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      "body": "Hello, thanks for commenting. You nailed it with 'not a popular destination'. Also it's inexpensive over there. Once I got arrived I immediately fell in love. The people are so dignified, the food is so interesting (horse stew, anyone? How about some camel milk?) and of course the history is amazing. \n\nThere is a lot of good Korean food in Mongolia too. I could go on and on. Let me know if you've got any other questions and thank you again for stopping by.",
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      "body": "Apparently it was an awesome ride, I think.  But why Mongolia actually?  This is not a popular destination",
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      "author": "deeallen",
      "permlink": "is-it-open-global-market-status-at-a-glance-with-python",
      "title": "IS IT OPEN? Global Market Status at-a-Glance with Python",
      "body": "![Screen Shot 2022-06-04 at 2.55.54 PM.png](https://cdn.steemitimages.com/DQmYTYUCqPJU6MiGWbpa3kDpLZXLytMgKVxX43WQkqckVGk/Screen%20Shot%202022-06-04%20at%202.55.54%20PM.png)\n\n\n\n\n# The price of Bitcoin tends to correlate with stock market indices, so the market hours of stock exchanges should be factored into some trading strategies. IS IT OPEN? is obviously an important question in that arena.\n<p>&nbsp;</p>\n\n\n\n*Below: NASDAQ 100 and BTC in a tight correlation on Friday, June 3rd 2022. Image captured by author.*\n![TNJJqjXH.png](https://cdn.steemitimages.com/DQmTNAFXCVQ6BrvcNrBVYyMa3qaD1jvUruHvX9MMephiBot/TNJJqjXH.png)\n\n\n# This post shares a Python tool I've been working on. I use it to consolidate and display the open/closed statuses of several large stock exchanges. I find it useful reference when I'm trading.  \n\n<p>&nbsp;</p>\n\n*Below: Example output of tool. Blue-filled states represent open-status of stock markets in or near state.  Image captured by author.*\n![Screen Shot 2022-06-04 at 2.38.04 PM.png](https://cdn.steemitimages.com/DQmbpyJtcBrbDTS4qVGZz1zsQDA4evG4MgEWjZcn8q4EvTC/Screen%20Shot%202022-06-04%20at%202.38.04%20PM.png)\n\n\n# I hope at least a few people here on Steemit find this tool useful. Please let me know what you think, and thanks in advance.\n<p>&nbsp;</p>\n\n\n## Introduction\n\nI've used the [World Stock Markets map](https://www.worldtimezone.com/markets24.php) on [worldtimezone.com](worldtimezone.com) for years to get a consolidated view of global market statuses. Lately though, I've wanted a more intuitive view of this information, so I decided to build a similar tool for my own use.\n\nBelow you'll find my Python code, pertinent links, and instructions for how to build it. \n\n*Below: [World Stock Markets map](https://www.worldtimezone.com/markets24.php) on [worldtimezone.com](worldtimezone.com), my long-time resource for global market statuses at-a-glance. Image captured by author Friday, June 3rd 2022.*\n\n    \n![Screen Shot 2022-06-03 at 8.05.42 AM.png](https://cdn.steemitimages.com/DQmPD9qkFa9i3dx6o9kDX6dZEtcWeZnNjs4TDnhNfQ18YCA/Screen%20Shot%202022-06-03%20at%208.05.42%20AM.png)  \n\n\n## Description & Prospectus  \n\nThis tool delivers the same services as the [World Stock Markets map](https://www.worldtimezone.com/markets24.php). That is, a map with data tables detailing major global markets’ open/closed hours and statuses. I find my tool a little more intuitive to read at-a-glance than www.worldtimezone.com (and easier on the eyes), even now in its rough, unfinished state. \n\n#### UNFINISHED\nIt is unfinished in that I still need to add the data for market holiday closures, which will take a little bit of effort for sure. I also intend to add data for some additional markets such as Singapore and Israel. Further down the line I would also like to visually integrate pre-post trading hours into the system for all applicable exchanges, and maybe, *maybe* put labels on the map - although I like how clean it looks as an unlabelled, [binary qualitative progression choropleth map](http://wiki.gis.com/wiki/index.php/Choropleth_map) \n\nSince everything below is just Python code at this point, you can simply copy it and use it for your own purposes. If you carry the work forward in the same direction as above, maybe we could collaborate on the project? Let me know. \n\n#### DISCLAIMER\n\nPlease note that like all tools of this kind, constant maintenance will be required in order for the data presented to stay up to date and accurate. \n\n## Methods\nWritten in [Google Colab's](https://colab.research.google.com/) free-tiered [Jupyter Notebook](https://jupyter.org). See the complete public notebook in Colab and on my Github.\n\n- [Open in Colab](https://colab.research.google.com/drive/1e85NwHhP2-wpIWbmkMIPaNvdSMlgrx93#scrollTo=S_q6K2PCuHhJ) \n\n- [Open in Github](https://github.com/dualcircle/NOTEBOOKS/blob/main/IS_IT_OPEN%3F_Global_Market_Status_at_a_Glance_with_Python.ipynb)\n\n\n#### CODE\n### Begin by installing and importing packages as applicable.\n\n\n```\n!pip install geopandas\nimport geopandas as gpd\nimport matplotlib.pyplot as plt\nplt.style.use('dark_background')\nimport datetime\nfrom datetime import datetime\nimport pytz\nimport pandas as pd\n\nutcmoment_naive = datetime.utcnow()\nutcmoment = utcmoment_naive.replace(tzinfo=pytz.utc)\n\n```\n<p>&nbsp;</p>\n\n### List the exchanges, their timezones, and their open/close hours. You might notice, lunch hours are included in own list, these will be integrated into the system at a later date, currently WIP.\n<p>&nbsp;</p>\n\n### These data are pulled from a variety of sources, most often [trading hours.com](https://www.tradinghours.com). If this tool ever really developed I'd want to pull the info right from the exchanges websites, as I've done for the [NZX exchange](https://www.nzx.com/services/nzx-trading/hours-boards). \n\n\n\n\n\n```\n\n\nT_ZNES = ['Asia/Seoul',\n 'Asia/Tokyo',\n 'Asia/Hong_Kong',\n 'Asia/Kolkata',\n 'Asia/Kolkata',\n 'Europe/Zurich',\n 'Europe/Zurich',\n 'Europe/Zurich',\n 'Europe/Zurich',\n 'Africa/Johannesburg',\n 'Europe/London',\n 'Australia/Sydney',\n 'America/Argentina/Buenos_Aires',\n 'America/New_York',\n 'America/New_York',\n 'America/Toronto',\n 'Asia/Riyadh',\n 'Asia/Shanghai',\n 'Asia/Shanghai',\n 'Asia/Shanghai',\n 'Pacific/Auckland']\n\n\nGLOBAL_XCHNGS=['Korea Exchange',\n'Tokyo Stock Exchange',\n'Stock Exchange of Hong Kong',\n'National Stock Exchange of India',\n'BSE Limited',\n'Frankfurt Stock Exchange',\n'SIX Swiss Exchange',\n'Euronext Amsterdam',\n'Nasdaq Stockholm AB',\n'Johannesburg Stock Exchange',\n'London Stock Exchange',\n'Australian Securities Exchange',\n'B3 S.A.',\n'New York Stock Exchange',\n'Nasdaq Stock Market',\n'Toronto Stock Exchange',\n'Saudi Stock Exchange',\n'Shanghai Stock Exchange',\n'Shenzhen Stock Exchange',\n'Taiwan Stock Exchange',\n'New Zealand Stock Exchange']\n\nOPENS=['9:00:00',\n'9:00:00',\n'9:30:00',\n'9:15:00',\n'9:15:00',\n'9:00:00',\n'9:00:00',\n'9:00:00',\n'9:00:00',\n'9:00:00',\n'8:00:00',\n'10:00:00',\n'10:00:00',\n'9:30:00',\n'9:30:00',\n'9:30:00',\n'10:00:00',\n'9:30:00',\n'9:30:00',\n'9:00:00',\n'10:00:00']\n\n\nCLOSES_OR_TAKES_LUNCH = ['15:30:00',\n '11:30:00',\n '12:00:00',\n '15:30:00',\n '15:30:00',\n '17:30:00',\n '17:20:00',\n '17:30:00',\n '17:25:00',\n '17:00:00',\n '12:00:00',\n '16:00:00',\n '17:55:00',\n '16:00:00',\n '16:00:00',\n '16:00:00',\n '15:00:00',\n '11:30:00',\n '11:30:00',\n '13:30:00',\n '16:45:00']\n\nREOPEN_AFTER_LUNCH=['9:00:00',\n'12:30:00',\n'13:00:00',\n'9:15:00',\n'9:15:00',\n'9:00:00',\n'9:00:00',\n'9:00:00',\n'9:00:00',\n'9:00:00',\n'12:02:00',\n'10:00:00',\n'10:00:00',\n'9:30:00',\n'9:30:00',\n'9:30:00',\n'10:00:00',\n'13:00:00',\n'13:00:00',\n'9:00:00',\n'10:00:00']\n\nTRUE_CLOSING_TIME = ['15:30:00',\n '15:00:00',\n '16:00:00',\n '15:30:00',\n '15:30:00',\n '17:30:00',\n '17:20:00',\n '17:30:00',\n '17:25:00',\n '17:00:00',\n '16:30:00',\n '16:00:00',\n '17:55:00',\n '16:00:00',\n '16:00:00',\n '16:00:00',\n '15:00:00',\n '15:00:00',\n '14:57:00',\n '13:30:00',\n '16:45:00']\n\n```\n<p>&nbsp;</p>\n\n### Create formatting for datetimes\n\n```\n# Format of local time\n\nLOCAL_TIME_FORMAT = \"%Y-%m-%d %H:%M:%S\"\n\n# Format of local time without date info\n\nLOCAL_TIME_FORMAT_NO_DATE = \"%H:%M:%S\"\n\n\n```\n<p>&nbsp;</p>\n\n### Combines local times accessed with the pytz package and then combine all the info into a table, seperate out some datetime elements for use momentarily.\n\n\n```\n\"\"\"# OPEN CLOSE 1\"\"\"\n\nFIRST_OPEN=pd.to_datetime(pd.Series(OPENS), format=\"%H:%M:%S\")\nFIRST_OPEN=FIRST_OPEN.dt.strftime(\"%H:%M:%S\")\n\n\nFIRST_CLOSE_OR_LUNCH=pd.to_datetime(pd.Series(CLOSES_OR_TAKES_LUNCH), format=\"%H:%M:%S\")\nFIRST_CLOSE_OR_LUNCH=FIRST_CLOSE_OR_LUNCH.dt.strftime(\"%H:%M:%S\")\n\n\n\"\"\"# OPEN CLOSE 2\"\"\"\n\nSECOND_OPEN_POST_LUNCH=pd.to_datetime(pd.Series(REOPEN_AFTER_LUNCH), format=\"%H:%M:%S\")\nSECOND_OPEN_POST_LUNCH=SECOND_OPEN_POST_LUNCH.dt.strftime(\"%H:%M:%S\")\n\n\nFINAL_CLOSING_TIME=pd.to_datetime(pd.Series(TRUE_CLOSING_TIME), format=\"%H:%M:%S\")\nFINAL_CLOSING_TIME=FINAL_CLOSING_TIME.dt.strftime(\"%H:%M:%S\")\n\n\"\"\"# LOCAL TIMES WITH DATE\"\"\"\n\nTIMELIST_0=['LOCAL_TIME_WITH_DATE']\n\nimport pandas as pd\nfor tz in T_ZNES:\n    localDatetime = utcmoment.astimezone(pytz.timezone(tz))\n    TIMELIST_0.append(str(localDatetime.strftime(LOCAL_TIME_FORMAT)))\n    #print(localDatetime.strftime(LOCAL_TIME_FORMAT))\nLOCAL_TIMES_WITH_DATE=pd.DataFrame(TIMELIST_0)[1:].reset_index()\nLOCAL_TIMES_WITH_DATE['LOCAL_TIME_WITH_DATE']=LOCAL_TIMES_WITH_DATE[0]\n\nLOCAL_TIMES_WITH_DATE\n\n\"\"\"# LOCAL TIMES WITHOUT DATE\"\"\"\n\nTIMELIST_1=['LOCAL_TIME_WITHOUT_DATE']\n\nimport pandas as pd\nfor tz in T_ZNES:\n    localDatetime = utcmoment.astimezone(pytz.timezone(tz))\n    TIMELIST_1.append(str(localDatetime.strftime(LOCAL_TIME_FORMAT_NO_DATE)))\n    #print(localDatetime.strftime(LOCAL_TIME_FORMAT))\nLOCAL_TIMES_WITHOUT_DATE=pd.DataFrame(TIMELIST_1)[1:].reset_index()\nLOCAL_TIMES_WITHOUT_DATE['LOCAL_TIME_WITHOUT_DATE']=LOCAL_TIMES_WITHOUT_DATE[0]\n\nLOCAL_TIMES_WITHOUT_DATE\n\n\"\"\"# EXGLOBAL_XCHNGS TO DF\"\"\"\n\nLOCAL_TIMES_WITH_DATE['LOCAL_TIME_WITHOUT_DATE']=LOCAL_TIMES_WITHOUT_DATE['LOCAL_TIME_WITHOUT_DATE']\nGLOBAL_XCHNGS=pd.Series(GLOBAL_XCHNGS)\nGLOBAL_XCHNGS=pd.DataFrame(GLOBAL_XCHNGS)\nGLOBAL_XCHNGS['GLOBAL_XCHNGS']=GLOBAL_XCHNGS[0]\nGLOBAL_XCHNGS\n\n\"\"\"# TIMEZONE KEYS TO DF\"\"\"\n\nZONES=pd.Series(T_ZNES)\nZONES=pd.DataFrame(ZONES)\nZONES\n\n\"\"\"# DF with: TIMEZONE KEYS, EXGLOBAL_XCHNGS, LOCAL TIMES WITH AND WITHOUT DATES\"\"\"\n\nZONES['ZONES']=ZONES[0]\nZONES['CHNGES']=GLOBAL_XCHNGS['GLOBAL_XCHNGS']\nZONES['LOCAL_TIME_WITH_DATE']=LOCAL_TIMES_WITH_DATE['LOCAL_TIME_WITH_DATE']\nZONES['LOCAL_TIME_WITHOUT_DATE']=LOCAL_TIMES_WITHOUT_DATE['LOCAL_TIME_WITHOUT_DATE']\nZONES['TIMESORT']=pd.DataFrame(pd.to_datetime(ZONES['LOCAL_TIME_WITH_DATE'], format=\"%Y-%m-%d %H:%M:%S\"))\nZONES=ZONES.drop([0, 'LOCAL_TIME_WITH_DATE'], axis=1)\nZONES\n\n\n\n\"\"\"# NEW COLUMNS FOR MONTH, DAY, DAY OF WEEK\"\"\"\n\nZONES['MONTH'] = ZONES['TIMESORT'].dt.month\n\nZONES['DAY'] = ZONES['TIMESORT'].dt.day\nZONES['DAYOFWEEK'] = ZONES['TIMESORT'].dt.dayofweek\n\n\"\"\"# ADD EXCHANGE OPEN AND CLOSE TO \"zone\" DATAFRAME\"\"\"\n\nZONES['FIRST_OPEN']=FIRST_OPEN\nZONES['FINAL_CLOSING_TIME']=FINAL_CLOSING_TIME\n\nZONES.index=ZONES['CHNGES']\n\nZONES.sort_values(by='TIMESORT')\n\n\n\n```\n<p>&nbsp;</p>\n\n### More data preparation next step, where we will crawl through to determine whether an exchange is open or closed at the moment.\n\n```\n\nSTAMPS=['STAMPS']\nfor i in range(len(ZONES)):\n  STAMPS.append(pd.Timestamp(ZONES['TIMESORT'].iloc[i]).timestamp())#.values.astype('int')\n\nCHECKR=pd.DataFrame(STAMPS[1:])\nCHECKR\n\nSTAMPS=['STAMPS']\nfor i in range(len(ZONES)):\n  STAMPS.append(pd.Timestamp(ZONES['TIMESORT'].iloc[i]).timestamp())#.values.astype('int')\n\nCHECKR=pd.DataFrame(STAMPS[1:])\nCHECKR\n\nSTAMPS=['opens']\nfor i in range(len(ZONES)):\n  STAMPS.append(pd.Timestamp(ZONES['FIRST_OPEN'].iloc[i]))#.values.astype('int')\nCHECKR['OPENRS']=pd.DataFrame(STAMPS[1:])\nCHECKR\n\nSTAMPS=['closrs']\nfor i in range(len(ZONES)):\n  STAMPS.append(pd.Timestamp(ZONES['FINAL_CLOSING_TIME'].iloc[i]))#.values.astype('int')\nCHECKR['CLOSRS']=pd.DataFrame(STAMPS[1:])\nCHECKR\n\nSTAMPS=['STAMPS']\nfor i in range(len(ZONES)):\n  STAMPS.append(pd.Timestamp(ZONES['TIMESORT'].iloc[i]))#.values.astype('int')\nCHECKR['CURRENT_TIME']=pd.DataFrame(STAMPS[1:])\nCHECKR\n\nCHECKR['OPEN_MONTH'] = CHECKR['OPENRS'].dt.month\nCHECKR['OPEN_DAY'] = CHECKR['OPENRS'].dt.day\nCHECKR['CLOSE_MONTH'] = CHECKR['CLOSRS'].dt.month\nCHECKR['CLOSE_DAY'] = CHECKR['CLOSRS'].dt.day\nCHECKR['NOW_MONTH'] = CHECKR['CURRENT_TIME'].dt.month\nCHECKR['NOW_DAY'] = CHECKR['CURRENT_TIME'].dt.day\nCHECKR\n\nCHECKR.index=ZONES.index\nCHECKR\n\nALREADYOPEN=['ALREADYOPEN']\nALREADYCLOSED=['ALREADYCLOSED']\n\n\n```\n<p>&nbsp;</p>\n\n### Use a 'for' loop to check and report open/closed status of markets, and to append the lists we just made to include open/closed = True/False flags. Plus, it delivers natural language summaries of the calculation steps and market open/closed status per exchange for human evaluation.\n\n\n```\nfor i in range(len(CHECKR)):\n  print(' ')\n  print('****', CHECKR.index[i], '****')\n  print(' ')\n  print(' ')\n  print('this is when it opens:', CHECKR['OPENRS'].iloc[i])\n  print('this is when it CLOSES_OR_TAKES_LUNCH:', CHECKR['CLOSRS'].iloc[i])\n  print('this is now:', CHECKR['CURRENT_TIME'].iloc[i])\n  if int(CHECKR['OPEN_MONTH'].iloc[i]) is int(CHECKR['NOW_MONTH'].iloc[i]):\n    print('same month')\n\n    print ('SO CHECK THE ****DAY****')\n\n    if int(CHECKR['OPEN_DAY'].iloc[i]) is int(CHECKR['NOW_DAY'].iloc[i]):\n      print('same day')\n\n      print('SO WAS IT ****ALREADY OPEN TODAY****? THE ANSWER IS:     ', CHECKR['OPENRS'][i]<CHECKR['CURRENT_TIME'][i])\n      ALREADYOPEN.append(CHECKR['OPENRS'][i]<CHECKR['CURRENT_TIME'][i])\n\n      print('AND WAS IT ****ALREADY CLOSED TODAY****? THE ANSWER IS:     ', CHECKR['CLOSRS'][i]<CHECKR['CURRENT_TIME'][i])\n      ALREADYCLOSED.append(CHECKR['CLOSRS'][i]<CHECKR['CURRENT_TIME'][i])\n\n    else:\n      print('diff day: SUBTRACT A **DAY** FROM NOW DAY')\n      print('      ')\n      print(CHECKR['CURRENT_TIME'].iloc[i])\n      print('THIS IS THE DATETIME WE USE IN THE CALCULATION: ', CHECKR['CURRENT_TIME'].iloc[i]-pd.Timedelta('1 day'))\n      print('      ')\n\n      print('SO WAS IT ****ALREADY OPEN TODAY****? THE ANSWER IS:     ', CHECKR['OPENRS'][i]<CHECKR['CURRENT_TIME'][i]-pd.Timedelta('1 day'))\n      ALREADYOPEN.append(CHECKR['OPENRS'][i]<CHECKR['CURRENT_TIME'][i]-pd.Timedelta('1 day'))\n\n      print('AND WAS IT ****ALREADY CLOSED TODAY****? THE ANSWER IS:     ', CHECKR['CLOSRS'][i]<CHECKR['CURRENT_TIME'][i]-pd.Timedelta('1 day'))\n      ALREADYCLOSED.append(CHECKR['CLOSRS'][i]<CHECKR['CURRENT_TIME'][i]-pd.Timedelta('1 day'))\n\n  else:\n    print('diff day: SUBTRACT A **DAY** FROM NOW DAY')\n    print('      ')\n    print(CHECKR['CURRENT_TIME'].iloc[i])\n    print('THIS IS THE DATETIME WE USE IN THE CALCULATION: ', CHECKR['CURRENT_TIME'].iloc[i]-pd.Timedelta('1 day'))\n    print('      ')\n\n    print('SO WAS IT ****ALREADY OPEN TODAY****? THE ANSWER IS:     ', CHECKR['OPENRS'][i]<CHECKR['CURRENT_TIME'][i]-pd.Timedelta('1 day'))\n    ALREADYOPEN.append(CHECKR['OPENRS'][i]<CHECKR['CURRENT_TIME'][i]-pd.Timedelta('1 day'))\n    print('AND WAS IT ****ALREADY CLOSED TODAY****? THE ANSWER IS:     ', CHECKR['CLOSRS'][i]<CHECKR['CURRENT_TIME'][i]-pd.Timedelta('1 day'))\n    ALREADYCLOSED.append(CHECKR['CLOSRS'][i]<CHECKR['CURRENT_TIME'][i]-pd.Timedelta('1 day'))\n\n\n\n\n```\n<p>&nbsp;</p>\n\n###  Add the new lists as columns. Was the exchnage already open today? Was it already closed? We now have our answers in the last two columns.\n```\nCHECKR['ALRDYOPEN']=ALREADYOPEN[1:]\nCHECKR['ALRDYCLOSE']=ALREADYCLOSED[1:]\nCHECKR.sort_values(by='CURRENT_TIME',  ascending=False)\n```\n<p>&nbsp;</p>\n\n### Next, use the open and closes statuses of markets to assign colors to the countries closest associated with the geographical locations of the exchanges. Blue means we are in open hours currently, black means we are outside market hours.\n\n```\nCHECKR['ALRDYOPEN']\n\nCOLORER=['colors']\nfor i in range(len(CHECKR)):\n  if str(CHECKR['ALRDYOPEN'][i]) is 'True':\n    if str(CHECKR['ALRDYCLOSE'][i]) is 'False':\n      COLORER.append('midnightblue')\n    else:\n      COLORER.append('k')\n  else:\n    COLORER.append('k')\n\n\n```\n<p>&nbsp;</p>\n\n### Finally, make and execute a function that puts the color on the map using Geopandas.\n\n```\ndef MAPPER():\n\n  WORLD = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n\n  ax2 = WORLD.plot(figsize=(20,30), edgecolor='k', color='darkslategrey')\n\n  WORLD[WORLD.name == \"South Korea\"].plot(edgecolor=u'white', color=COLORER[1], ax=ax2)\n  WORLD[WORLD.name == \"Japan\"].plot(edgecolor=u'white', color=COLORER[2], ax=ax2)\n  WORLD[WORLD.name == \"China\"].plot(edgecolor=u'white', color=COLORER[3], ax=ax2)\n  WORLD[WORLD.name == \"India\"].plot(edgecolor=u'white', color=COLORER[4], ax=ax2)\n  WORLD[WORLD.name == \"India\"].plot(edgecolor=u'white', color=COLORER[5], ax=ax2)\n  WORLD[WORLD.name == \"Germany\"].plot(edgecolor=u'white', color=COLORER[6], ax=ax2)\n  WORLD[WORLD.name == \"Switzerland\"].plot(edgecolor=u'white', color=COLORER[7], ax=ax2)\n  WORLD[WORLD.name == \"Netherlands\"].plot(edgecolor=u'white', color=COLORER[8], ax=ax2)\n  WORLD[WORLD.name == \"Sweden\"].plot(edgecolor=u'white', color=COLORER[9], ax=ax2)\n  WORLD[WORLD.name == \"South Africa\"].plot(edgecolor=u'white', color=COLORER[10], ax=ax2)\n  WORLD[WORLD.name == \"United Kingdom\"].plot(edgecolor=u'white', color=COLORER[11], ax=ax2)\n  WORLD[WORLD.name == \"Australia\"].plot(edgecolor=u'white', color=COLORER[12], ax=ax2)\n  WORLD[WORLD.name == \"Brazil\"].plot(edgecolor=u'white', color=COLORER[13], ax=ax2)\n  WORLD[WORLD.name == \"United States of America\"].plot(edgecolor='white', color=COLORER[14], ax=ax2)\n  WORLD[WORLD.name == \"United States of America\"].plot(edgecolor='white', color=COLORER[15], ax=ax2)\n  WORLD[WORLD.name == \"Canada\"].plot(edgecolor=u'white', color=COLORER[16], ax=ax2)\n  WORLD[WORLD.name == \"Saudi Arabia\"].plot(edgecolor=u'white', color=COLORER[17], ax=ax2)\n  WORLD[WORLD.name == \"China\"].plot(edgecolor=u'white', color=COLORER[18], ax=ax2)\n  WORLD[WORLD.name == \"China\"].plot(edgecolor=u'white', color=COLORER[19], ax=ax2)\n  WORLD[WORLD.name == \"Taiwan\"].plot(edgecolor=u'white', color=COLORER[20], ax=ax2)\n  WORLD[WORLD.name == \"New Zealand\"].plot(edgecolor=u'white', color=COLORER[21], ax=ax2)\n\n  #ax2.axis('off') \n  plt.show()\n\nMAPPER()\n\n\n```\n<p>&nbsp;</p>\n\n### This data table holds all the key info for reference including current local time, opening, and closing times.\n```\nCHECKR[CHECKR.columns[CHECKR.columns.isin(['OPENRS','CLOSRS', 'CURRENT_TIME', 'ALRDYOPEN' ,'ALRDYCLOSE'])]].sort_values(by='CURRENT_TIME', ascending=False).style.set_properties(**{'background-color': 'black',\n                           'color': 'white',\n                           'border-color': 'white'})\n```\n\n\n\n\n\n\n\n\n\n### References & Resources\n\n1. [World Stock Markets map](https://www.worldtimezone.com/markets24.php)\n2. [Google Colab](https://colab.research.google.com/) \n3. [Jupyter Notebook](https://jupyter.org)\n4. [This code as Colab Notebook](https://colab.research.google.com/github/dualcircle/NOTEBOOKS/blob/main/IS_IT_OPEN%3F_Global_Markets_Statuses_at_a_Glance_with_Python.ipynb) \n\n5. [This code in Github](https://github.com/dualcircle/NOTEBOOKS/blob/main/IS_IT_OPEN%3F_Global_Market_Status_at_a_Glance_with_Python.ipynb) \n\n6. [Choropleth map wiki](http://wiki.gis.com/wiki/index.php/Choropleth_map) \n\n7. [trading hours.com](https://www.tradinghours.com)\n\n8. [NZX exchange](https://www.nzx.com/services/nzx-trading/hours-boards). \n\n9. Jordahl, K. (2014). GeoPandas: Python tools for geographic data. URL: https://Github.Com/Geopandas/Geopandas.\n\n10. J. D. Hunter, \"Matplotlib: A 2D Graphics Environment\", Computing in Science & Engineering, vol. 9, no. 3, pp. 90-95, 2007.\n\n11. McKinney, W., & others. (2010). Data structures for statistical computing in python. In Proceedings of the 9th Python in Science Conference (Vol. 445, pp. 51–56).\n\n12. Bishop, S. (2022). pytz: World timezone definitions, modern and historical. URL: https://github.com/stub42/pytz.",
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      "body": "<div align=\"center\">\n  <center><h1>Ulaanbaatar, Mongolia</h1></center>\n</div>\n<br/> \n\n<div align=\"center\">\n  <center><h4> Rediscovering my wanderlust in a drift through the back-streets and markets of Mongolia's capital city. Personal values of liberty plus aesthetics interweave with respect for\n local people's privacy in color photos and black & whites, captured on several cameras. </h4></center>\n</div>\n<br/>\n\nhttps://cdn.steemitimages.com/DQmRcwAxCGdsf1aMVmZvrsUwq5hxRXJCirf3x5jCyMyAf3s/image.jpeg\nhttps://cdn.steemitimages.com/DQmbVonNQ1Rwk1pFGhMikFtvD4arSN7N14gATX7LyUAyzsH/image.jpeg\nhttps://cdn.steemitimages.com/DQmZyGSiNCfamPJKqQqkBQ2CxuGXMqJvmvdVzdpVzagScpJ/image.jpeg\nhttps://cdn.steemitimages.com/DQmRX9ANjHWNQgbPrGAHiw72tHftbCgFYQbwDVuTJuQeR3T/image.jpeg\nhttps://cdn.steemitimages.com/DQmW565tST6VfuPF1veVgcqCBFGpXHrnMuwteGu7ZEuZLFN/image.jpeg\n\nhttps://cdn.steemitimages.com/DQmf5jnYsQEw8J7EL28wtCWZoLmFQax6t1CMKmj8Be9Nks6/image.jpeg\nhttps://cdn.steemitimages.com/DQmcrTc4fw6Z44dYhqj2L2v18S3M5GHCX6Fo3NEXJsde6QZ/image.jpeg\nhttps://cdn.steemitimages.com/DQmd7wAqq7rBZ7rqf98YTgErtTG6QFu6ju1UDMq54sPXXD8/image.jpeg\nhttps://cdn.steemitimages.com/DQmWibuLUNkFjy1oAuZTz9DGu4JQcdfe8RkyaWhPLjBKfsu/IMG_20220509_092951190~2.jpg\nhttps://cdn.steemitimages.com/DQmZHtBNerinoVs54KVKcfgkYMaK9Hb6TSQuhA1by9mHR4H/image.jpeg\n\nhttps://cdn.steemitimages.com/DQmSe7SNXqRgfg1v8jgGTdZgk5mjZdFUKKQ9Xk4GM91BJgN/image.jpeg\n\nhttps://cdn.steemitimages.com/DQmZ5sHdJZ1oEXXwSboDyJniMAtzrfU7pYcQxzTe8kiunjA/image.jpeg\nhttps://cdn.steemitimages.com/DQmUD1be1wXbKLX6REPv8vM9QL1kcy4mGogf6Zy8nSd2LWh/image.jpeg\n\nhttps://cdn.steemitimages.com/DQmNP7F4pGZZGFTgtjWP5ZU4xUMcD4FXzL7pFy6EvVkzTrh/image.jpeg\n\nhttps://cdn.steemitimages.com/DQma9fbL5rh6F4aGd9rYgXi9G1EhDH3GWKg5xv5pYFBZ1Bb/image.jpeg\nhttps://cdn.steemitimages.com/DQmewn4sMb7B6hJd5Lerdsbq7EcAspFL1UAesb6fMB7HDLN/image.jpeg\n## ...\n![image.jpeg](https://cdn.steemitimages.com/DQmdhQdHYjE5rkF45jBFUuYYgepWhUJvxCjjgqPmSHciT6Y/image.jpeg)\n\n<div align=\"center\">\n  <center><h1>Thanks for stopping by</h1></center>\n</div>\n<br/> \n\n<div align=\"center\">\n  <center><h4>My name is Daniel, I am from Portland, Oregon. I currently reside in Hawaii (in a tent, mainly) and travel when I can. This is my first Steemit post in about four years, I thought you might enjoy. Let me know what you think of my photos? Thanks again, friends. See you soon.</h4></center>\n</div>\n<br/> \n\n\n\n![image.jpeg](https://cdn.steemitimages.com/DQmW6zaK29CmzGFghXyBEQZRNatLU31LUAVwBph8WcfPzbf/image.jpeg)\n\n## BONUS! \n\n#### Since you made it to the end, here's some black and whites from my trip into the countryside with my very gracious host, Mr. Gan. On my next trip to I'll be taking a lot more of these...\n\n![image.jpeg](https://cdn.steemitimages.com/DQmUijhaehWiFHgiHts5bKyUMMLGh1Shgv3KAjcoyrZzrwJ/image.jpeg)\n \n\n![image.jpeg](https://cdn.steemitimages.com/DQmRn5pYrjZVKDToT8sEF5MURYvTSuw3N9ZabsLSkaC24qK/image.jpeg)\n\n![image.jpeg](https://cdn.steemitimages.com/DQmdzkiWGGB7UbnFrmaGU11VST2XGPWqtgiEPeX7Fjfq5AV/image.jpeg)\n\n![image.jpeg](https://cdn.steemitimages.com/DQmNRdHSwFuyZHtxBnRygHR7wLqn6JrTAsyDGEXuwZppxai/image.jpeg)\n\n\n![image.jpeg](https://cdn.steemitimages.com/DQmckDeEJ7fCTpowFvQPebvkPzLK8xSYAjZpeXxjkfZMopK/image.jpeg)\n\n\n\n![image.jpeg](https://cdn.steemitimages.com/DQmXyyoJPtQ1v67zJTn5vrnNUYLpnBBH6vw6ks4ruVr46F2/image.jpeg)\n\n ## Later",
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      "body": "@@ -139,29 +139,22 @@\n BMAC\n-. This is not true as\n+ is incorrect.\n  Som\n@@ -184,16 +184,28 @@\n fied as \n+the species \n Ephedra \n@@ -218,24 +218,69 @@\n lada\n-, a species that\n+ that yields the stimulant Ephedrine. This particular species\n  is \n",
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      "body": "Thanks. This is interesting, but perhaps incomplete.\n\nI'd speculate that the location that you mention the PIE migrants encounter Soma as BMAC. This is not true as Soma has since been identified as Ephedra pachyclada, a species that is geographically bound to the hills of Afghanistan. The identification was based not just on morphology, but also from the Parsis of Bombay, who had been continuously using Soma in their rituals, bringing it in the dried form from Afghanistan.\n\nHowever, it is probably true to say that the migrating PIE pastoralists did borrow much from the BMAC, including linguistic innovations as well as mythological characters (for instance, Indra and Vishnu)",
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      "body": "This is really excellent info and you've portrayed data elegantly☑ so thanks for sharing🐰",
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      "body": "Considering the stigma associated with UFO sightings, \"The Internet Stupid\" cannot explain it all away. Here's a video I made about a sighting I had when I was 7 years old. Took me over 40 yers to report it. And well, I still was unable to show my face. I have more videos where I am finally OK with talking about it all. \n\nSkeptics constantly say there is not evidence. But, why mention facts such as radar data and government documents when your mind is made up? \n\nhttps://steemit.com/ufo/@fakeinternetnews/w3yozgg2 \n\nhttps://steemit.com/ufo/@fakeinternetnews/w3yozgg2",
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      "title": "WHAT IF EVERY BOOK HAD TO TEACH YOU TO READ? A Wish for the Popular Science Genre",
      "body": "<h3>What if the introduction to every book ever published was a complete education in <em>how to read</em>? </h3>\n<img src=\"https://i.imgur.com/uDm0MHp.jpg\" />\n</figure>\n<p><a href=\"https://www.flickr.com/photos/83633410@N07/7658278494/in/photolist-cEJFoo-fcJ7W3-6QmoX-panZto-4SpmP9-54VA3z-9kEtGU-6dmyZy-8oUH34-o3PQ5p-9MUKRY-btLnvB-FH44k-fkr4Fc-dYJWCE-6iBan-fQnF3F-7xMoXo-aSL2ka-dhNbrr-aXWDzt-MqE4jv-bvRkiE-8MExEF-s5zJsf-698vjW-ixpUNm-7ajhh-8oKrbN-r7fyRD-Wzi57r-jLTkK5-S4eP7Y-foBHT1-GNLs7k-2jvD2V-r7Sndv-9mZwQJ-ozNbcy-mMkDH1-m8KMN9-8kyy6Y-adgypB-i6jPQw-8bkya7-oVe8q6-aeoQRS-r1QW7Y-YrY6QE-gknT1B\">Image</a>, <a href=\"https://creativecommons.org/licenses/by-sa/2.0/\">CC license</a></p>\n<p>Imagine page one of literally every book on the planet: an illustration of an eye, an arrow leading into it, and a letter ‘A’ at the arrow’s butt. <em>Start by seeing this letter</em> is the message (change the letter as needed to fit your respective language). </p>\n<p>Flip to page two, and see diagrams of human mouths, contorted into the shapes we make for each variation of the pronunciation of ‘a’, with wavy lines at the throat indicating use of voice: <em>When you see this letter, it might mean these sounds with your throat and mouth…</em> </p>\n<p>…on to step three… and so on.</p>\n<p>I can only guess that it would take a lifetime, at least, to teach a normal person to read that way. Imagine at age one hundred, just finishing your first pass on the introduction. You think “certainly, a refresher course is needed.&quot;  </p>\n<img src=\"https://i.imgur.com/VVMSyEm.jpg\" />\n</figure>\n<p><a href=\"https://www.flickr.com/photos/daquellamanera/4035166398/in/photolist-79zhos-NaZFUP-pZ7HeQ-aAQenp-E4r8j-oKcH3u-bkpd7C-aC8ahL-fimSQh-gqrZGf-4ZcEgt-ixq2Jh-c15Mrd-rdrVj6-pRyVnG-3zf3id-p78SnP-k9CJv3-W4WFfW-ZPoZDF-a5LdRr-rQ99J4-cJPFKA-dVLedP-mRqnfX-kY6As3-qhkXgY-21u7uuT-rm1uH4-edHvST-5QEwiB-anX9FA-9fydEN-adgkTH-peLFhY-J83wgp-qypQKF-iyZKBF-Aim4Z-qoBLXj-4Yo4v8-6tknkf-81Rc1J-pnbZdd-chcDm-op1H3t-6P1AnH-hLFDSN-h2MT45-iJ4kBq\">Image</a>, <a href=\"https://creativecommons.org/licenses/by/2.0/\">CC license</a></p>\n<p>The seemingly infinite steps involved in moving from primitive conceptions of reading (the picture of the letter and the eye) into competent reading skills simply can’t be the job of Books. Rather, it is accomplished through massive collaborative efforts among many people within special institutions (schools). Books are written assuming a community of competent readers is out there already, and that baseline reading ability in any given community will be independent of any specific book or set of books, at least at the time of publication.</p>\n<img src=\"https://i.imgur.com/oXtcHkl.jpg\" />\n</figure>\n<p><a href=\"https://www.flickr.com/photos/cityofbostonarchives/13765395833/in/photolist-mYpfAV-7pQSQ-bXoyaF-5KRnrx-6r26iv-VXqFpF-8692PM-akMFUQ-cYTm3q-53F2Yv-dRisxW-VohbUZ-3meyN-nxNiDK-bj1NSg-7118rU-5kAQsG-f9B9Cf-dDdbUu-5qQvgh-aYtq6-8JpV4R-9ew87p-s5zJsf-dmbarB-nbPPKB-ceL57w-5bBZB-ao7hAp-4RSiX7-bPQxSR-d2mhdU-nbfA9F-p1rLZH-8Jkhjr-cRiZjJ-62VZzV-fDfUAj-akKqKw-5p1N5a-b8vv3K-RK8kjj-6vabsj-7BVsM-39tfq4-dDdcQ1-dYFLFr-8DBKfw-vQCJQ-fDfBkh\">Image</a>, <a href=\"https://creativecommons.org/licenses/by/2.0/\">CC license</a></p>\n<p>Yet there <em>are</em> books out there that task themselves—in an only somewhat metaphorical sense—with teaching the reader to read. It’s a type of book that you’ll probably recognize, one which is both (a) written for general audiences and (b) tasked with explaining highly complex concepts in the sciences—AKA <strong>Popular Science books</strong>. These books have no choice but to start from scratch, at least to a greater degree than is the case in other genres.</p>\n<p>That’s because if you want to get non-scientists to really understand the science you’re writing about, you’ve got to spell out and explore even the philosophical foundations of science; the basics of statistics, and certainly the history of the topic your book is actually about—for starters. Whereas in a school system we can assume the reader/student has taken the prerequisite courses to grasp the texts they’re given, the more you make such an assumption as a writer of Popular Science, the less of a Popular Science book you have written. It’s a trade off. <em>All</em> argumentative writers are tasked with layering evidential, theoretical, and philosophical bedrock before they can get to the point, yet because so much of Science is rooted in principles that are utterly counterintuitive to normal human brains (<a href=\"http://edci670.pbworks.com/w/file/fetch/59138742/Carey_1986.pdf\">Carey, 1986</a>), this task is especially hard for Popular Science authors—hence the lengthiness of books in that genre, and hence PopSci not selling out in stores quite like your Harry Potters and Dan Browns. Let’s face it: the more complex the topic, the more a truly <em>Popular</em> book on the topic must resemble my imagined book that <em>must first teach you to read</em>. </p>\n<img src=\"https://i.imgur.com/AufwM0q.jpg\" />\n</figure>\n<p><a href=\"https://www.flickr.com/photos/nicmcphee/2756494307/in/photolist-5czKHV-jCkPNG-2XUSwK-amZAug-drsgfm-7E3U4g-4zCtVk-bozDhG-bozD8f-Myr8m3-5VDVQa-AUh7cY-bBuyxr-kN6Z19-jCkPQA-k1H7g-pZSLne-cLYUc1-dMg6mC-hFurSM-bK5vji-qt377f-221MEbt-9AVvQY-oesLyt-9LgZfQ-rxc86S-bVxBZv-kX1S3-ifXDEK-SgPVCN-vTFF6P-c9XwMj-a24W6f-b4KD9R-bBcXA2-hFeqHy-8mrcjk-ci5BKj-5Z4Ngw-f9Wm2i-DuXEQ4-bRMJZX-BtcWbK-6b6ku9-5Va8iR-JnWz1b-W8CTYe-4q1ttr-krwKcV\">Image</a>, <a href=\"https://creativecommons.org/licenses/by-sa/2.0/\">CC license</a></p>\n<p>Of course Popular Science writers assume <em>some</em> competencies in the reader (the ability to read, for example), but again, this limits their audience. Every science author (and their publishers, certainly) decides the cutoff for prerequisite competency for reading their book, and then works to teach the reader the “reading skills” necessary to get to the book’s point.       </p>\n<p>Personally, I enjoy a slim and elegant book. I often wish popular science writers would break with the time-honored tradition of laying down foundational concepts and topics and would just tell me, straight up, the basic guiding assumptions of the book I’m about to read and where I can read about them. Read the list, they’d instruct, and choose if you want to (1) go and learn about the fundamentals on your own time, (2) accept them as givens in the context of the book and read on, or (3) reject them flat-out and seek a refund for my book, (and maybe even send the author some hate-mail). This would be a book that spent the minimum time and effort on prerequisite assumptions, and so was only be about what it’s about. I dream of a book that gives the reader’s sophistication on all topics concerned the benefit of the doubt, by telling them exactly what the author needs them to believe in order for them to believe what the author wants them to believe. </p>\n<p></p>\n<h3>References</h3>\n<p>Carey, S. (1986). Cognitive science and science education. <em>American Psychologist</em>, <em>41</em>(10), 1123.</p>",
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