initial flattened summary report data

Author: d33bs

README.md

@@ -1,2 +1,3 @@
 # toggl-work-reports
+
 Perform routine toggl work report generation or calculations

poetry.lock

@@ -0,0 +1,17 @@
+[tool.poetry]
+name = "toggl-work-reports"
+version = "0.0.1"
+description = "Perform routine toggl work report generation or calculations"
+authors = ["d33bs <[email protected]>"]
+license = "Apache License 2.0"
+readme = "README.md"
+packages = [{include = "toggl_work_reports"}]
+
+[tool.poetry.dependencies]
+python = "^3.9"
+togglpy = "^0.1.2"
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

pyproject.toml

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+import os
+import pprint
+from datetime import datetime, timedelta
+
+from toggl.TogglPy import Toggl
+
+# init a pretty printer
+pp = pprint.PrettyPrinter(indent=4)
+
+# gather the api token from environment
+api_token = os.environ["TOGGL_API_TOKEN"]
+
+# form the toggl client
+toggl = Toggl()
+toggl.setAPIKey(api_token)
+
+
+def get_flattened_summary_data(toggl: Toggl, date: str) -> dict:
+    """
+    Find and return data structure which includes flattened
+    summary stats from toggl workday based on date str provided
+    """
+
+    # gather a workspace for making report requests
+    workspaces = toggl.getWorkspaces()
+    default_workspace_id = workspaces[0]["id"]
+
+    # specify that we want reports from this week
+    req_data = {
+        "workspace_id": default_workspace_id,
+        "since": date,
+        "until": date,
+    }
+
+    # gather the report data
+    report_data = toggl.getSummaryReport(req_data)
+
+    # filter the report data
+    filtered_report_data = [
+        {"title": item["title"]["project"].split(" - ")[0], "time": item["time"]}
+        for item in report_data["data"]
+    ]
+
+    # group time values together
+    grouped = {}
+    for item in filtered_report_data:
+        if item["title"] not in grouped.keys():
+            grouped[item["title"]] = item["time"]
+        else:
+            grouped[item["title"]] += item["time"]
+
+    # percentage of total time for grouped values
+    return {
+        key: round((val / (report_data["total_grand"])) * 100, 2)
+        for key, val in grouped.items()
+    }
+
+
+# gather the last 3 days of work as flattened report data
+for num in range(1, 4):
+    date = (datetime.now() - timedelta(num)).strftime("%Y-%m-%d")
+    print(date)
+    pp.pprint(get_flattened_summary_data(toggl, date))