apply. \\\n", + "100 Observational (Space-Based), Numerical Simulat... \n", + "111 Observational (Space-Based), Observational (Gr... \n", + "158 Observational (Space-Based), Observational (Gr... \n", + "182 Observational (Space-Based), Observational (Gr... \n", + "191 Theory \n", + "204 Observational (Space-Based), Numerical Simulat... \n", + "205 Theory \n", + "209 Numerical Simulations \n", + "212 Numerical Simulations, Theory \n", + "217 Numerical Simulations, Theory \n", + "237 Observational (Space-Based) \n", + "279 Observational (Space-Based), Instrumentation \n", + "281 Observational (Space-Based) \n", + "308 Observational (Space-Based), Numerical Simulat... \n", + "312 Observational (Space-Based), Observational (Gr... \n", + "\n", + " How would you describe the stage of your career? \\\n", + "100 Faculty, Staff Scientist, or Researcher \n", + "111 Faculty, Staff Scientist, or Researcher \n", + "158 Faculty, Staff Scientist, or Researcher \n", + "182 Faculty, Staff Scientist, or Researcher \n", + "191 Faculty, Staff Scientist, or Researcher \n", + "204 Graduate or Undergraduate Student \n", + "205 Faculty, Staff Scientist, or Researcher \n", + "209 Graduate or Undergraduate Student \n", + "212 Graduate or Undergraduate Student \n", + "217 Graduate or Undergraduate Student \n", + "237 Graduate or Undergraduate Student \n", + "279 Faculty, Staff Scientist, or Researcher \n", + "281 Faculty, Staff Scientist, or Researcher \n", + "308 Postdoc \n", + "312 Faculty, Staff Scientist, or Researcher \n", + "\n", + " What country is your institution in? \\\n", + "100 United States \n", + "111 United Kingdom \n", + "158 Slovakia \n", + "182 Japan \n", + "191 India \n", + "204 Germany \n", + "205 Germany \n", + "209 Germany \n", + "212 Germany \n", + "217 United States \n", + "237 Austria \n", + "279 United States \n", + "281 Belgium \n", + "308 Germany \n", + "312 United States \n", + "\n", + " Do you self-identify as one or more underrepresented minorities in solar physics? This question is optional. \\\n", + "100 No \n", + "111 No \n", + "158 NaN \n", + "182 Yes \n", + "191 NaN \n", + "204 No \n", + "205 No \n", + "209 No \n", + "212 NaN \n", + "217 No \n", + "237 No \n", + "279 NaN \n", + "281 NaN \n", + "308 No \n", + "312 No \n", + "\n", + " Do you self-identify as a unrepresented gender identity in Solar Physics? This question is optional. \\\n", + "100 No \n", + "111 No \n", + "158 NaN \n", + "182 No \n", + "191 NaN \n", + "204 No \n", + "205 Yes \n", + "209 No \n", + "212 NaN \n", + "217 No \n", + "237 No \n", + "279 NaN \n", + "281 NaN \n", + "308 No \n", + "312 No \n", + "\n", + " Do you use software in your research? \\\n", + "100 Yes \n", + "111 Yes \n", + "158 Yes \n", + "182 Yes \n", + "191 Yes \n", + "204 Yes \n", + "205 Yes \n", + "209 Yes \n", + "212 Yes \n", + "217 Yes \n", + "237 Yes \n", + "279 Yes \n", + "281 Yes \n", + "308 Yes \n", + "312 Yes \n", + "\n", + " Have you had formal training in programming? \\\n", + "100 No \n", + "111 Yes, a little (e.g. online classes, books, wor... \n", + "158 Yes, a lot (e.g. CS courses at an undergraduat... \n", + "182 Yes, a little (e.g. online classes, books, wor... \n", + "191 Yes, a lot (e.g. CS courses at an undergraduat... \n", + "204 Yes, a lot (e.g. CS courses at an undergraduat... \n", + "205 No \n", + "209 Yes, a lot (e.g. CS courses at an undergraduat... \n", + "212 No \n", + "217 Yes, a lot (e.g. CS courses at an undergraduat... \n", + "237 Yes, a lot (e.g. CS courses at an undergraduat... \n", + "279 No \n", + "281 Yes, a lot (e.g. CS courses at an undergraduat... \n", + "308 No \n", + "312 Yes, a lot (e.g. CS courses at an undergraduat... \n", + "\n", + " Which of the following statements is most applicable to you? \\\n", + "100 Somewhere in between. \n", + "111 Somewhere in between. \n", + "158 Somewhere in between. \n", + "182 I mostly use software written by others. \n", + "191 Somewhere in between. \n", + "204 I write mostly my own software. \n", + "205 Somewhere in between. \n", + "209 I write mostly my own software. \n", + "212 Somewhere in between. \n", + "217 Somewhere in between. \n", + "237 I write mostly my own software. \n", + "279 I write mostly my own software. \n", + "281 Somewhere in between. \n", + "308 Somewhere in between. \n", + "312 Somewhere in between. \n", + "\n", + " Which of the following have you personally utilized in your work within the last year? \\\n", + "100 IDL, Python, Fortran, Github (or similar) \n", + "111 IDL, SolarSoft, Python, Shell scripting, Fortran \n", + "158 IDL, SolarSoft \n", + "182 IDL, SolarSoft \n", + "191 C, Fortran, R, Mathematica \n", + "204 Python, Mathematica \n", + "205 IDL, SolarSoft \n", + "209 Python, MATLAB \n", + "212 R \n", + "217 IDL, Fortran \n", + "237 IDL, SolarSoft, Python, SunPy, Shell scripting... \n", + "279 IDL, Python \n", + "281 IDL, SolarSoft \n", + "308 Python \n", + "312 IDL, SolarSoft \n", + "\n", + " Have you cited software papers in your published research? \\\n", + "100 Yes \n", + "111 Sometimes \n", + "158 Yes \n", + "182 No \n", + "191 Sometimes \n", + "204 No \n", + "205 No \n", + "209 No \n", + "212 Yes \n", + "217 Sometimes \n", + "237 Sometimes \n", + "279 Yes \n", + "281 Sometimes \n", + "308 Yes \n", + "312 No \n", + "\n", + " Why haven’t you cited software in your research? \\\n", + "100 NaN \n", + "111 NaN \n", + "158 NaN \n", + "182 I am not sure how to appropriately cite software \n", + "191 NaN \n", + "204 I am not sure how to appropriately cite software \n", + "205 I am not sure how to appropriately cite software \n", + "209 I do not think it is necessary \n", + "212 NaN \n", + "217 NaN \n", + "237 NaN \n", + "279 NaN \n", + "281 NaN \n", + "308 NaN \n", + "312 I am not sure how to appropriately cite software \n", + "\n", + " On which of these have you run software for solar-physics research? \\\n", + "100 Shared workstation \n", + "111 Shared workstation \n", + "158 Shared workstation \n", + "182 Shared workstation \n", + "191 Local Cluster \n", + "204 Local Cluster \n", + "205 Shared workstation, Local Cluster \n", + "209 Local Cluster \n", + "212 Shared workstation, Local Cluster \n", + "217 Regional or National Cluster \n", + "237 Shared workstation, Local Cluster \n", + "279 Shared workstation \n", + "281 Shared workstation, Local Cluster \n", + "308 Local Cluster \n", + "312 Shared workstation, Local Cluster \n", + "\n", + " Breadth \n", + "100 4 \n", + "111 5 \n", + "158 2 \n", + "182 2 \n", + "191 4 \n", + "204 2 \n", + "205 2 \n", + "209 2 \n", + "212 1 \n", + "217 2 \n", + "237 8 \n", + "279 2 \n", + "281 2 \n", + "308 1 \n", + "312 2 " + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[no_laptop]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Conclusions:\n", + "* Hardware choices vary significantly across area of expertise.\n", + " * A greater percentage of people doing numerical simulations + theory utilize local clusters (63% and 60%, respectively, compared with 51% overall).\n", + " * A greater percentage of people doing numerical simulations + theory utilize regional/national clusters (26% and 26%, respectively, compared with 14% overall). \n", + "* Everyone relies on their personal machine. 29% of the community relies exclusively on their personal machine.\n", + "* The solar physics community puts a lot of effort into building and maintaining their own local clusters and workstations. Most of the community who wants additional computational resource uses a shared workstation or local cluster. Half the community has experience working with a local cluster. \n", + "* The community does not utilize pre-existing infrastructure:\n", + " * 5% of the community has experience with commercial cloud.\n", + " * 14% with a regional/national cluster. (However, some countries like the United States require citizenship or permanent residence status to use these clusters)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Question 13: Do you have any comments? \n", + "**(This is a free form response; comments are not required. Please feel free to give us feedback about topics like: version control, collaborative coding platforms such as Github, standard or best practices in coding, operating systems, text editors, or your personal experience with writing code and releasing software, or general thoughts about SunPy).**" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [], + "source": [ + "df_comments = pd.read_csv('free_form_comments.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Only wish that some formal programming training was standard in the undergrad/grad curriculum. \n", + "\n", + "I think version control and backward compatibility may be taken into some more serious consideration as SunPy goes into future. Sometimes I got frustrated as some scripts I wrote that utilize functions from earlier versions of SunPy do not work anymore after updating SunPy to a newer version.\n", + "\n", + "SunPy is great and I do 95% of my coding in Python. But I still have to use IDL/SolarSoft for the aia_prep function because I am under the impression the equivalent function in SunPy does not produce the same results. This single routine means I still require an IDL license. \n", + "\n", + "I've not published any papers yet.\n", + "\n", + "The barriers between creating a work flow on a personal workstation and commercial/private cloud are significant. I am not sure where to begin transitioning from using everything local to making use of a cloud environment. \n", + "\n", + "I shifted from IDL/SSW to Python/Sunpy when I moved to the private university from the national institute. I think that development of Sunpy is very useful for Solar Physics Community to promote the scientist communications across the science fields because most scientist in the other astronomy field recently use Python as a main software for data analysis.\n", + "\n", + "My background is more IDL based. I have been trying python and would like to use it more, but the difficulty is the large amount of heritage code I have in IDL, and the fact that converting it would be time consuming. \n", + "\n", + "Institutional researchers aren't the only people using SunPy.\n", + "\n", + "I created an English wikipedia item for SunPy, but it was deleted again and again.\n", + "https://en.wikipedia.org/wiki/SunPy . However, I created a Chinese version https://zh.wikipedia.org/wiki/SunPy . It seems that the English speaking community does not recognize the status of SunPy. \n", + "\n", + "I am hesitating re IDL > Python waiting for complete SolarSoft port.\n", + "\n", + "My only issue with SunPy is it should have better documentation describing the code properly. This could be a good task for summer students?\n", + "\n", + "I started doing solar research using IDL and am in the process of trying to convert to using Python and Sunpy as my primary programming environment. I am finding it particularly difficult to make this transition cleanly because my work relies on a number of programs and packages written in IDL. As such, I'm in a position of having to run most of my analysis in IDL and then adapt the outputs so that they can be manipulated and visualized in Python.\n", + "\n", + "General thoughts about SunPy: a major turnoff is the naming convention in variables (that is, the lack of) and syntax of functions. The variables are rarely given descriptive names, and the functions require some horrid syntax. For example, from the SunPy tutorial, consider this ugly code: aia = sunpy.map.Map(sunpy.data.sample.AIA_171_IMAGE). This is hideous, and strongly discourages me from adopting SunPy. (map.Map? Really? And it's even case sensitive?)\n", + "\n", + "\n", + "\n", + "We develop applications primarily for local, specialized use, which are then extended for community/shared use. In my view, the area where SunPy should put resources is in providing facility (information, user guides, platforms) for making this transition as seamless as possible. Specifically, guidelines or tools for code version control BEFORE software is released for general use would be appreciated.\n", + "\n", + "Version control (SVN) plus openly shared software (Python\n", + "\n", + "I love using SunPy, but sometimes feel as if I am in an infinite loop when using the documentation. I have been very impressed though, with the responsiveness of the developers to my questions. \n", + "\n", + "Not yet used SunPy\n", + "\n", + "Replacing the core functionality of Solarsoft would be a \"game changer\". \n", + "\n", + "SunPy should aim to expand its reach to more instruments. Especially, in regards to future missions like solar orbiter, JVLA, SKA etc.. This way you guys can become more relevant in the solar physics community. \n", + "\n", + "SunPy actually can expand quickly w.r.t radio studies of Sun. One can develop modules by using python-based CASA and existing SDO instruments. This would be very useful for the solar community as radio wavelengths are slowly becoming mainstream in solar physics.\n", + "\n", + "I think it is good to share software that one has written and that may be of use for the community. However, I have not done that, mostly because I dont think I am an expert programmer or because my code works \"for me\" but not in general cases. Citing software seems more useful if the software computes scientific results, e.g. ADIPLS, GYRE, MESA, ... I am not sure whether tools, e.g. python packages for reading/ writing MESA models, etc. should be cited or acknowledged. So far, I tried to acknowledge such tools if I used them. I guess SunPy is somewhere in between these two cases. But since journals offer the possibility to do software papers nowadays, I think one should cite them if used!\n", + "\n", + "sunpy will highly take advantages for students and faculty that IDL comes with high cost\n", + "\n", + "I'm a data manager and don't publish my own research, so my responses may not be applicable. I would think researchers using specialized software and open-source software should cite these packages.\n", + "\n", + "SunPy is generally great and very useful for my work. However, I find following examples to be the easiest way to understand how to use various codes, and it seems to me that there are not enough examples and/or the examples given are overly simplified or generic. There are often keyword options for routines that either don't have example usage or clear usage documentation. It would also be nice to have access to the ephemeris data for various satellites/instruments without necessarily having to read in actual data.\n", + "\n", + "Please send us the survey results including the error bars. \n", + "\n", + "One issue that came up in a discussion recently was how code sharing is not currently well incentivized. In particular, it takes significant time investment to develop code for public release, and it is unclear if that work (which amounts to community service in some respect) is properly valued when it comes to job applications and career advancement compared to e.g. publications.\n", + "\n", + "SolarSoft and IDL are very important to me.\n", + "\n", + "SunPy is a great tool to bring observational solar physics into the python environment. At this time there is no significant SunPy support for widely used data driven models (PFSS, CHANTI, WSA, ENLIL, NLFF, BATS-R-US, etc.). For SunPy to support the entire field, I would like to see affiliated modeling packages too. \n", + "\n", + "Plan to start learning Python\n", + "\n", + "I switched to Python a few years ago but still have to deal with several astronomy IDL packages. The engineering work I do is split between Matlab, Mathematica and various professional engineering packages, mostly in Windows (unfortunately). There is a big difference between a quick-and-dirty proof of concept with data analysis scripts, and industrial strength, error proof automated software package development. I have no formal training in coding practices though some branches of astronomy are moving that direction. Some large professional engineering packages come with scripting languages (ZPL in Zemax for instance) and often simple (messy) code is enough to prove that an algorithm works or get a quick result. I have no desire to retrain as a software engineer as those professionals with far better skills are engaged in most package development efforts I've seen. I would never attempt to write software packages for distribution without engaging an actual professional. \n", + "\n", + "We need more robust approaches to reproducibility in software-based numerical and theoretical studies. The standards in solar physics are well behind other fields, and many solar physics publications would be rejected out of hand in the physics (PRL, PRF, etc.) and fluid dynamics (JFM, etc.) journals. This stems from fundamental non-reproducibility of much of the solar work, especially work built around closed-source code communities. It's time for us as a discipline to embrace well documented best practices.\n", + "\n", + "Need IRIS IDL tools re-written SunPy. PyRAF/IRAF is still very useful to observational astronomers.\n", + "\n", + "sunPy will prevail eventually, it needs time and big projects, e.g., space instruments and DKIST!, to adopt it first.\n", + "\n", + "Am sick of IDL and am planning to transition to SunPy.\n", + "\n", + "The problem with SunPy that it lacks the detailed _prep routines as they are available in solarsoft now. For instance, the aia_prep has a lot of detail in solarsoft, but in python it is only a few lines. I am sure the other lines are there for a good reason. Usually I end up doing the aia_prep in IDL, and then transfer the data to python to work with.\n", + "\n", + "I have become productive with Python and Jupyter notebooks in just the past year, but am not yet making use of SunPy. I enjoy playing with Python, but since I have little prior background in object oriented coding, and since Python has an idiosyncratic approach to array indexing, I find that it comes a bit slowly to me.\n", + "\n", + "I am hoping to find time to learn more about SunPy, so that I can move away from IDL at some point. A training course would be great.\n", + "\n", + "IDL documentation is always easier to follow. Can't put finger on why.\n", + "\n", + "There should be more formal training, available to researchers at all points in academic life, regarding the topics you mentioned above (version control, coding practices, etc.)\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "for i in range(len(df_comments)):\n", + " print(df_comments['Do you have any comments? (This is a free form response; comments are not required. Please feel free to give us feedback about topics like: version control, collaborative coding platforms such as Github, standard or best practices in coding, operating systems, text editors, or your personal experience with writing code and releasing software, or general thoughts about SunPy).'][i])\n", + " print(\"\")" + ] + } + ], + "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": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/2019_survey/free_form_comments.csv b/2019_survey/free_form_comments.csv new file mode 100644 index 0000000..a44508d --- /dev/null +++ b/2019_survey/free_form_comments.csv @@ -0,0 +1,46 @@ +"Do you have any comments? (This is a free form response; comments are not required. Please feel free to give us feedback about topics like: version control, collaborative coding platforms such as Github, standard or best practices in coding, operating systems, text editors, or your personal experience with writing code and releasing software, or general thoughts about SunPy)." +Only wish that some formal programming training was standard in the undergrad/grad curriculum. +I think version control and backward compatibility may be taken into some more serious consideration as SunPy goes into future. Sometimes I got frustrated as some scripts I wrote that utilize functions from earlier versions of SunPy do not work anymore after updating SunPy to a newer version. +SunPy is great and I do 95% of my coding in Python. But I still have to use IDL/SolarSoft for the aia_prep function because I am under the impression the equivalent function in SunPy does not produce the same results. This single routine means I still require an IDL license. +I've not published any papers yet. +The barriers between creating a work flow on a personal workstation and commercial/private cloud are significant. I am not sure where to begin transitioning from using everything local to making use of a cloud environment. +I shifted from IDL/SSW to Python/Sunpy when I moved to the private university from the national institute. I think that development of Sunpy is very useful for Solar Physics Community to promote the scientist communications across the science fields because most scientist in the other astronomy field recently use Python as a main software for data analysis. +"My background is more IDL based. I have been trying python and would like to use it more, but the difficulty is the large amount of heritage code I have in IDL, and the fact that converting it would be time consuming. " +Institutional researchers aren't the only people using SunPy. +"I created an English wikipedia item for SunPy, but it was deleted again and again. +https://en.wikipedia.org/wiki/SunPy . However, I created a Chinese version https://zh.wikipedia.org/wiki/SunPy . It seems that the English speaking community does not recognize the status of SunPy. " +I am hesitating re IDL > Python waiting for complete SolarSoft port. +My only issue with SunPy is it should have better documentation describing the code properly. This could be a good task for summer students? +"I started doing solar research using IDL and am in the process of trying to convert to using Python and Sunpy as my primary programming environment. I am finding it particularly difficult to make this transition cleanly because my work relies on a number of programs and packages written in IDL. As such, I'm in a position of having to run most of my analysis in IDL and then adapt the outputs so that they can be manipulated and visualized in Python." +"General thoughts about SunPy: a major turnoff is the naming convention in variables (that is, the lack of) and syntax of functions. The variables are rarely given descriptive names, and the functions require some horrid syntax. For example, from the SunPy tutorial, consider this ugly code: aia = sunpy.map.Map(sunpy.data.sample.AIA_171_IMAGE). This is hideous, and strongly discourages me from adopting SunPy. (map.Map? Really? And it's even case sensitive?) + +" +"We develop applications primarily for local, specialized use, which are then extended for community/shared use. In my view, the area where SunPy should put resources is in providing facility (information, user guides, platforms) for making this transition as seamless as possible. Specifically, guidelines or tools for code version control BEFORE software is released for general use would be appreciated." +Version control (SVN) plus openly shared software (Python +"I love using SunPy, but sometimes feel as if I am in an infinite loop when using the documentation. I have been very impressed though, with the responsiveness of the developers to my questions. " +Not yet used SunPy +"Replacing the core functionality of Solarsoft would be a ""game changer"". " +"SunPy should aim to expand its reach to more instruments. Especially, in regards to future missions like solar orbiter, JVLA, SKA etc.. This way you guys can become more relevant in the solar physics community. + +SunPy actually can expand quickly w.r.t radio studies of Sun. One can develop modules by using python-based CASA and existing SDO instruments. This would be very useful for the solar community as radio wavelengths are slowly becoming mainstream in solar physics." +"I think it is good to share software that one has written and that may be of use for the community. However, I have not done that, mostly because I dont think I am an expert programmer or because my code works ""for me"" but not in general cases. Citing software seems more useful if the software computes scientific results, e.g. ADIPLS, GYRE, MESA, ... I am not sure whether tools, e.g. python packages for reading/ writing MESA models, etc. should be cited or acknowledged. So far, I tried to acknowledge such tools if I used them. I guess SunPy is somewhere in between these two cases. But since journals offer the possibility to do software papers nowadays, I think one should cite them if used!" +sunpy will highly take advantages for students and faculty that IDL comes with high cost +"I'm a data manager and don't publish my own research, so my responses may not be applicable. I would think researchers using specialized software and open-source software should cite these packages." +"SunPy is generally great and very useful for my work. However, I find following examples to be the easiest way to understand how to use various codes, and it seems to me that there are not enough examples and/or the examples given are overly simplified or generic. There are often keyword options for routines that either don't have example usage or clear usage documentation. It would also be nice to have access to the ephemeris data for various satellites/instruments without necessarily having to read in actual data." +Please send us the survey results including the error bars. +"One issue that came up in a discussion recently was how code sharing is not currently well incentivized. In particular, it takes significant time investment to develop code for public release, and it is unclear if that work (which amounts to community service in some respect) is properly valued when it comes to job applications and career advancement compared to e.g. publications." +SolarSoft and IDL are very important to me. +"SunPy is a great tool to bring observational solar physics into the python environment. At this time there is no significant SunPy support for widely used data driven models (PFSS, CHANTI, WSA, ENLIL, NLFF, BATS-R-US, etc.). For SunPy to support the entire field, I would like to see affiliated modeling packages too. " +Plan to start learning Python +"I switched to Python a few years ago but still have to deal with several astronomy IDL packages. The engineering work I do is split between Matlab, Mathematica and various professional engineering packages, mostly in Windows (unfortunately). There is a big difference between a quick-and-dirty proof of concept with data analysis scripts, and industrial strength, error proof automated software package development. I have no formal training in coding practices though some branches of astronomy are moving that direction. Some large professional engineering packages come with scripting languages (ZPL in Zemax for instance) and often simple (messy) code is enough to prove that an algorithm works or get a quick result. I have no desire to retrain as a software engineer as those professionals with far better skills are engaged in most package development efforts I've seen. I would never attempt to write software packages for distribution without engaging an actual professional. " +"We need more robust approaches to reproducibility in software-based numerical and theoretical studies. The standards in solar physics are well behind other fields, and many solar physics publications would be rejected out of hand in the physics (PRL, PRF, etc.) and fluid dynamics (JFM, etc.) journals. This stems from fundamental non-reproducibility of much of the solar work, especially work built around closed-source code communities. It's time for us as a discipline to embrace well documented best practices." +Need IRIS IDL tools re-written SunPy. PyRAF/IRAF is still very useful to observational astronomers. +"sunPy will prevail eventually, it needs time and big projects, e.g., space instruments and DKIST!, to adopt it first." +Am sick of IDL and am planning to transition to SunPy. +"The problem with SunPy that it lacks the detailed _prep routines as they are available in solarsoft now. For instance, the aia_prep has a lot of detail in solarsoft, but in python it is only a few lines. I am sure the other lines are there for a good reason. Usually I end up doing the aia_prep in IDL, and then transfer the data to python to work with." +"I have become productive with Python and Jupyter notebooks in just the past year, but am not yet making use of SunPy. I enjoy playing with Python, but since I have little prior background in object oriented coding, and since Python has an idiosyncratic approach to array indexing, I find that it comes a bit slowly to me." +"I am hoping to find time to learn more about SunPy, so that I can move away from IDL at some point. A training course would be great." +IDL documentation is always easier to follow. Can't put finger on why. +"There should be more formal training, available to researchers at all points in academic life, regarding the topics you mentioned above (version control, coding practices, etc.) + +" diff --git a/2019_survey/raw_survey_responses_no_comments.csv b/2019_survey/raw_survey_responses_no_comments.csv new file mode 100644 index 0000000..541a0df --- /dev/null +++ b/2019_survey/raw_survey_responses_no_comments.csv @@ -0,0 +1,369 @@ +Which of these areas of solar physics do you work in? Check all that apply.,How would you describe the stage of your career?,What country is your institution in?,Do you self-identify as one or more underrepresented minorities in solar physics? This question is optional.,Do you self-identify as a unrepresented gender identity in Solar Physics? This question is optional.,Do you use software in your research?,Have you had formal training in programming?,Which of the following statements is most applicable to you?,Which of the following have you personally utilized in your work within the last year?,Have you cited software papers in your published research?,Why haven’t you cited software in your research?,On which of these have you run software for solar-physics research? +Observational (Space-Based),"Faculty, Staff Scientist, Researcher",United States,Yes,Yes,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"Python, SunPy, Shell scripting, C, SQL, Ruby, Github (or similar)",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster, Commercial cloud" +"Observational (Space-Based), Observational (Ground-Based), Instrumentation","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Shell scripting, C, C++, Fortran, Java, Spreadsheets (e.g. Excel), MPI, Github (or similar)",Sometimes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +Observational (Ground-Based),Software developer,United Kingdom,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"Python, SunPy, Shell scripting, C, Fortran, Github (or similar)",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster, Commercial cloud" +"Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",United States,No,,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"Python, Shell scripting, Fortran, Julia, MPI, Github (or similar)",Yes,,"Laptop / Desktop computer, Local Cluster, Regional or National Cluster" +"Observational (Space-Based), Instrumentation",Postdoc,United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Shell scripting, MATLAB, Ruby, HTML/CSS, Spreadsheets (e.g. Excel), Github (or similar)",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster, Commercial cloud" +Numerical Simulations,"Faculty, Staff Scientist, Researcher",Brazil,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"Python, SunPy, Shell scripting, C, MPI, Github (or similar)",Sometimes,,"Laptop / Desktop computer, GPU" +"Observational (Space-Based), Observational (Ground-Based), Theory","Faculty, Staff Scientist, Researcher",Spain,No,No,Yes,No,I write mostly my own software.,"Python, Shell scripting, Fortran, Javascript, Mathematica, MPI, Github (or similar)",Yes,,"Laptop / Desktop computer, Shared workstation, Regional or National Cluster, GPU" +"Observational (Space-Based), Instrumentation","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"IDL, SolarSoft, Python, SunPy",No,I am not sure how to appropriately cite software,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based)",Graduate student,United Kingdom,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"Python, SunPy, Github (or similar)",Yes,,"Laptop / Desktop computer, GPU" +"Observational (Space-Based), Instrumentation","Faculty, Staff Scientist, Researcher",United States,No,,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Github (or similar)",Sometimes,,"Laptop / Desktop computer, Shared workstation" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations","Faculty, Staff Scientist, Researcher",United Kingdom,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Shell scripting, Perl, Javascript, HTML/CSS, Spreadsheets (e.g. Excel), Mathematica, Github (or similar)",Yes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based)","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Github (or similar)",Yes,,"Laptop / Desktop computer, Local Cluster" +Observational (Space-Based),Postdoc,Czech Republic,,,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, CASA",Yes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Numerical Simulations, Theory",Graduate student,Germany,,,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"Python, SunPy, C, C++, MPI, Github (or similar)",Sometimes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Ground-Based), Instrumentation",Software developer,Germany,,,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"Python, Shell scripting, C, C++, Github (or similar)",No,,"Laptop / Desktop computer, Shared workstation" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Theory, Instrumentation","Faculty, Staff Scientist, Researcher",Germany,,,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"IDL, Fortran, MPI",Yes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based)",Postdoc,Sweden,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"Python, MPI, Github (or similar)",Yes,,Laptop / Desktop computer +Observational (Space-Based),Graduate student,United Kingdom,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"Python, SunPy, Shell scripting, Github (or similar)",Yes,,Laptop / Desktop computer +Observational (Ground-Based),Software developer,United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"IDL, SolarSoft, Python, SunPy, Shell scripting, C, Javascript, SQL, HTML/CSS, Spreadsheets (e.g. Excel), Github (or similar)",No,,"Laptop / Desktop computer, Shared workstation" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Instrumentation",Graduate student,United States,Yes,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Shell scripting, C++, Fortran, IRAF, Perl, HTML/CSS, Spreadsheets (e.g. Excel), Mathematica, Github (or similar)",No,I do not think software belongs in citations,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based)","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Shell scripting, C, Fortran, Javascript, SQL, HTML/CSS, Spreadsheets (e.g. Excel), Github (or similar)",No,I am not sure how to appropriately cite software,"Laptop / Desktop computer, Shared workstation, Commercial cloud" +"Observational (Space-Based), Numerical Simulations, Theory",Graduate student,United States,No,No,Yes,No,Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Shell scripting, C++, HTML/CSS, Github (or similar)",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster, Commercial cloud" +"Observational (Space-Based), Observational (Ground-Based)","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"IDL, SolarSoft, Python, Shell scripting, Perl, Github (or similar)",No,I am not sure how to appropriately cite software,Laptop / Desktop computer +Observational (Ground-Based),Graduate student,United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, Shell scripting, C, C++, Mathematica, MPI, Github (or similar)",Sometimes,,"Laptop / Desktop computer, Local Cluster, Regional or National Cluster" +Observational (Ground-Based),Software developer,Germany,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, Python, SunPy, Shell scripting, SQL, Spreadsheets (e.g. Excel), Github (or similar)",No,I am not sure how to appropriately cite software,"Laptop / Desktop computer, Shared workstation, Local Cluster" +Observational (Space-Based),Software developer,United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"Python, SunPy, C, C++, Github (or similar), CUDA",No,I am not sure how to appropriately cite software,"Laptop / Desktop computer, GPU" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Instrumentation","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Shell scripting, Fortran, Perl, HTML/CSS",Yes,,"Laptop / Desktop computer, Shared workstation" +"Observational (Space-Based), Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",India,No,No,Yes,No,I write mostly my own software.,"Python, Shell scripting, Fortran, Julia, MATLAB, MPI, Github (or similar)",Sometimes,,"Laptop / Desktop computer, Local Cluster, Regional or National Cluster" +"Observational (Space-Based), Observational (Ground-Based), Instrumentation",Graduate student,India,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"Python, SunPy, Github (or similar)",No,I do not think it is necessary,"Laptop / Desktop computer, Local Cluster" +Numerical Simulations,Solar Dimension of Earthquake researches,Turkey,,,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"Python, SunPy, Spreadsheets (e.g. Excel), Github (or similar)",No,I do not think it is necessary,Laptop / Desktop computer +"Observational (Space-Based), Instrumentation","Faculty, Staff Scientist, Researcher",Spain,Yes,No,Yes,No,Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Shell scripting, Spreadsheets (e.g. Excel), Github (or similar)",Yes,,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based), Instrumentation","Faculty, Staff Scientist, Researcher",United Kingdom,No,No,Yes,No,Somewhere in between.,"IDL, SolarSoft, Shell scripting, Spreadsheets (e.g. Excel)",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based)",Undergraduate student,China,Yes,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I mostly use software written by others.,"IDL, SolarSoft, Python, SunPy",No,I am not sure how to appropriately cite software,Laptop / Desktop computer +Observational (Space-Based),"Faculty, Staff Scientist, Researcher",Switzerland,Yes,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, HTML/CSS, Spreadsheets (e.g. Excel)",No,I am not sure how to appropriately cite software,"Laptop / Desktop computer, GPU" +"Observational (Space-Based), Observational (Ground-Based)",Graduate student,Finland,Yes,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Spreadsheets (e.g. Excel), Github (or similar)",Yes,,"Laptop / Desktop computer, GPU" +Observational (Space-Based),Graduate student,United Kingdom,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"Python, SunPy, Shell scripting, SQL, Github (or similar)",Yes,,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations","Faculty, Staff Scientist, Researcher",Norway,,,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"IDL, SolarSoft, Python, SunPy, Shell scripting, C, Fortran, Julia, HTML/CSS, MPI, Github (or similar)",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster, Regional or National Cluster" +Numerical Simulations,Graduate student,Norway,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Python, Shell scripting, C, C++, Fortran, Github (or similar)",No,,"Laptop / Desktop computer, Shared workstation, Local Cluster, Regional or National Cluster" +Observational (Ground-Based),Postdoc,Norway,,,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Shell scripting, Fortran, MPI, Github (or similar)",Yes,,"Laptop / Desktop computer, Local Cluster, Regional or National Cluster" +Observational (Space-Based),Software developer,Norway,,,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"IDL, SolarSoft",Yes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Numerical Simulations",Graduate student,United Kingdom,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"IDL, SolarSoft, Python, SunPy, Fortran, Spreadsheets (e.g. Excel), Github (or similar)",Yes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based)","Faculty, Staff Scientist, Researcher",Norway,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, Shell scripting",Sometimes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations","Faculty, Staff Scientist, Researcher",Norway,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Python, Shell scripting, C, C++, Fortran, HTML/CSS, MPI, Github (or similar)",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster, Regional or National Cluster, GPU" +"Observational (Ground-Based), Numerical Simulations",Postdoc,Norway,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"Python, SunPy, Fortran, Julia, MATLAB, Spreadsheets (e.g. Excel), Mathematica, MPI, Github (or similar), OMP",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +Numerical Simulations,Graduate student,Norway,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"Python, Fortran, Github (or similar)",No,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations",Postdoc,Norway,No,,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft",Sometimes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Instrumentation",Software developer,Norway,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Shell scripting, C, C++, Perl, Javascript, SQL, HTML/CSS, Github (or similar)",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Instrumentation",Graduate student,Norway,Yes,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"Python, SunPy, MATLAB, Github (or similar)",No,I am not sure how to appropriately cite software,"Laptop / Desktop computer, Shared workstation, Local Cluster" +Observational (Space-Based),Graduate student,United Kingdom,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"IDL, SolarSoft, Python, SunPy, Github (or similar)",No,,Laptop / Desktop computer +"Numerical Simulations, Theory",Graduate student,United Kingdom,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I mostly use software written by others.,"IDL, Fortran, Perl, MPI, Github (or similar)",No,I am not sure how to appropriately cite software,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based), Instrumentation",Postdoc,Brazil,Yes,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Shell scripting, Github (or similar)",Yes,,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based), Theory",Graduate student,Costa Rica,,,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I mostly use software written by others.,"IDL, SolarSoft, Python, SunPy, MATLAB, Spreadsheets (e.g. Excel), Github (or similar)",Sometimes,,"Laptop / Desktop computer, Local Cluster, Commercial cloud" +Theory,"Faculty, Staff Scientist, Researcher",Spain,No,No,Yes,No,I write mostly my own software.,"IDL, Python, Fortran",Yes,,"Laptop / Desktop computer, Shared workstation" +"Observational (Space-Based), Numerical Simulations","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Shell scripting, C, Spreadsheets (e.g. Excel), Github (or similar)",Yes,,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based)","Faculty, Staff Scientist, Researcher",United States,,,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, Python, C, Fortran, IRAF, Javascript, MATLAB, Spreadsheets (e.g. Excel), Github (or similar)",Sometimes,,"Laptop / Desktop computer, Local Cluster" +Numerical Simulations,Postdoc,Mexico,Yes,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,Fortran,Sometimes,,Laptop / Desktop computer +"Observational (Space-Based), Theory","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Shell scripting, Javascript, MATLAB, SQL, Github (or similar)",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster, GPU" +"Observational (Space-Based), Numerical Simulations",Postdoc,Japan,Yes,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"IDL, SolarSoft, Python, SunPy, Shell scripting, Spreadsheets (e.g. Excel), OpenCV, Scipy and other Python Packages",No,I am not sure how to appropriately cite software,"Laptop / Desktop computer, Shared workstation" +"Observational (Space-Based), Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",United Kingdom,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"IDL, SolarSoft, Python, Shell scripting, Fortran, Spreadsheets (e.g. Excel), Maple",Sometimes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Space-Based), Numerical Simulations, Theory",Postdoc,Japan,No,No,Yes,No,Somewhere in between.,"Python, C++, Fortran, MPI, Github (or similar)",Sometimes,,"Laptop / Desktop computer, Shared workstation" +"Observational (Space-Based), Numerical Simulations",Graduate student,Japan,,,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"IDL, SolarSoft, Python, SunPy, Fortran",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster, GPU" +"Observational (Space-Based), Observational (Ground-Based)","Faculty, Staff Scientist, Researcher",Japan,No,No,Yes,No,I mostly use software written by others.,"IDL, SolarSoft, Python, Shell scripting, HTML/CSS, Spreadsheets (e.g. Excel), CASA",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Space-Based), Numerical Simulations","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, Shell scripting, Fortran, Javascript, HTML/CSS, ANA",No,I am not sure how to appropriately cite software,"Laptop / Desktop computer, Local Cluster" +Observational (Space-Based),Postdoc,India,No,No,Yes,No,Somewhere in between.,"IDL, SolarSoft, Python, Julia, R",No,I do not think it is necessary,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Theory",Postdoc,Japan,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"IDL, SolarSoft, Python, Shell scripting, Fortran, Javascript, HTML/CSS, Spreadsheets (e.g. Excel), MPI, Github (or similar)",No,I am not sure how to appropriately cite software,"Laptop / Desktop computer, Shared workstation, Regional or National Cluster" +Numerical Simulations,"Faculty, Staff Scientist, Researcher",Japan,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"IDL, Python, SunPy, Fortran, MPI, Github (or similar)",Sometimes,,"Laptop / Desktop computer, Shared workstation, Regional or National Cluster" +Observational (Space-Based),"Faculty, Staff Scientist, Researcher",Japan,Yes,No,Yes,No,Somewhere in between.,"IDL, SolarSoft",Yes,,"Laptop / Desktop computer, Shared workstation" +"Observational (Space-Based), Observational (Ground-Based)",Postdoc,United Kingdom,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Shell scripting, MATLAB, SQL, HTML/CSS, Github (or similar)",Yes,,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations",Undergraduate student,India,,,Yes,"Yes, a little (e.g. online classes, books, workshops)",I mostly use software written by others.,"IDL, SolarSoft, Python, SunPy, C, C++, Fortran",No,I am not sure how to appropriately cite software,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",Japan,Yes,Yes,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"IDL, Python, SunPy, Shell scripting, Fortran, MATLAB, HTML/CSS",Sometimes,,"Laptop / Desktop computer, Shared workstation, Local Cluster, Regional or National Cluster" +"Observational (Space-Based), Observational (Ground-Based)",Graduate student,Japan,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python",No,I do not think it is necessary,"Laptop / Desktop computer, Shared workstation" +"Observational (Space-Based), Theory, Instrumentation",Postdoc,China,Yes,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy",Yes,,Laptop / Desktop computer +Numerical Simulations,Graduate student,United Kingdom,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I mostly use software written by others.,"IDL, Fortran, MATLAB, Maple",Yes,,"Laptop / Desktop computer, Local Cluster" +Instrumentation,Hobbyist,United States,,,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, Python, SunPy, Shell scripting, C, Perl, R, Git (github is a proprietary social networking platform, git is the software)",No,I do not think it is necessary,"Laptop / Desktop computer, GPU" +"Observational (Space-Based), Numerical Simulations, Theory",Postdoc,United Kingdom,,,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"Python, Fortran, MATLAB",Yes,,"Laptop / Desktop computer, Local Cluster, GPU" +Observational (Space-Based),Postdoc,Ireland,No,Yes,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"Python, SunPy, R, SQL, Github (or similar)",Sometimes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based)",Graduate student,Austria,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"Python, SunPy, Github (or similar)",Yes,,"Laptop / Desktop computer, Local Cluster" +Numerical Simulations,Postdoc,United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, Shell scripting, C++, Fortran, Mathematica",Sometimes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based)",My role is something other than solar physics or software development,Japan,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,HTML/CSS,Sometimes,,Shared workstation +"Observational (Ground-Based), Instrumentation","Faculty, Staff Scientist, Researcher",Belgium,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"Python, C++, Fortran, SQL",No,I do not think it is necessary,Laptop / Desktop computer +"Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",United Kingdom,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, Python, Fortran, HTML/CSS, Maple",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster, GPU" +"Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",Spain,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I mostly use software written by others.,"IDL, Fortran, Mathematica",Sometimes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Theory, Instrumentation","Faculty, Staff Scientist, Researcher",Germany,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, Shell scripting, C, Fortran",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster, Regional or National Cluster" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",Romania,,,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, Shell scripting, C, Fortran, SQL, Spreadsheets (e.g. Excel), MPI",Yes,,"Laptop / Desktop computer, Local Cluster" +Observational (Ground-Based),Graduate student,Germany,No,No,Yes,No,I write mostly my own software.,"IDL, Python, SunPy",No,I do not think it is necessary,"Laptop / Desktop computer, Shared workstation, Local Cluster" +Observational (Space-Based),Postdoc,China,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft",Sometimes,,"Laptop / Desktop computer, Shared workstation" +"Observational (Space-Based), Numerical Simulations","Faculty, Staff Scientist, Researcher",France,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Shell scripting, C++, Fortran, SQL, HTML/CSS, MPI, Github (or similar)",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster, Regional or National Cluster, GPU" +Observational (Ground-Based),Graduate student,Germany,Yes,,Yes,No,Somewhere in between.,"IDL, Python",No,I am not sure how to appropriately cite software,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Numerical Simulations","Faculty, Staff Scientist, Researcher",Austria,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"IDL, SolarSoft, Python, SunPy, Shell scripting, Spreadsheets (e.g. Excel), Github (or similar)",Yes,,"Laptop / Desktop computer, Shared workstation" +Numerical Simulations,Graduate student,Brazil,,,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"Python, C++",Yes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Numerical Simulations",Graduate student,India,,,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Fortran, MATLAB, MPI",Sometimes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based)",Software developer,Switzerland,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"IDL, SolarSoft, C, Javascript, Java, R, SQL, HTML/CSS, Spreadsheets (e.g. Excel), Github (or similar), C# & .NET",Yes,,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations",Graduate student,Sweden,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"Python, SunPy, MATLAB, Spreadsheets (e.g. Excel), Mathematica",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations",Retired,Netherlands,No,No,Yes,No,I write mostly my own software.,"IDL, SolarSoft, Shell scripting",Yes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Numerical Simulations",Graduate student,South Korea,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"IDL, SolarSoft, Python, SunPy, C",No,I do not think it is necessary,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Instrumentation","Faculty, Staff Scientist, Researcher",Italy,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, Python, SunPy, Shell scripting, Fortran",Sometimes,,"Laptop / Desktop computer, Local Cluster" +Observational (Space-Based),Graduate student,United Kingdom,No,No,No,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"Python, SunPy, HTML/CSS, Github (or similar)",No,I do not think software belongs in citations,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Instrumentation","Faculty, Staff Scientist, Researcher",Belgium,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,IDL,No,I am not sure how to appropriately cite software,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based), Instrumentation","Faculty, Staff Scientist, Researcher",France,No,No,Yes,No,I write mostly my own software.,"Python, MATLAB",Sometimes,,Laptop / Desktop computer +Observational (Space-Based),Graduate student,United States,Yes,No,Yes,No,Somewhere in between.,"IDL, SolarSoft, Python",No,I am not sure how to appropriately cite software,Laptop / Desktop computer +"Observational (Space-Based), Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"MATLAB, Mathematica",Sometimes,,Laptop / Desktop computer +"Observational (Space-Based), Numerical Simulations","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Shell scripting, HTML/CSS, Spreadsheets (e.g. Excel), Github (or similar)",Sometimes,,"Laptop / Desktop computer, Shared workstation" +"Observational (Space-Based), Numerical Simulations","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,No,Somewhere in between.,"IDL, Python, Fortran, Github (or similar)",Yes,,Shared workstation +Observational (Space-Based),"Faculty, Staff Scientist, Researcher",Germany,,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft",No,I am not sure how to appropriately cite software,Laptop / Desktop computer +"Observational (Space-Based), Instrumentation","Faculty, Staff Scientist, Researcher",United States,,,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Shell scripting, C++, Perl, MATLAB, R, Spreadsheets (e.g. Excel), Github (or similar)",No,I am not sure how to appropriately cite software,"Laptop / Desktop computer, Shared workstation" +"Observational (Space-Based), Observational (Ground-Based), Instrumentation",Instrument developer,United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, Shell scripting, Java, Spreadsheets (e.g. Excel), Mathematica, Github (or similar)",Sometimes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +Observational (Space-Based),Graduate student,United Kingdom,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"Python, SunPy, Spreadsheets (e.g. Excel)",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster, Commercial cloud" +"Observational (Space-Based), Instrumentation","Faculty, Staff Scientist, Researcher",Brazil,Yes,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, C, IRAF",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based)","Faculty, Staff Scientist, Researcher",United States,Yes,Yes,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"Python, SunPy, Spreadsheets (e.g. Excel), Github (or similar)",No,I am not sure how to appropriately cite software,Laptop / Desktop computer +"Observational (Space-Based), Instrumentation","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Shell scripting, C, Perl, HTML/CSS, Spreadsheets (e.g. Excel)",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Theory, Instrumentation",Postdoc,United States,Yes,,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, Shell scripting, R, Spreadsheets (e.g. Excel), Github (or similar)",Sometimes,,"Laptop / Desktop computer, Shared workstation" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"IDL, Fortran, HTML/CSS",Yes,,"Laptop / Desktop computer, Shared workstation" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations",Postdoc,United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, HTML/CSS, Spreadsheets (e.g. Excel), Github (or similar)",Yes,,"Laptop / Desktop computer, Shared workstation" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",United Kingdom,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, Shell scripting, Fortran",Sometimes,,Shared workstation +"Observational (Space-Based), Observational (Ground-Based), Instrumentation","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,No,Somewhere in between.,"IDL, SolarSoft, Python, Fortran, Java, Spreadsheets (e.g. Excel), Github (or similar)",Yes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"Python, SunPy, C, Fortran, Perl, MPI, Github (or similar)",Yes,,"Laptop / Desktop computer, Local Cluster, GPU" +"Observational (Space-Based), Observational (Ground-Based)","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"IDL, SolarSoft, Python, Shell scripting",Yes,,"Laptop / Desktop computer, Local Cluster, GPU" +"Observational (Space-Based), Numerical Simulations","Faculty, Staff Scientist, Researcher",United States,No,Yes,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"IDL, Python, Fortran, MATLAB, R, HTML/CSS",Sometimes,,Laptop / Desktop computer +Observational (Space-Based),"Faculty, Staff Scientist, Researcher",United States,No,Yes,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, HTML/CSS",No,I am not sure how to appropriately cite software,Laptop / Desktop computer +Numerical Simulations,"Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, C, C++, Github (or similar)",Yes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Instrumentation",Instrument developer,United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Python, Shell scripting, C, C++, Perl, Javascript, Java, SQL, HTML/CSS, Spreadsheets (e.g. Excel), Github (or similar)",No,I am not sure how to appropriately cite software,"Laptop / Desktop computer, Shared workstation" +"Numerical Simulations, Theory",Postdoc,Spain,Yes,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"IDL, Shell scripting, Fortran, MPI, Github (or similar)",Yes,,"Laptop / Desktop computer, Local Cluster, Regional or National Cluster" +"Observational (Space-Based), Numerical Simulations, Instrumentation","Faculty, Staff Scientist, Researcher",United States,Yes,Yes,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, MATLAB, Spreadsheets (e.g. Excel)",Sometimes,,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based)","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"Python, C, Perl, MATLAB, HTML/CSS, Spreadsheets (e.g. Excel), Github (or similar)",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Instrumentation",Postdoc,Germany,,,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, Spreadsheets (e.g. Excel)",Yes,,"Laptop / Desktop computer, Local Cluster" +Observational (Ground-Based),Graduate student,United States,No,Yes,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"Python, SunPy",No,I am not sure how to appropriately cite software,Laptop / Desktop computer +Numerical Simulations,Graduate student,United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"Python, MPI",No,I am not sure how to appropriately cite software,"Laptop / Desktop computer, Local Cluster" +Observational (Space-Based),"Faculty, Staff Scientist, Researcher",United States,Yes,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"Python, SunPy, Shell scripting, HTML/CSS",Yes,,Laptop / Desktop computer +Numerical Simulations,Postdoc,Germany,,,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, Shell scripting, Fortran, MATLAB, MPI, Github (or similar)",Sometimes,,"Laptop / Desktop computer, Shared workstation, Local Cluster, Regional or National Cluster" +"Observational (Space-Based), Observational (Ground-Based)",Graduate student,Japan,,,Yes,No,Somewhere in between.,"IDL, SolarSoft, Python, SunPy, C, Spreadsheets (e.g. Excel)",No,I do not think it is necessary,"Laptop / Desktop computer, Shared workstation, GPU" +"Observational (Space-Based), Numerical Simulations","Faculty, Staff Scientist, Researcher",Italy,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"IDL, SolarSoft, Python",Sometimes,,"Laptop / Desktop computer, Shared workstation" +"Observational (Space-Based), Numerical Simulations, Theory",Graduate student,Spain,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"IDL, Python",No,I do not think software belongs in citations,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based), Instrumentation","Faculty, Staff Scientist, Researcher",United Kingdom,No,No,Yes,No,I write mostly my own software.,"IDL, SolarSoft",Sometimes,,"Laptop / Desktop computer, Shared workstation, Local Cluster, Regional or National Cluster" +"Observational (Ground-Based), Instrumentation",Software developer,United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I mostly use software written by others.,"Python, SunPy, Spreadsheets (e.g. Excel)",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster, GPU" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",United Kingdom,,,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, SunPy, Fortran, MATLAB, HTML/CSS, Github (or similar)",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster, Regional or National Cluster, GPU" +"Observational (Space-Based), Numerical Simulations","Faculty, Staff Scientist, Researcher",India,,,Yes,No,Somewhere in between.,"IDL, SolarSoft",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations","Faculty, Staff Scientist, Researcher",India,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"MATLAB, Spreadsheets (e.g. Excel), Github (or similar)",Sometimes,,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Spreadsheets (e.g. Excel), Github (or similar)",No,I do not think software belongs in citations,Laptop / Desktop computer +"Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",India,Yes,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I mostly use software written by others.,MATLAB,Sometimes,,Laptop / Desktop computer +"Observational (Ground-Based), Numerical Simulations",Graduate student,India,No,No,Yes,No,Somewhere in between.,"IDL, SolarSoft, Fortran",No,I am not sure how to appropriately cite software,Laptop / Desktop computer +"Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",Germany,No,No,Yes,No,I write mostly my own software.,"IDL, SolarSoft, Python, SunPy, Shell scripting, C, C++, Fortran, MPI, Github (or similar)",Sometimes,,"Laptop / Desktop computer, Shared workstation, Local Cluster, Regional or National Cluster" +"Observational (Space-Based), Observational (Ground-Based)","Faculty, Staff Scientist, Researcher",Italy,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft",Sometimes,,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based)",Graduate student,India,,,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Python, C, IRAF, Spreadsheets (e.g. Excel), Github (or similar)",Sometimes,,Laptop / Desktop computer +"Observational (Space-Based), Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",India,,,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, MATLAB, Spreadsheets (e.g. Excel), Github (or similar)",Yes,,Laptop / Desktop computer +"Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",France,No,No,Yes,No,Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Shell scripting, C, C++, Fortran, MPI, Github (or similar)",Sometimes,,"Laptop / Desktop computer, Local Cluster, Regional or National Cluster" +"Observational (Space-Based), Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",Belgium,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Shell scripting, C++, Fortran, Spreadsheets (e.g. Excel), MPI, Github (or similar), maple",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster, Regional or National Cluster" +Observational (Space-Based),"Faculty, Staff Scientist, Researcher",Taiwan,Yes,,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, C, Fortran",Sometimes,,Laptop / Desktop computer +Theory,Graduate student,India,,,Yes,No,I write mostly my own software.,Mathematica,Yes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Instrumentation",Graduate student,Switzerland,Yes,Yes,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Spreadsheets (e.g. Excel), MPI, Github (or similar)",No,I am not sure how to appropriately cite software,"Laptop / Desktop computer, Local Cluster" +"Numerical Simulations, Theory",Graduate student,India,,,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Shell scripting, Fortran, Mathematica, MPI, Github (or similar)",Sometimes,,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations",Graduate student,Japan,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Fortran",No,I do not think software belongs in citations,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Space-Based), Theory","Part time PhD student in Computational Astrophysics, with a full time job as a software developer",India,Yes,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"Python, SunPy, Spreadsheets (e.g. Excel), Github (or similar)",No,,Laptop / Desktop computer +"Observational (Space-Based), Theory","Faculty, Staff Scientist, Researcher",Germany,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"Python, Shell scripting, C, Fortran, MATLAB, Mathematica, MPI",Sometimes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +Observational (Space-Based),Graduate student,India,,,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Spreadsheets (e.g. Excel)",Yes,,"Laptop / Desktop computer, Local Cluster" +Observational (Ground-Based),"Faculty, Staff Scientist, Researcher",India,No,No,Yes,No,I write mostly my own software.,"IDL, SolarSoft, C, C++",Yes,,Laptop / Desktop computer +Observational (Space-Based),Ph.D,India,Yes,Yes,Yes,No,Somewhere in between.,"IDL, SolarSoft",Sometimes,,Laptop / Desktop computer +Observational (Space-Based),"Faculty, Staff Scientist, Researcher",India,Yes,Yes,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Fortran",Yes,,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Theory, Instrumentation","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, Shell scripting, Fortran, Javascript, R, SQL, MPI, Github (or similar)",Yes,,"Laptop / Desktop computer, Local Cluster, Regional or National Cluster, GPU" +Numerical Simulations,Graduate student,Hungary,Yes,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, Python, SunPy, C++, Fortran, MATLAB, Mathematica",Sometimes,,Laptop / Desktop computer +"Observational (Ground-Based), Instrumentation","Faculty, Staff Scientist, Researcher",United Kingdom,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, Shell scripting, Fortran, Spreadsheets (e.g. Excel), Github (or similar)",Sometimes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Instrumentation","Faculty, Staff Scientist, Researcher",Slovakia,,,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft",Yes,,Shared workstation +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Theory",Graduate student,United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"Python, SunPy, Shell scripting, Fortran, Julia, MATLAB, Spreadsheets (e.g. Excel), MPI, Github (or similar)",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster, GPU" +"Observational (Space-Based), Theory",Postdoc,United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"IDL, Python",Yes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based)","Faculty, Staff Scientist, Researcher",India,,,No,No,Somewhere in between.,Spreadsheets (e.g. Excel),No,I do not think it is necessary,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based)","Faculty, Staff Scientist, Researcher",India,Yes,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I mostly use software written by others.,"IDL, SolarSoft, MATLAB",Sometimes,,Laptop / Desktop computer +Theory,Graduate student,India,,,No,No,Somewhere in between.,MATLAB,No,I am not sure how to appropriately cite software,Laptop / Desktop computer +"Numerical Simulations, Theory",Graduate student,United Kingdom,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"Python, Fortran, MATLAB, HTML/CSS, MPI, Github (or similar)",Sometimes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations",Postdoc,Switzerland,,,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"IDL, SolarSoft, Python, SunPy, Fortran, Github (or similar)",Yes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations","Faculty, Staff Scientist, Researcher",India,,,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"MATLAB, Spreadsheets (e.g. Excel)",Yes,,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based), Instrumentation","Faculty, Staff Scientist, Researcher",United States,No,Yes,Yes,No,I mostly use software written by others.,"IDL, SolarSoft, Python, Java",Sometimes,,"Laptop / Desktop computer, Shared workstation" +"Observational (Space-Based), Observational (Ground-Based)",Graduate student,Taiwan,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I mostly use software written by others.,"IDL, SolarSoft, Shell scripting",Yes,,"Laptop / Desktop computer, Shared workstation" +"Observational (Space-Based), Numerical Simulations, Theory",Graduate student,Japan,,,Yes,No,Somewhere in between.,"IDL, SolarSoft, Fortran",Sometimes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based)",Postdoc,India,,,Yes,"Yes, a little (e.g. online classes, books, workshops)",I mostly use software written by others.,"IDL, SolarSoft",Yes,,"Laptop / Desktop computer, Local Cluster" +Theory,"Faculty, Staff Scientist, Researcher",India,Yes,Yes,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,MATLAB,No,I do not think software belongs in citations,Laptop / Desktop computer +"Numerical Simulations, Theory",Postdoc,United Arab Emirates,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"Python, Shell scripting, C, Fortran, Julia, Mathematica, MPI, Github (or similar)",No,I do not think software belongs in citations,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Ground-Based), Numerical Simulations","Faculty, Staff Scientist, Researcher",Sweden,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, C++, Fortran, MPI, Github (or similar)",No,I am not sure how to appropriately cite software,"Laptop / Desktop computer, Shared workstation, Local Cluster, Regional or National Cluster" +"Observational (Space-Based), Observational (Ground-Based), Theory","Faculty, Staff Scientist, Researcher",Greece,No,No,Yes,No,I write mostly my own software.,"IDL, SolarSoft, Fortran",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Instrumentation","Faculty, Staff Scientist, Researcher",Indonesia,No,No,Yes,No,Somewhere in between.,"IDL, SolarSoft, SunPy, C, IRAF, Spreadsheets (e.g. Excel), Mathematica, Github (or similar)",Sometimes,,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations","Faculty, Staff Scientist, Researcher",Indonesia,,,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"SolarSoft, Python, SunPy, MATLAB",Sometimes,,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations",Postdoc,Hungary,,,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, Fortran, MATLAB",Sometimes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based)",Graduate student,India,,,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, MATLAB, Java, Spreadsheets (e.g. Excel)",No,I am not sure how to appropriately cite software,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based)","Faculty, Staff Scientist, Researcher",Japan,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I mostly use software written by others.,"IDL, SolarSoft",No,I do not think it is necessary,"Laptop / Desktop computer, Shared workstation" +"Observational (Ground-Based), Numerical Simulations",Graduate student,United Kingdom,Yes,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Shell scripting, C, MPI, Github (or similar)",Yes,,"Laptop / Desktop computer, Local Cluster" +Observational (Ground-Based),Graduate student,Brazil,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"Python, SunPy, Shell scripting, Mathematica",Sometimes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based)","Faculty, Staff Scientist, Researcher",Japan,Yes,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I mostly use software written by others.,"IDL, SolarSoft",No,I am not sure how to appropriately cite software,Shared workstation +"Observational (Space-Based), Numerical Simulations",Graduate student,United Kingdom,No,No,Yes,No,I write mostly my own software.,"IDL, SolarSoft, Shell scripting",No,I do not think it is necessary,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Theory, Instrumentation","Faculty, Staff Scientist, Researcher",China,,,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Shell scripting, Fortran, IRAF, Javascript, Java, SQL, HTML/CSS, Spreadsheets (e.g. Excel), Github (or similar)",Yes,,"Laptop / Desktop computer, Shared workstation, Commercial cloud" +"Observational (Space-Based), Instrumentation",Retired,United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Shell scripting, Javascript, HTML/CSS, Github (or similar)",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Space-Based), Numerical Simulations, Instrumentation","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Python, Shell scripting, Julia, Spreadsheets (e.g. Excel), Github (or similar)",Yes,,"Laptop / Desktop computer, Local Cluster, Commercial cloud" +"Observational (Space-Based), Numerical Simulations","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Shell scripting, C++, Fortran, HTML/CSS, Mathematica, MPI, Github (or similar)",Sometimes,,"Laptop / Desktop computer, Local Cluster, Regional or National Cluster, Commercial cloud" +Observational (Space-Based),Postdoc,United States,No,,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft",Yes,,"Laptop / Desktop computer, Shared workstation" +"Observational (Space-Based), Instrumentation",Software developer,United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Shell scripting, Javascript, Java, SQL, HTML/CSS, Spreadsheets (e.g. Excel), Github (or similar)",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +Observational (Space-Based),"Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft",Sometimes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +Theory,"Faculty, Staff Scientist, Researcher",India,,,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"C, Fortran, R, Mathematica",Sometimes,,Local Cluster +"Observational (Space-Based), Instrumentation","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, Python, Shell scripting, C, Fortran, Perl, SQL, HTML/CSS, MPI",Sometimes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based)",Retired,United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"C, R, HTML/CSS, Spreadsheets (e.g. Excel), COBOL",No,I do not think software belongs in citations,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based)","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"Shell scripting, C, SQL",No,I do not think it is necessary,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Numerical Simulations","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"IDL, SolarSoft, Python, Shell scripting, Fortran, MPI, Github (or similar)",Sometimes,,"Laptop / Desktop computer, Local Cluster, Regional or National Cluster" +"Observational (Space-Based), Observational (Ground-Based), Instrumentation","Faculty, Staff Scientist, Researcher",United States,Yes,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Shell scripting, C++",Sometimes,,Laptop / Desktop computer +Observational (Space-Based),"Faculty, Staff Scientist, Researcher",United States,No,No,Yes,No,Somewhere in between.,"IDL, SolarSoft, C++, Fortran, Perl",No,I do not think it is necessary,"Laptop / Desktop computer, Shared workstation" +"Numerical Simulations, Theory",Graduate student,Germany,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"IDL, Python, Fortran, HTML/CSS, Spreadsheets (e.g. Excel), Github (or similar)",No,I do not think it is necessary,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Instrumentation","Faculty, Staff Scientist, Researcher",Germany,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"IDL, SolarSoft, Python, SunPy, Fortran, MPI, Github (or similar)",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Space-Based), Instrumentation",Graduate student,Germany,No,Yes,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"Python, Shell scripting, Github (or similar)",No,I am not sure how to appropriately cite software,Laptop / Desktop computer +Numerical Simulations,Postdoc,Germany,,,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"Python, Shell scripting, C++, MATLAB",Sometimes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Instrumentation",Instrument developer,Germany,,,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, Python, Spreadsheets (e.g. Excel), Mathematica, Github (or similar), ZEMAX, FreeCAD",No,I am not sure how to appropriately cite software,"Laptop / Desktop computer, Shared workstation" +Observational (Space-Based),Graduate student,Germany,No,No,Yes,No,I write mostly my own software.,"Python, SunPy, Shell scripting, Spreadsheets (e.g. Excel)",No,I am not sure how to appropriately cite software,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Space-Based), Numerical Simulations, Theory",Graduate student,Germany,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"Python, Mathematica",No,I am not sure how to appropriately cite software,Local Cluster +Theory,"Faculty, Staff Scientist, Researcher",Germany,No,Yes,Yes,No,Somewhere in between.,"IDL, SolarSoft",No,I am not sure how to appropriately cite software,"Shared workstation, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Theory",unemployed,Germany,,,Yes,No,I write mostly my own software.,"IDL, Python, C, Fortran, MATLAB, Mathematica",No,I do not think software belongs in citations,"Laptop / Desktop computer, Shared workstation" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Theory",Graduate student,Germany,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Shell scripting, Fortran, Spreadsheets (e.g. Excel), MPI, Github (or similar)",No,I am not sure how to appropriately cite software,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Instrumentation",Postdoc,Spain,Yes,Yes,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Github (or similar)",Yes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Theory",Graduate student,Germany,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Shell scripting, Spreadsheets (e.g. Excel), Mathematica",Sometimes,,"Laptop / Desktop computer, Shared workstation, Local Cluster, Commercial cloud" +Numerical Simulations,Graduate student,Germany,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"Python, MATLAB",No,I do not think it is necessary,Local Cluster +"Observational (Space-Based), Numerical Simulations, Theory",Postdoc,Germany,No,No,Yes,No,I write mostly my own software.,"IDL, Python, C++, Mathematica",Sometimes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations","Faculty, Staff Scientist, Researcher",Indonesia,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"Fortran, Spreadsheets (e.g. Excel)",Sometimes,,Laptop / Desktop computer +"Numerical Simulations, Theory",Undergraduate student,Germany,,,Yes,No,Somewhere in between.,R,Yes,,"Shared workstation, Local Cluster" +Instrumentation,Instrument developer,Germany,,,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"Python, Spreadsheets (e.g. Excel)",No,,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Theory, Instrumentation",Graduate student,Germany,,,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"Python, C++, Fortran",No,I am not sure how to appropriately cite software,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,No,Somewhere in between.,"IDL, C++, Fortran, Spreadsheets (e.g. Excel), MPI, Github (or similar)",Yes,,"Laptop / Desktop computer, Regional or National Cluster" +Numerical Simulations,"Faculty, Staff Scientist, Researcher",Germany,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I mostly use software written by others.,"IDL, Python, Fortran, MATLAB, Spreadsheets (e.g. Excel), Github (or similar)",Yes,,"Laptop / Desktop computer, Shared workstation, Regional or National Cluster" +"Numerical Simulations, Theory",Graduate student,United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, Fortran",Sometimes,,Regional or National Cluster +Theory,Postdoc,United States,,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I mostly use software written by others.,"Python, C, Fortran, MATLAB, Spreadsheets (e.g. Excel), Mathematica",No,I do not think it is necessary,Laptop / Desktop computer +"Numerical Simulations, Theory",Graduate student,United States,Yes,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, C, Fortran, MATLAB, Mathematica",No,I do not think it is necessary,"Laptop / Desktop computer, Regional or National Cluster" +"Observational (Space-Based), Instrumentation","Faculty, Staff Scientist, Researcher",Germany,No,No,Yes,No,Somewhere in between.,"IDL, SolarSoft, Spreadsheets (e.g. Excel)",Sometimes,,"Laptop / Desktop computer, Shared workstation" +"Numerical Simulations, Theory",Graduate student,United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"Python, C++, MATLAB, MPI, LuaJIT",Yes,,"Laptop / Desktop computer, Local Cluster, Regional or National Cluster" +"Observational (Space-Based), Observational (Ground-Based), Instrumentation","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,No,Somewhere in between.,"IDL, SolarSoft",Yes,,Laptop / Desktop computer +"Observational (Space-Based), Instrumentation",Postdoc,United States,Yes,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"Python, MATLAB, Spreadsheets (e.g. Excel), Mathematica",No,I do not think it is necessary,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based), Theory, Instrumentation","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,No,Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Mathematica, Github (or similar)",Yes,,Laptop / Desktop computer +"Observational (Space-Based), Numerical Simulations, Instrumentation","Faculty, Staff Scientist, Researcher",United States,Yes,Yes,Yes,No,Somewhere in between.,"IDL, Python, Shell scripting, MPI",Yes,,"Laptop / Desktop computer, Local Cluster" +Numerical Simulations,"Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"IDL, SolarSoft, Python, Shell scripting, Fortran, Perl, MPI, Github (or similar)",No,I do not think it is necessary,"Laptop / Desktop computer, Regional or National Cluster" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, Shell scripting, C, Fortran, Perl, MATLAB, SQL, HTML/CSS, Spreadsheets (e.g. Excel), MPI, Github (or similar), OpenMP",No,I do not think it is necessary,"Laptop / Desktop computer, Local Cluster, Regional or National Cluster" +"Observational (Space-Based), Instrumentation","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Spreadsheets (e.g. Excel), Github (or similar)",Yes,,"Laptop / Desktop computer, Shared workstation" +Observational (Space-Based),"Faculty, Staff Scientist, Researcher",United States,No,,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"Python, Shell scripting, Javascript, SQL, Spreadsheets (e.g. Excel), Github (or similar)",No,,"Laptop / Desktop computer, Local Cluster" +"Numerical Simulations, Theory",Postdoc,Germany,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"Python, Shell scripting, Fortran, MPI, Github (or similar)",No,I am not sure how to appropriately cite software,"Laptop / Desktop computer, Local Cluster" +Theory,Undergraduate student,Germany,No,No,Yes,No,Somewhere in between.,"IDL, Python, Shell scripting",No,,"Laptop / Desktop computer, Shared workstation" +"Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,No,I write mostly my own software.,"IDL, Shell scripting, Fortran, Mathematica, MPI, Github (or similar)",No,I do not think it is necessary,"Laptop / Desktop computer, Regional or National Cluster" +"Observational (Space-Based), Instrumentation","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,Python,Yes,,"Laptop / Desktop computer, Shared workstation" +Observational (Space-Based),"Faculty, Staff Scientist, Researcher",United States,Yes,Yes,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Github (or similar)",Sometimes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +Instrumentation,Instrument developer,Switzerland,,,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"IDL, Python, Shell scripting, C, Java",No,I do not think software belongs in citations,"Laptop / Desktop computer, Shared workstation" +"Observational (Space-Based), Instrumentation","Faculty, Staff Scientist, Researcher",Belgium,No,No,Yes,No,I write mostly my own software.,"IDL, SolarSoft, Python, SunPy, Shell scripting, C, C++, Javascript, HTML/CSS, Github (or similar), PhP",Sometimes,,Laptop / Desktop computer +Observational (Space-Based),Graduate student,Austria,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"IDL, SolarSoft, Python, SunPy, Shell scripting, C, C++, MPI",Sometimes,,"Shared workstation, Local Cluster" +"Observational (Space-Based), Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",France,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Shell scripting, Fortran, SQL, HTML/CSS, Spreadsheets (e.g. Excel), MPI, Github (or similar)",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster, GPU" +"Observational (Space-Based), Observational (Ground-Based)","Faculty, Staff Scientist, Researcher",Belgium,No,No,Yes,No,I mostly use software written by others.,"Javascript, Spreadsheets (e.g. Excel)",Sometimes,,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based)",Software developer,Belgium,Yes,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"IDL, SolarSoft, Python, SunPy, Shell scripting, C++, Javascript, SQL, HTML/CSS, Github (or similar)",Yes,,"Laptop / Desktop computer, Local Cluster" +Observational (Space-Based),"Faculty, Staff Scientist, Researcher",Belgium,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"Python, SunPy, C++, R",Sometimes,,"Laptop / Desktop computer, Local Cluster" +Observational (Space-Based),"Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"C, Perl, SQL, Spreadsheets (e.g. Excel), Github (or similar)",Yes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"Python, SunPy, Javascript, MATLAB, HTML/CSS, Github (or similar)",Yes,,"Laptop / Desktop computer, Shared workstation" +"Observational (Space-Based), Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"Python, SunPy, C, Fortran, Perl, HTML/CSS, MPI, Github (or similar)",Yes,,"Laptop / Desktop computer, Local Cluster, GPU" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Instrumentation","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"SolarSoft, Python, SunPy, Shell scripting, C, Perl, SQL, HTML/CSS, Spreadsheets (e.g. Excel), Github (or similar)",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +Observational (Space-Based),"Faculty, Staff Scientist, Researcher",United States,No,Yes,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, HTML/CSS",Sometimes,,Laptop / Desktop computer +"Observational (Space-Based), Instrumentation","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"IDL, SolarSoft, Shell scripting",Sometimes,,Laptop / Desktop computer +Observational (Space-Based),Graduate student,United States,,,Yes,No,I write mostly my own software.,"IDL, Python, Fortran, Spreadsheets (e.g. Excel), Github (or similar)",No,I do not think software belongs in citations,Laptop / Desktop computer +"Observational (Space-Based), Numerical Simulations","Faculty, Staff Scientist, Researcher",United States,No,Yes,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Fortran, Spreadsheets (e.g. Excel), Github (or similar)",No,I do not think software belongs in citations,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"IDL, Python, SunPy, Shell scripting, C, C++, Fortran, Perl, Javascript, Java, SQL, HTML/CSS, Spreadsheets (e.g. Excel), MPI, Github (or similar)",Yes,,"Laptop / Desktop computer, Local Cluster, Regional or National Cluster" +Observational (Space-Based),"Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,MATLAB,No,I do not think it is necessary,Laptop / Desktop computer +"Observational (Space-Based), Instrumentation",Software developer,United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I mostly use software written by others.,"IDL, SolarSoft, Spreadsheets (e.g. Excel)",No,I do not think it is necessary,Laptop / Desktop computer +"Observational (Space-Based), Instrumentation","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I mostly use software written by others.,"IDL, SolarSoft, Spreadsheets (e.g. Excel)",Sometimes,,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations",Postdoc,United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, HTML/CSS, Spreadsheets (e.g. Excel), Github (or similar)",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Space-Based), Numerical Simulations, Theory, Instrumentation","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,No,I write mostly my own software.,"IDL, Shell scripting, Fortran, Mathematica, Github (or similar)",Yes,,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,No,Somewhere in between.,"IDL, SolarSoft, Fortran, Spreadsheets (e.g. Excel)",Sometimes,,"Laptop / Desktop computer, Local Cluster, Regional or National Cluster" +"Observational (Space-Based), Numerical Simulations, Instrumentation","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,No,Somewhere in between.,"IDL, SolarSoft, HTML/CSS, Spreadsheets (e.g. Excel)",Yes,,Laptop / Desktop computer +Observational (Space-Based),"Faculty, Staff Scientist, Researcher",United States,No,No,Yes,No,I write mostly my own software.,C,No,I am not sure how to appropriately cite software,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Instrumentation",Postdoc,United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"IDL, Python, Shell scripting, C, C++, Fortran, MATLAB, Spreadsheets (e.g. Excel), MPI, Github (or similar)",No,I am not sure how to appropriately cite software,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Theory, Instrumentation","Faculty, Staff Scientist, Researcher",United States,Yes,Yes,Yes,No,Somewhere in between.,"IDL, SolarSoft, MATLAB",Sometimes,,Laptop / Desktop computer +"Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"Python, Shell scripting, C, C++, Fortran, Perl, Julia, MATLAB, R, SQL, MPI, Github (or similar)",Sometimes,,"Laptop / Desktop computer, Shared workstation, Local Cluster, Regional or National Cluster, GPU" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Theory",1 year PhD student,Belgium,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Shell scripting, Spreadsheets (e.g. Excel)",Sometimes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Instrumentation",Software developer,United States,Yes,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, Python, Shell scripting, Javascript, SQL, HTML/CSS, Spreadsheets (e.g. Excel), Github (or similar)",Yes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,No,Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Shell scripting, MATLAB, Github (or similar)",Yes,,"Laptop / Desktop computer, Shared workstation, GPU" +"Observational (Space-Based), Observational (Ground-Based), Instrumentation","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,No,I write mostly my own software.,IDL,No,I do not think it is necessary,Laptop / Desktop computer +Instrumentation,"Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"IDL, Python, C, C++, Julia, HTML/CSS, Github (or similar)",Yes,,"Laptop / Desktop computer, Shared workstation" +"Observational (Space-Based), Observational (Ground-Based), Theory, Instrumentation","Faculty, Staff Scientist, Researcher",United States,No,Yes,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Spreadsheets (e.g. Excel)",Yes,,"Laptop / Desktop computer, Shared workstation" +"Observational (Space-Based), Observational (Ground-Based)","Faculty, Staff Scientist, Researcher",United States,No,,Yes,No,Somewhere in between.,"IDL, SolarSoft, C, Fortran, MATLAB",Sometimes,,Laptop / Desktop computer +"Observational (Ground-Based), Numerical Simulations, Instrumentation","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,No,Somewhere in between.,"IDL, Python, SunPy, MATLAB, Spreadsheets (e.g. Excel), Mathematica, Zemax, Autodesk Inventor / Solidworks, TFCalc",Sometimes,,"Laptop / Desktop computer, Shared workstation, Local Cluster, Regional or National Cluster" +"Observational (Ground-Based), Theory","Faculty, Staff Scientist, Researcher",United States,,,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Fortran, IRAF",Yes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Theory","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,No,I write mostly my own software.,"IDL, Fortran, HTML/CSS, Mathematica",Yes,,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based)","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,No,I write mostly my own software.,IDL,Sometimes,,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based), Instrumentation","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, Fortran, IRAF, Spreadsheets (e.g. Excel)",Yes,,"Laptop / Desktop computer, Shared workstation" +Instrumentation,"Faculty, Staff Scientist, Researcher",United States,,,Yes,No,I write mostly my own software.,"C++, MATLAB, Octave",Sometimes,,"Laptop / Desktop computer, Shared workstation" +"Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"Python, HTML/CSS, Spreadsheets (e.g. Excel), MPI, Github (or similar)",Yes,,"Laptop / Desktop computer, Regional or National Cluster, Commercial cloud" +"Observational (Ground-Based), Instrumentation","Faculty, Staff Scientist, Researcher",United States,No,Yes,Yes,"Yes, a little (e.g. online classes, books, workshops)",I mostly use software written by others.,"IDL, SolarSoft, Javascript, Spreadsheets (e.g. Excel)",Sometimes,,"Laptop / Desktop computer, Local Cluster" +Observational (Ground-Based),"Faculty, Staff Scientist, Researcher",United States,Yes,Yes,Yes,No,I write mostly my own software.,"IDL, Shell scripting, HTML/CSS",No,I am not sure how to appropriately cite software,"Laptop / Desktop computer, Shared workstation" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,No,Somewhere in between.,"IDL, SolarSoft, Python, C++, Fortran, IRAF",Yes,,"Laptop / Desktop computer, Shared workstation" +"Observational (Space-Based), Instrumentation","Faculty, Staff Scientist, Researcher",United States,,,Yes,No,I write mostly my own software.,"IDL, Python",Yes,,Shared workstation +"Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",United States,Yes,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, Python, Shell scripting, HTML/CSS, Spreadsheets (e.g. Excel), Mathematica, MPI",No,I am not sure how to appropriately cite software,"Laptop / Desktop computer, Regional or National Cluster" +Observational (Space-Based),"Faculty, Staff Scientist, Researcher",Belgium,,,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft",Sometimes,,"Shared workstation, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Theory",Undergraduate student,Argentina,,,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Shell scripting, Spreadsheets (e.g. Excel), Github (or similar)",No,I am not sure how to appropriately cite software,"Laptop / Desktop computer, Shared workstation" +"Observational (Space-Based), Observational (Ground-Based), Instrumentation",Postdoc,Argentina,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"IDL, SolarSoft, Python, C, C++, Spreadsheets (e.g. Excel), Mathematica, Github (or similar)",Sometimes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations",Graduate student,India,,,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Python, C, C++",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based)",Graduate student,Austria,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"Python, SunPy",Yes,,Laptop / Desktop computer +"Observational (Space-Based), Numerical Simulations",Postdoc,France,,,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"IDL, SolarSoft, MATLAB",Sometimes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Theory","Faculty, Staff Scientist, Researcher",India,,,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"Python, Spreadsheets (e.g. Excel), origin",Sometimes,,Laptop / Desktop computer +Observational (Space-Based),Graduate student,Ethiopia,,,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,MATLAB,Yes,,Laptop / Desktop computer +Numerical Simulations,Software developer,Russia,Yes,Yes,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"IDL, SolarSoft, Fortran",Yes,,"Laptop / Desktop computer, GPU" +"Observational (Space-Based), Observational (Ground-Based)","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft",Sometimes,,Laptop / Desktop computer +"Observational (Space-Based), Numerical Simulations","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Shell scripting, Fortran, Perl, MPI, Github (or similar)",Sometimes,,"Laptop / Desktop computer, Shared workstation, Local Cluster, Regional or National Cluster, GPU" +Observational (Space-Based),Postdoc,United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I mostly use software written by others.,"IDL, SolarSoft, Python, SunPy",Yes,,Laptop / Desktop computer +"Observational (Space-Based), Instrumentation",Software developer,United States,,,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Shell scripting, Javascript, Java, SQL, HTML/CSS",Yes,,"Laptop / Desktop computer, Local Cluster" +Observational (Space-Based),"SolarSoft, part time retiree.",United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Shell scripting, Javascript, HTML/CSS, Github (or similar)",Yes,,"Laptop / Desktop computer, Local Cluster, Commercial cloud" +Observational (Space-Based),Postdoc,United States,No,Yes,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Github (or similar)",Yes,,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based), Instrumentation","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"IDL, SolarSoft, Python, SunPy, Shell scripting, SQL, HTML/CSS, Spreadsheets (e.g. Excel), Github (or similar)",Sometimes,,"Laptop / Desktop computer, Shared workstation, Commercial cloud" +"Numerical Simulations, Theory",Graduate student,United States,Yes,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"Python, Shell scripting, Spreadsheets (e.g. Excel), MPI, Github (or similar)",Yes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Instrumentation","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Github (or similar)",Sometimes,,"Laptop / Desktop computer, Local Cluster, Commercial cloud" +"Observational (Space-Based), Observational (Ground-Based)",Postdoc,Latvia,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"IDL, SolarSoft, MATLAB, Spreadsheets (e.g. Excel)",Yes,,Laptop / Desktop computer +"Observational (Space-Based), Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",France,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"IDL, SolarSoft, Python, SunPy, Shell scripting, C++, Fortran, SQL, HTML/CSS, Spreadsheets (e.g. Excel), MPI, Github (or similar)",Yes,,"Laptop / Desktop computer, Local Cluster, Regional or National Cluster, GPU" +"Observational (Space-Based), Observational (Ground-Based), Instrumentation","Faculty, Staff Scientist, Researcher",United Kingdom,No,No,Yes,No,Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Shell scripting",Yes,,"Laptop / Desktop computer, Local Cluster" +"Numerical Simulations, Theory",Postdoc,Spain,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, Python, SunPy, Shell scripting, C, C++, Fortran, HTML/CSS, Mathematica, MPI, Github (or similar)",Sometimes,,"Laptop / Desktop computer, Local Cluster, Regional or National Cluster" +"Observational (Space-Based), Observational (Ground-Based)","Faculty, Staff Scientist, Researcher",United Kingdom,No,Yes,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy",Yes,,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",Spain,No,No,Yes,No,Somewhere in between.,"IDL, SolarSoft, Python, Fortran, Spreadsheets (e.g. Excel), Mathematica",Sometimes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations",Graduate student,Austria,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"IDL, SolarSoft, Python, SunPy, Shell scripting, Spreadsheets (e.g. Excel), MPI, Github (or similar)",Sometimes,,"Laptop / Desktop computer, Shared workstation, Local Cluster, Regional or National Cluster" +"Observational (Ground-Based), Instrumentation","Faculty, Staff Scientist, Researcher",Sweden,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"IDL, SolarSoft, Python, SQL",Yes,,"Laptop / Desktop computer, Local Cluster" +"Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",France,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"SolarSoft, Python, SunPy, C, Fortran, HTML/CSS, MPI, Github (or similar)",Yes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Numerical Simulations, Theory",Postdoc,Germany,No,No,Yes,No,Somewhere in between.,Python,Yes,,Local Cluster +"Observational (Space-Based), Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",Germany,Yes,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Shell scripting, C, C++, Fortran, MATLAB, R, SQL, Spreadsheets (e.g. Excel), MPI, Github (or similar)",Sometimes,,"Laptop / Desktop computer, Shared workstation, Local Cluster, Regional or National Cluster, GPU" +"Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",Belgium,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Shell scripting, C++, Fortran, HTML/CSS, Spreadsheets (e.g. Excel), maple",Sometimes,,"Laptop / Desktop computer, Shared workstation, Local Cluster, Regional or National Cluster, GPU" +"Observational (Space-Based), Observational (Ground-Based)","Faculty, Staff Scientist, Researcher",Sweden,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"IDL, SolarSoft, Python, SunPy, Shell scripting, Perl, HTML/CSS, Github (or similar)",Yes,,"Laptop / Desktop computer, Local Cluster, Regional or National Cluster" +"Observational (Space-Based), Observational (Ground-Based)","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft",No,I am not sure how to appropriately cite software,"Shared workstation, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based)","Faculty, Staff Scientist, Researcher",Finland,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I mostly use software written by others.,"IDL, SolarSoft, HTML/CSS",Sometimes,,"Laptop / Desktop computer, Shared workstation" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations","Faculty, Staff Scientist, Researcher",Czech Republic,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Shell scripting, Fortran",Sometimes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Instrumentation","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, R",Sometimes,,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"IDL, SolarSoft, MATLAB, Mathematica",Yes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Numerical Simulations, Instrumentation","Faculty, Staff Scientist, Researcher",United States,No,Yes,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"IDL, SolarSoft, Python, SunPy, Shell scripting",Yes,,Laptop / Desktop computer +"Observational (Space-Based), Instrumentation","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"IDL, Python, SunPy, MATLAB, SQL, Spreadsheets (e.g. Excel)",Yes,,Laptop / Desktop computer +"Observational (Space-Based), Numerical Simulations","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Shell scripting, HTML/CSS, Spreadsheets (e.g. Excel), Github (or similar)",Sometimes,,Laptop / Desktop computer +"Observational (Ground-Based), Instrumentation","Faculty, Staff Scientist, Researcher",United States,,,Yes,No,Somewhere in between.,"C++, MATLAB",Sometimes,,"Laptop / Desktop computer, Shared workstation" +Observational (Space-Based),partially retired,United States,,,Yes,No,Somewhere in between.,"IDL, SolarSoft, Python, Github (or similar)",Sometimes,,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based)","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,No,I write mostly my own software.,"Python, Shell scripting, COBOL",No,I do not think it is necessary,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",United States,No,,Yes,No,I write mostly my own software.,"IDL, Fortran",Yes,,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based), Instrumentation","Faculty, Staff Scientist, Researcher",United States,,,Yes,"Yes, a little (e.g. online classes, books, workshops)",I mostly use software written by others.,"IDL, SolarSoft, Python, SunPy",Yes,,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I mostly use software written by others.,"IDL, Python, SunPy, Shell scripting, C++, Fortran, Perl, MATLAB, SQL, HTML/CSS, MPI, Github (or similar)",Yes,,"Laptop / Desktop computer, Local Cluster, GPU" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations",Postdoc,United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, C++, HTML/CSS, Github (or similar)",Yes,,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations",Postdoc,United States,No,,Yes,"Yes, a little (e.g. online classes, books, workshops)",I mostly use software written by others.,"IDL, SolarSoft",Yes,,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Shell scripting, C, Fortran, Spreadsheets (e.g. Excel)",Yes,,"Laptop / Desktop computer, Shared workstation" +Numerical Simulations,"Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I mostly use software written by others.,"Python, C++, Fortran, MPI, Github (or similar)",No,I do not think software belongs in citations,"Laptop / Desktop computer, Regional or National Cluster" +"Observational (Space-Based), Numerical Simulations, Theory, Instrumentation","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Python, Shell scripting, MATLAB, HTML/CSS, Spreadsheets (e.g. Excel), Github (or similar)",Sometimes,,"Laptop / Desktop computer, Shared workstation" +"Observational (Ground-Based), Numerical Simulations",Graduate student,United States,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"Python, Shell scripting",No,I am not sure how to appropriately cite software,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"IDL, SolarSoft, Python, SunPy, Shell scripting, C, Fortran, MPI, Github (or similar)",No,,"Laptop / Desktop computer, Shared workstation, Regional or National Cluster" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Theory",Retired,United States,No,No,Yes,No,Somewhere in between.,"IDL, Mathematica",No,I do not think software belongs in citations,Laptop / Desktop computer +Theory,"Faculty, Staff Scientist, Researcher",Spain,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"IDL, Python",Yes,,Laptop / Desktop computer +Observational (Space-Based),"Retired, but still doing research at a University",United States,No,No,Yes,No,Somewhere in between.,"IDL, SolarSoft, Fortran, Spreadsheets (e.g. Excel)",No,I am not sure how to appropriately cite software,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations",Postdoc,Hungary,Yes,Yes,Yes,"Yes, a little (e.g. online classes, books, workshops)",I mostly use software written by others.,"IDL, Python",Yes,,"Laptop / Desktop computer, Shared workstation" +"Observational (Space-Based), Observational (Ground-Based)","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"IDL, Python, Shell scripting, C, C++, Fortran, Perl, Javascript, Julia, MATLAB, Java, R, SQL, HTML/CSS, Spreadsheets (e.g. Excel), MPI, Github (or similar)",No,I do not think it is necessary,"Laptop / Desktop computer, Shared workstation, Local Cluster, Regional or National Cluster, GPU" +Observational (Space-Based),"Faculty, Staff Scientist, Researcher",Germany,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I mostly use software written by others.,"IDL, SolarSoft, Shell scripting, HTML/CSS",Sometimes,,Laptop / Desktop computer +Theory,Graduate student,United Kingdom,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"Python, SunPy, MATLAB, Github (or similar)",Yes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",Russia,,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, Spreadsheets (e.g. Excel)",Sometimes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Space-Based), Numerical Simulations",Postdoc,Belgium,No,,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, C, C++",Sometimes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Instrumentation","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I mostly use software written by others.,"IDL, SolarSoft, Python, SunPy, Shell scripting, C, HTML/CSS, Spreadsheets (e.g. Excel)",No,I am not sure how to appropriately cite software,"Laptop / Desktop computer, Shared workstation, GPU, Commercial cloud" +"Observational (Space-Based), Numerical Simulations",Graduate student,Belgium,,,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"Python, Shell scripting, Fortran, Github (or similar)",Yes,,Laptop / Desktop computer +"Numerical Simulations, Theory",Graduate student,Belgium,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"IDL, Python, Shell scripting, Fortran, MATLAB",Sometimes,,"Laptop / Desktop computer, Local Cluster" +Theory,Graduate student,United Kingdom,No,Yes,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"Python, SunPy, Maple",No,I do not think it is necessary,Laptop / Desktop computer +"Observational (Space-Based), Instrumentation","Faculty, Staff Scientist, Researcher",United States,Yes,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Shell scripting, C, C++, Javascript, HTML/CSS, Spreadsheets (e.g. Excel), Github (or similar)",Sometimes,,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based)",Postdoc,United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft",Yes,,Laptop / Desktop computer +"Observational (Space-Based), Observational (Ground-Based)",Software developer,Switzerland,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"IDL, Python, C++, MATLAB, R, SQL, HTML/CSS",Sometimes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +Numerical Simulations,"Faculty, Staff Scientist, Researcher",Mexico,Yes,Yes,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"C++, Fortran, MPI",Sometimes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Theory",Graduate student,United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Shell scripting, Fortran, Mathematica, MPI, Github (or similar)",Sometimes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Theory, Instrumentation",Postdoc,India,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Shell scripting, Javascript, MATLAB, HTML/CSS, Spreadsheets (e.g. Excel), Github (or similar)",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Space-Based), Numerical Simulations","Faculty, Staff Scientist, Researcher",United Kingdom,No,No,Yes,No,Somewhere in between.,"IDL, Python, Fortran, MATLAB",Sometimes,,"Laptop / Desktop computer, Shared workstation, Regional or National Cluster" +Theory,"Faculty, Staff Scientist, Researcher",United Kingdom,No,No,Yes,No,Somewhere in between.,"Python, MATLAB, Mathematica",Yes,,Laptop / Desktop computer +"Observational (Space-Based), Numerical Simulations",Postdoc,Finland,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Shell scripting, Fortran, Spreadsheets (e.g. Excel), MPI, Github (or similar)",Sometimes,,"Laptop / Desktop computer, Local Cluster, Regional or National Cluster" +"Observational (Space-Based), Observational (Ground-Based)","Faculty, Staff Scientist, Researcher",United Kingdom,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"IDL, SolarSoft, Python",Sometimes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Space-Based), Numerical Simulations, Theory",Graduate student,United States,No,No,Yes,No,I mostly use software written by others.,"IDL, SolarSoft, Python, SunPy, R",Sometimes,,Laptop / Desktop computer +Observational (Space-Based),Undergraduate student,United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"Python, SunPy, Spreadsheets (e.g. Excel), Github (or similar)",No,,Laptop / Desktop computer +"Observational (Space-Based), Numerical Simulations",Postdoc,United Kingdom,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"Python, SunPy, Shell scripting, Github (or similar)",Yes,,Laptop / Desktop computer +"Numerical Simulations, Theory","Faculty, Staff Scientist, Researcher",United Kingdom,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"Python, C, C++, Fortran, MATLAB, Mathematica",Sometimes,,"Laptop / Desktop computer, Local Cluster" +"Observational (Space-Based), Observational (Ground-Based), Numerical Simulations, Theory, Instrumentation","Faculty, Staff Scientist, Researcher",United Kingdom,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",I write mostly my own software.,"IDL, SolarSoft, Python, SunPy, Shell scripting, Fortran, MPI",Yes,,"Laptop / Desktop computer, Local Cluster, Regional or National Cluster" +"Numerical Simulations, Theory",Recently completed PhD but now working in industry,United Kingdom,No,No,Yes,"Yes, a lot (e.g. CS courses at an undergraduate or graduate level)",Somewhere in between.,"Python, Fortran, MATLAB, HTML/CSS, MPI, Github (or similar)",Sometimes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" +"Observational (Space-Based), Instrumentation",Postdoc,United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Shell scripting, MATLAB, HTML/CSS, Spreadsheets (e.g. Excel), Github (or similar)",Yes,,"Laptop / Desktop computer, Local Cluster, Commercial cloud" +"Observational (Space-Based), Observational (Ground-Based)",Undergrad student and working in IT company as DevOps,Serbia,,,Yes,"Yes, a little (e.g. online classes, books, workshops)",I write mostly my own software.,"IDL, SolarSoft, Python, SunPy, Shell scripting, Github (or similar)",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster, Commercial cloud" +"Observational (Space-Based), Observational (Ground-Based), Instrumentation","Faculty, Staff Scientist, Researcher",United States,No,No,Yes,"Yes, a little (e.g. online classes, books, workshops)",Somewhere in between.,"IDL, SolarSoft, Python, SunPy, Shell scripting, Fortran, Perl, Javascript, SQL, HTML/CSS, Spreadsheets (e.g. Excel), Github (or similar)",Yes,,"Laptop / Desktop computer, Shared workstation, Local Cluster" diff --git a/CITATION b/CITATION new file mode 100644 index 0000000..e674317 --- /dev/null +++ b/CITATION @@ -0,0 +1,2 @@ +The results of the survey will be submitted as a paper to *Solar Physics*; if it is accepted, we will update this file with the associated BibTex entry. In the meantime, please cite these results using the BibTex entry of the most recent Zenodo deposit of this repository: + diff --git a/README.md b/README.md index 2dc2004..49e66b6 100644 --- a/README.md +++ b/README.md @@ -1 +1,15 @@ -# survey \ No newline at end of file +# A Survey of Computational Tools in Solar Physics + +Between February 7, 2019 and July 28, 2019, The SunPy Project opened a 13-question survey to understand the software and hardware usage of the solar physics community. The survey was similar to one conducted by Ivelina Momcheva and Erik Tollerud in 2015, who surveyed 1142 astronomers about [software use in astrophysics](https://arxiv.org/abs/1507.03989). + +This repository contains the raw data from the survey (in `raw_survey_responses_no_comments.csv` and `free_form_comments.csv`) alongside the source code to analyze these data (`computational_tools_in_solar_physics.ipynb`). + +### Citation + +The results of the survey will be submitted as a paper to *Solar Physics*; if it is accepted, we will update this file with the associated BibTex entry. In the meantime, please cite these results using the BibTex entry of the most recent Zenodo deposit of this repository: + +``` + +``` + +