{
    "info": {
        "author": "David Ohana",
        "author_email": "davidoha@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Software Development :: Libraries"
        ],
        "description": "# Scolp\n\n## Introduction\n\nScolp is Streaming Column Printer for Python 3.6 or later.\n\nScolp let you easily pretty-print masses of tabular data in a streaming fashion - each value is printed when available, without waiting for end of data. It is perfect for apps that need to print progress reports in columns.\n\nMain features:\n\n* Auto-adjusting column width according to the largest value so far or column header width.\n\n* Control verbosity of printing by:\n    - printing ``1`` of each ``n`` rows\n    - printing no more than ``1`` row per ``n`` seconds\n\n* Control format of printed values by:\n    - setting global defaults\n    - setting defaults per variable type (``int``, ``float``, ``str``, ``datetime``)\n    - setting explicit formatting per column\n\n* Control alignment of printed values:\n    - left\n    - right\n    - center\n    - auto: numbers to the right, strings or other types to the left.\n\n* Control cosmetics of columns (initial width, padding fill char, alignment, and more..) by:\n    - setting global defaults\n    - setting explicit formatting per column\n\n* Control column title printing style:\n    - Inline in each row\n    - As headers, repeating each n rows\n\n* Easily print row count or time since execution started without need to keep track of those values yourself.\n\n## Examples\n\n#### Example 1\n\nLets start with a simple country statistics output using default settings:\n\n```python\nimport scolp\n\nscolper = scolp.Scolp()\nscolper.config.add_columns(\"country\", \"population (mil)\", \"capital city\", \"life expectancy (female)\",\n                           \"life expectancy (male)\", \"fertility rate\")\nscolper.print(\"Netherlands\", 16.81, \"Amsterdam\", 83, 79, 1.5,\n              \"China\", 1350.0, \"Beijing\", 76, 72, 1.8,\n              \"Israel\", 7.71, \"Jerusalem\", 84, 80, 2.7,\n              \"Nigeria\")\nscolper.print(174.51)\n```\n\nOutput: \n\n(Note how column width is auto adjusting, line breaks are printed automatically after last column, and each value is printed immediately without waiting for end of row)\n\n```\ncountry |population (mil)|capital city|life expectancy (female)|life expectancy (male)|fertility rate\n--------|----------------|------------|------------------------|----------------------|--------------\nNetherlands|          16.810|Amsterdam   |                      83|                    79|         1.500\n\ncountry    |population (mil)|capital city|life expectancy (female)|life expectancy (male)|fertility rate\n-----------|----------------|------------|------------------------|----------------------|--------------\nChina      |       1,350.000|Beijing     |                      76|                    72|         1.800\nIsrael     |           7.710|Jerusalem   |                      84|                    80|         2.700\nNigeria    |         174.510|\n```\n\n#### Example 2\n\nLets build a program that find prime numbers. We will print the count of primes\nwe found so far and the last prime found.\n\n```python\nimport datetime, scolp\n\ndef is_prime(num):\n    return 2 in [num, 2 ** num % num]\n\nscolper = scolp.Scolp()\nscolper.config.add_columns(\"time\", \"elapsed\", \"inspected_count\", \"prime_count\", \"last\", \"progress %\")\nscolper.config.output_each_n_seconds = 1\n\nprime_count = 0\nlast_prime = None\ni = 9_999_800\ntarget_count = 30\nwhile prime_count < target_count:\n    if is_prime(i):\n        last_prime = i\n        prime_count += 1\n    progress = prime_count / target_count * 100\n    scolper.print(datetime.datetime.now(), scolper.elapsed_since_init(),\n                  scolper.row_index + 1, prime_count, last_prime, progress)\n    i += 1\n\n```\n\nOutput: \n\n(Note how the header repeats, the column width auto-expanding and the numbers are aligned to the right)\n\n```\ntime    |elapsed |inspected_count|prime_count|last    |progress %\n--------|--------|---------------|-----------|--------|----------\n2019-06-05 11:49:31.271191|0:00:00 |              1|          0|None    |     0.000\n\ntime                      |elapsed |inspected_count|prime_count|last    |progress %\n--------------------------|--------|---------------|-----------|--------|----------\n2019-06-05 11:49:32.306225|0:00:01 |             27|          1|9,999,823|     3.333\n2019-06-05 11:49:33.325694|0:00:02 |             53|          1|9,999,823|     3.333\n2019-06-05 11:49:34.341678|0:00:03 |             79|          3|9,999,877|    10.000\n2019-06-05 11:49:35.378966|0:00:04 |            105|          6|9,999,901|    20.000\n2019-06-05 11:49:36.399298|0:00:05 |            131|          8|9,999,929|    26.667\n2019-06-05 11:49:37.415522|0:00:06 |            157|         11|9,999,943|    36.667\n2019-06-05 11:49:38.450551|0:00:07 |            183|         13|9,999,973|    43.333\n2019-06-05 11:49:39.478987|0:00:08 |            209|         14|9,999,991|    46.667\n2019-06-05 11:49:40.485409|0:00:09 |            233|         15|10,000,019|    50.000\n\ntime                      |elapsed |inspected_count|prime_count|last      |progress %\n--------------------------|--------|---------------|-----------|----------|----------\n2019-06-05 11:49:41.508298|0:00:10 |            259|         15|10,000,019|    50.000\n2019-06-05 11:49:42.543115|0:00:11 |            283|         16|10,000,079|    53.333\n2019-06-05 11:49:43.555733|0:00:12 |            306|         17|10,000,103|    56.667\n2019-06-05 11:49:44.572379|0:00:13 |            328|         18|10,000,121|    60.000\n2019-06-05 11:49:45.574066|0:00:14 |            349|         20|10,000,141|    66.667\n2019-06-05 11:49:46.583462|0:00:15 |            372|         21|10,000,169|    70.000\n2019-06-05 11:49:47.594724|0:00:16 |            396|         22|10,000,189|    73.333\n2019-06-05 11:49:48.639124|0:00:17 |            420|         22|10,000,189|    73.333\n2019-06-05 11:49:49.661211|0:00:18 |            441|         24|10,000,229|    80.000\n2019-06-05 11:49:50.691037|0:00:19 |            463|         27|10,000,261|    90.000\n\ntime                      |elapsed |inspected_count|prime_count|last      |progress %\n--------------------------|--------|---------------|-----------|----------|----------\n2019-06-05 11:49:51.721844|0:00:20 |            487|         28|10,000,271|    93.333\n2019-06-05 11:49:52.733437|0:00:22 |            510|         29|10,000,303|    96.667\n2019-06-05 11:49:53.750463|0:00:23 |            534|         29|10,000,303|    96.667\n```\n\n#### Example 3\n\nNow, lets change the code of the previous example to add a bit of custom formatting:\n\n```python\nscolper = scolp.Scolp()\nscolper.config.add_column(\"time\", width=20)\nscolper.config.add_columns(\"elapsed\",\n                           \"inspected_count\",\n                           \"prime_count\")\nscolper.config.add_column(\"last\", width=11)\nscolper.config.add_column(\"progress\", fmt=\"{:.1%}\")\nscolper.config.output_each_n_seconds = 1\nscolper.config.header_repeat_row_count_first = 0\nscolper.config.default_column.column_separator = \" \"\nscolper.config.default_column.type_to_format[datetime.datetime] = \"{:%Y-%m-%d %H:%M:%S}\"\n\nprime_count = 0\nlast_prime = None\ni = 9_999_800\ntarget_count = 30\nwhile prime_count < target_count:\n    if is_prime(i):\n        last_prime = i\n        prime_count += 1\n    progress = prime_count / target_count\n    scolper.print(datetime.datetime.now(), scolper.elapsed_since_init(),\n                  scolper.row_index + 1, prime_count, last_prime, progress)\n    i += 1\n```\n\nOutput:\n\n```\ntime                 elapsed  inspected_count prime_count last        progress\n-------------------- -------- --------------- ----------- ----------- --------\n2019-06-05 11:54:46  0:00:00                1           0 None            0.0%\n2019-06-05 11:54:47  0:00:01               23           0 None            0.0%\n2019-06-05 11:54:48  0:00:02               45           1   9,999,823     3.3%\n2019-06-05 11:54:49  0:00:03               67           2   9,999,863     6.7%\n2019-06-05 11:54:50  0:00:04               90           5   9,999,889    16.7%\n2019-06-05 11:54:51  0:00:05              115           7   9,999,907    23.3%\n2019-06-05 11:54:52  0:00:06              139          10   9,999,937    33.3%\n2019-06-05 11:54:53  0:00:07              164          11   9,999,943    36.7%\n2019-06-05 11:54:54  0:00:08              188          13   9,999,973    43.3%\n2019-06-05 11:54:55  0:00:09              212          14   9,999,991    46.7%\n\ntime                 elapsed  inspected_count prime_count last        progress\n-------------------- -------- --------------- ----------- ----------- --------\n2019-06-05 11:54:56  0:00:10              237          15  10,000,019    50.0%\n2019-06-05 11:54:57  0:00:11              261          15  10,000,019    50.0%\n2019-06-05 11:54:58  0:00:12              284          16  10,000,079    53.3%\n2019-06-05 11:54:59  0:00:13              308          17  10,000,103    56.7%\n2019-06-05 11:55:00  0:00:14              331          18  10,000,121    60.0%\n2019-06-05 11:55:01  0:00:15              355          20  10,000,141    66.7%\n2019-06-05 11:55:02  0:00:16              379          21  10,000,169    70.0%\n2019-06-05 11:55:03  0:00:17              403          22  10,000,189    73.3%\n2019-06-05 11:55:04  0:00:18              426          23  10,000,223    76.7%\n2019-06-05 11:55:05  0:00:20              448          25  10,000,247    83.3%\n\ntime                 elapsed  inspected_count prime_count last        progress\n-------------------- -------- --------------- ----------- ----------- --------\n2019-06-05 11:55:06  0:00:21              471          27  10,000,261    90.0%\n2019-06-05 11:55:07  0:00:22              493          28  10,000,271    93.3%\n2019-06-05 11:55:08  0:00:23              516          29  10,000,303    96.7%\n2019-06-05 11:55:09  0:00:24              539          29  10,000,303    96.7%\n```\n\n#### Example 4\n\nLets build an HTTP big-file downloader.\n\n```python\nimport datetime, urllib3, scolp\n\nurl = \"http://speedtest.tele2.net/100MB.zip\"\npath = \"downloaded.tmp\"\nchunk_size_bytes = 1000\n\nscolp_cfg = scolp.Config()\nscolp_cfg.add_column(\"time\", fmt=\"{:%H:%M:%S}\")\nscolp_cfg.add_column(\"elapsed\")\nscolp_cfg.add_column(\"downloaded\", width=16, fmt=\"{:,} B\")\nscolp_cfg.add_column(\"speed\", width=14, pad_align=scolp.Alignment.RIGHT, type_to_format={float: \"{:,.1f} kB/s\"})\n\nscolp_cfg.output_each_n_seconds = 1\nscolp_cfg.title_mode = scolp.TitleMode.INLINE\nscolp_cfg.default_column.column_separator = \"  |  \"\n\nscolper = scolp.Scolp(scolp_cfg)\n\nhttp = urllib3.PoolManager()\nr = http.request('GET', url, preload_content=False)\n\ndl_bytes = 0\n\n\ndef progress_update():\n    elapsed_sec = scolper.elapsed_since_init().total_seconds()\n    speed_kbps = dl_bytes / elapsed_sec / 1000 if elapsed_sec > 0 else \"unknown\"\n    scolper.print(datetime.datetime.now(), scolper.elapsed_since_init(), dl_bytes, speed_kbps)\n\n\nwith open(path, 'wb') as out:\n    while True:\n        data = r.read(chunk_size_bytes)\n        if not data:\n            break\n        out.write(data)\n        dl_bytes += len(data)\n        progress_update()\n\nscolper.force_print_next_row()\nprogress_update()\nr.release_conn()\n\n```\n\nOutput:\n\n```\ntime=14:30:11  |  elapsed=0:00:00   |  downloaded=         1,000 B  |  speed=       unknown\ntime=14:30:12  |  elapsed=0:00:01   |  downloaded=       801,000 B  |  speed=    801.0 kB/s\ntime=14:30:13  |  elapsed=0:00:02   |  downloaded=     1,743,000 B  |  speed=    871.5 kB/s\ntime=14:30:14  |  elapsed=0:00:03   |  downloaded=     2,758,000 B  |  speed=    919.3 kB/s\ntime=14:30:15  |  elapsed=0:00:04   |  downloaded=     3,779,000 B  |  speed=    944.8 kB/s\ntime=14:30:16  |  elapsed=0:00:05   |  downloaded=     4,794,000 B  |  speed=    958.8 kB/s\ntime=14:30:17  |  elapsed=0:00:06   |  downloaded=     5,809,000 B  |  speed=    968.2 kB/s\ntime=14:30:18  |  elapsed=0:00:07   |  downloaded=     6,824,000 B  |  speed=    974.9 kB/s\ntime=14:30:19  |  elapsed=0:00:08   |  downloaded=     7,839,000 B  |  speed=    979.9 kB/s\ntime=14:30:20  |  elapsed=0:00:09   |  downloaded=     8,857,000 B  |  speed=    984.1 kB/s\ntime=14:30:21  |  elapsed=0:00:10   |  downloaded=     9,799,000 B  |  speed=    979.9 kB/s\ntime=14:30:22  |  elapsed=0:00:11   |  downloaded=    10,814,000 B  |  speed=    983.1 kB/s\ntime=14:30:23  |  elapsed=0:00:12   |  downloaded=    11,838,000 B  |  speed=    986.5 kB/s\ntime=14:30:24  |  elapsed=0:00:13   |  downloaded=    12,855,000 B  |  speed=    988.8 kB/s\ntime=14:30:25  |  elapsed=0:00:14   |  downloaded=    13,870,000 B  |  speed=    990.7 kB/s\ntime=14:30:26  |  elapsed=0:00:15   |  downloaded=    14,891,000 B  |  speed=    992.7 kB/s\ntime=14:30:27  |  elapsed=0:00:16   |  downloaded=    15,906,000 B  |  speed=    994.1 kB/s\ntime=14:30:28  |  elapsed=0:00:18   |  downloaded=    25,600,000 B  |  speed=  1,422.2 kB/s\ntime=14:30:29  |  elapsed=0:00:19   |  downloaded=    37,146,000 B  |  speed=  1,955.1 kB/s\ntime=14:30:30  |  elapsed=0:00:20   |  downloaded=    47,847,000 B  |  speed=  2,392.3 kB/s\ntime=14:30:31  |  elapsed=0:00:21   |  downloaded=    60,962,000 B  |  speed=  2,903.0 kB/s\ntime=14:30:32  |  elapsed=0:00:22   |  downloaded=    72,931,000 B  |  speed=  3,315.0 kB/s\ntime=14:30:33  |  elapsed=0:00:23   |  downloaded=    85,094,000 B  |  speed=  3,699.7 kB/s\ntime=14:30:34  |  elapsed=0:00:24   |  downloaded=   104,857,600 B  |  speed=  4,369.1 kB/s\n```\n\n\n## Requirements\n\nScolp has no 3rd party requirements other than Python 3.6 or later.\n\n\n## Getting Started\n\nScolp is available via PyPi and can be installed using:\n\n```pip install scolp```.\n\n## Todo\n\n* 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