symbioquine/seeding_logs_by_weekday.ipynb

## seeding_logs_by_weekday.ipynb
{
  "metadata": {
    "language_info": {
      "codemirror_mode": {
        "name": "python",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8"
    },
    "kernelspec": {
      "name": "python",
      "display_name": "Pyolite",
      "language": "python"
    }
  },
  "nbformat_minor": 4,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "code",
      "source": "from pyodide.http import pyfetch\nfrom IPython.display import JSON\nfrom js import location\n\nasync def get_all_entities(base_url):\n    next_page = base_url\n    while next_page is not None:\n        resp = await pyfetch(next_page, method='GET')\n\n        data = await resp.json()\n\n        for entity in data.get('data', []):\n            yield entity\n\n        next_page = data.get('links', {}).get('next', {}).get('href', None)\n\nall_seeding_logs = [log async for log in get_all_entities(location.origin + '/api/log/seeding')]",
      "metadata": {
        "trusted": true
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "import datetime\nfrom collections import defaultdict\n\ndef log_ts(log):\n    return datetime.datetime.strptime(log['attributes']['timestamp'], \"%Y-%m-%dT%H:%M:%S%z\")\n\nWEEKDAYS = ['Mo', 'Tu', 'We', 'Th', 'Fr', 'Sa', 'Su']\n\nlog_counts_by_weekday = defaultdict(lambda: 0)\n\nfor log in all_seeding_logs:\n    log_counts_by_weekday[WEEKDAYS[log_ts(log).weekday()]] += 1\n\nJSON(log_counts_by_weekday)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 69,
      "outputs": [
        {
          "execution_count": 69,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<IPython.core.display.JSON object>",
            "application/json": {
              "Th": 56,
              "Su": 156,
              "Tu": 85,
              "Fr": 105,
              "We": 58,
              "Mo": 40,
              "Sa": 22
            }
          },
          "metadata": {
            "application/json": {
              "expanded": false,
              "root": "root"
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "import numpy as np\n\nweekday_occurrences = np.array([log_ts(log).weekday() for log in all_seeding_logs])",
      "metadata": {
        "trusted": true
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "import matplotlib.pyplot as plt\n\n%matplotlib inline\n\nfig, ax = plt.subplots(1, 1)\nax.hist(weekday_occurrences, 7)\n\nax.set_title(\"Seeding Logs by Weekday\")\nax.set_ylabel('# of logs')\n\nfor rect, label in zip(ax.patches, WEEKDAYS):\n    height = rect.get_height()\n    ax.text(rect.get_x() + rect.get_width() / 2, height + 0.1, label, ha='center', va='bottom')\n\nplt.show()\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x7850348>",
            "image/png": 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"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 0 Axes>"
          },
          "metadata": {}
        }
      ]
    }
  ]
}
	{
	"metadata": {
	"language_info": {
	"codemirror_mode": {
	"name": "python",
	"version": 3
	},
	"file_extension": ".py",
	"mimetype": "text/x-python",
	"name": "python",
	"nbconvert_exporter": "python",
	"pygments_lexer": "ipython3",
	"version": "3.8"
	},
	"kernelspec": {
	"name": "python",
	"display_name": "Pyolite",
	"language": "python"
	}
	},
	"nbformat_minor": 4,
	"nbformat": 4,
	"cells": [
	{
	"cell_type": "code",
	"source": "from pyodide.http import pyfetch\nfrom IPython.display import JSON\nfrom js import location\n\nasync def get_all_entities(base_url):\n next_page = base_url\n while next_page is not None:\n resp = await pyfetch(next_page, method='GET')\n\n data = await resp.json()\n\n for entity in data.get('data', []):\n yield entity\n\n next_page = data.get('links', {}).get('next', {}).get('href', None)\n\nall_seeding_logs = [log async for log in get_all_entities(location.origin + '/api/log/seeding')]",
	"metadata": {
	"trusted": true
	},
	"execution_count": 68,
	"outputs": []
	},
	{
	"cell_type": "code",
	"source": "import datetime\nfrom collections import defaultdict\n\ndef log_ts(log):\n return datetime.datetime.strptime(log['attributes']['timestamp'], \"%Y-%m-%dT%H:%M:%S%z\")\n\nWEEKDAYS = ['Mo', 'Tu', 'We', 'Th', 'Fr', 'Sa', 'Su']\n\nlog_counts_by_weekday = defaultdict(lambda: 0)\n\nfor log in all_seeding_logs:\n log_counts_by_weekday[WEEKDAYS[log_ts(log).weekday()]] += 1\n\nJSON(log_counts_by_weekday)",
	"metadata": {
	"trusted": true
	},
	"execution_count": 69,
	"outputs": [
	{
	"execution_count": 69,
	"output_type": "execute_result",
	"data": {
	"text/plain": "<IPython.core.display.JSON object>",
	"application/json": {
	"Th": 56,
	"Su": 156,
	"Tu": 85,
	"Fr": 105,
	"We": 58,
	"Mo": 40,
	"Sa": 22
	}
	},
	"metadata": {
	"application/json": {
	"expanded": false,
	"root": "root"
	}
	}
	}
	]
	},
	{
	"cell_type": "code",
	"source": "import numpy as np\n\nweekday_occurrences = np.array([log_ts(log).weekday() for log in all_seeding_logs])",
	"metadata": {
	"trusted": true
	},
	"execution_count": 70,
	"outputs": []
	},
	{
	"cell_type": "code",
	"source": "import matplotlib.pyplot as plt\n\n%matplotlib inline\n\nfig, ax = plt.subplots(1, 1)\nax.hist(weekday_occurrences, 7)\n\nax.set_title(\"Seeding Logs by Weekday\")\nax.set_ylabel('# of logs')\n\nfor rect, label in zip(ax.patches, WEEKDAYS):\n height = rect.get_height()\n ax.text(rect.get_x() + rect.get_width() / 2, height + 0.1, label, ha='center', va='bottom')\n\nplt.show()\n",
	"metadata": {
	"trusted": true
	},
	"execution_count": 71,
	"outputs": [
	{
	"output_type": "display_data",
	"data": {
	"text/plain": "<pyolite.display.Image at 0x7850348>",
	"image/png": 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"
	},
	"metadata": {}
	},
	{
	"output_type": "display_data",
	"data": {
	"text/plain": "<Figure size 432x288 with 0 Axes>"
	},
	"metadata": {}
	}
	]
	}
	]
	}