328 lines
9.5 KiB
Python
328 lines
9.5 KiB
Python
from collections import Counter, defaultdict
|
|
from datetime import datetime, timedelta
|
|
from pprint import pprint
|
|
|
|
from pymongo import MongoClient
|
|
from zoneinfo import ZoneInfo
|
|
|
|
timezone = ZoneInfo("America/Argentina/Buenos_Aires")
|
|
utctz = ZoneInfo("UTC")
|
|
|
|
client = MongoClient()
|
|
db = client.deskmeter
|
|
switches = db.switch
|
|
tasks = db.task
|
|
|
|
|
|
task_file = "/home/mariano/LETRAS/adm/task/main"
|
|
|
|
|
|
def get_current_task_info():
|
|
"""Get current task ID and path from state and tasks collection"""
|
|
states = db.state
|
|
current_doc = states.find_one({"_id": "current"})
|
|
|
|
if not current_doc or "task" not in current_doc:
|
|
return None, None
|
|
|
|
task_id = current_doc["task"]
|
|
task_doc = tasks.find_one({"task_id": task_id})
|
|
|
|
if task_doc and "path" in task_doc:
|
|
return task_id, task_doc["path"]
|
|
|
|
return task_id, None
|
|
|
|
|
|
def get_task_time_seconds(start, end, task_id, workspaces=None):
|
|
"""Get total seconds for a task within a time period using MongoDB aggregation."""
|
|
if workspaces is None:
|
|
workspaces = ["Plan", "Think", "Work"]
|
|
|
|
pipeline = [
|
|
{
|
|
"$match": {
|
|
"date": {"$gte": start, "$lte": end},
|
|
"task": task_id,
|
|
"workspace": {"$in": workspaces}
|
|
}
|
|
},
|
|
{
|
|
"$group": {
|
|
"_id": None,
|
|
"total_seconds": {"$sum": "$delta"}
|
|
}
|
|
}
|
|
]
|
|
|
|
result = list(switches.aggregate(pipeline))
|
|
|
|
if result and len(result) > 0:
|
|
return result[0]["total_seconds"]
|
|
|
|
return 0
|
|
|
|
|
|
def task_or_none(task=None):
|
|
if not task:
|
|
task = read_and_extract(task_file)
|
|
|
|
if task == "all":
|
|
task = None
|
|
|
|
return task
|
|
|
|
|
|
def now():
|
|
return datetime.now(timezone)
|
|
|
|
|
|
def convert_seconds(seconds, use_days=False):
|
|
days = seconds // 86400
|
|
hours = (seconds % 86400) // 3600
|
|
minutes = (seconds % 3600) // 60
|
|
remaining_seconds = seconds % 60
|
|
|
|
if use_days:
|
|
return "{} days, {:02d}:{:02d}:{:02d}".format(
|
|
days, hours, minutes, remaining_seconds
|
|
)
|
|
return "{:02d}:{:02d}:{:02d}".format(hours + days * 24, minutes, remaining_seconds)
|
|
|
|
|
|
def extract(line):
|
|
if line.rstrip().endswith("*"):
|
|
pipe_index = line.find("|")
|
|
if pipe_index != -1 and len(line) > pipe_index + 8:
|
|
value = line[pipe_index + 1 : pipe_index + 9]
|
|
return value
|
|
return None
|
|
|
|
|
|
def read_and_extract(file_path):
|
|
with open(file_path, "r") as file:
|
|
for line in file:
|
|
value = extract(line)
|
|
if value:
|
|
return value
|
|
return None
|
|
|
|
|
|
def get_work_period_totals(start, end):
|
|
"""Get period totals grouped by task with full path."""
|
|
# Get all tasks with time in the period
|
|
pipeline = [
|
|
{
|
|
"$match": {
|
|
"date": {"$gte": start, "$lte": end},
|
|
"workspace": {"$in": ["Plan", "Think", "Work"]},
|
|
"task": {"$exists": True, "$ne": None}
|
|
}
|
|
},
|
|
{
|
|
"$group": {
|
|
"_id": "$task",
|
|
"total_seconds": {"$sum": "$delta"}
|
|
}
|
|
}
|
|
]
|
|
|
|
results = list(switches.aggregate(pipeline))
|
|
combined_rows = []
|
|
|
|
for result in results:
|
|
task_id = result["_id"]
|
|
total_seconds = result["total_seconds"]
|
|
|
|
if total_seconds > 0:
|
|
# Get task path from tasks collection
|
|
task_doc = tasks.find_one({"task_id": task_id})
|
|
task_path = task_doc["path"] if task_doc and "path" in task_doc else task_id
|
|
|
|
combined_rows.append({
|
|
"ws": task_path,
|
|
"total": convert_seconds(total_seconds)
|
|
})
|
|
|
|
# Sort by path for consistency
|
|
combined_rows.sort(key=lambda x: x["ws"])
|
|
return combined_rows
|
|
|
|
|
|
def get_period_totals(start, end, task=None):
|
|
task_query = {"$in": task.split(",")} if task else {}
|
|
|
|
match_query = {"date": {"$gte": start, "$lte": end}}
|
|
if task_query:
|
|
match_query["task"] = task_query
|
|
|
|
pipeline = [
|
|
{"$match": match_query},
|
|
{"$sort": {"date": 1}},
|
|
{
|
|
"$group": {
|
|
"_id": None,
|
|
"documents_in_range": {"$push": "$$ROOT"},
|
|
"first_doc": {"$first": "$$ROOT"},
|
|
"last_doc": {"$last": "$$ROOT"},
|
|
}
|
|
},
|
|
{
|
|
"$lookup": {
|
|
"from": "switch",
|
|
"let": {"first_date": "$first_doc.date", "task": "$first_doc.task"},
|
|
"pipeline": [
|
|
{
|
|
"$match": {
|
|
"$expr": {
|
|
"$and": [
|
|
{
|
|
"$lt": ["$date", "$$first_date"]
|
|
}, # Only before the first date
|
|
{
|
|
"$eq": ["$task", "$$task"]
|
|
}, # Must have the same task
|
|
]
|
|
}
|
|
}
|
|
},
|
|
{"$sort": {"date": -1}}, # Get the most recent (closest) document
|
|
{"$limit": 1}, # Only the immediate previous document
|
|
],
|
|
"as": "before_first",
|
|
}
|
|
},
|
|
{
|
|
"$project": {
|
|
"documents": {
|
|
"$concatArrays": [
|
|
{"$ifNull": ["$before_first", []]}, # Add only if found
|
|
"$documents_in_range",
|
|
]
|
|
}
|
|
}
|
|
},
|
|
{"$unwind": "$documents"},
|
|
{"$replaceRoot": {"newRoot": "$documents"}},
|
|
{
|
|
"$group": {
|
|
"_id": "$workspace",
|
|
"total": {"$sum": "$delta"},
|
|
}
|
|
},
|
|
]
|
|
|
|
results = list(switches.aggregate(pipeline))
|
|
|
|
if not results:
|
|
return [{"ws": "No Data", "total": ""}]
|
|
|
|
pipeline_before_after = [
|
|
# Match documents within the date range
|
|
{"$match": match_query},
|
|
{"$sort": {"date": 1}},
|
|
{
|
|
"$group": {
|
|
"_id": None,
|
|
"first_doc": {"$first": "$$ROOT"},
|
|
"last_doc": {"$last": "$$ROOT"},
|
|
}
|
|
},
|
|
# Lookup to get one document before the first document in the range
|
|
{
|
|
"$lookup": {
|
|
"from": "switch",
|
|
"let": {"first_date": "$first_doc.date", "task": "$first_doc.task"},
|
|
"pipeline": [
|
|
{
|
|
"$match": {
|
|
"$expr": {
|
|
"$and": [
|
|
{
|
|
"$lt": ["$date", "$$first_date"]
|
|
}, # Only before the first date
|
|
{
|
|
"$eq": ["$task", "$$task"]
|
|
}, # Must have the same task
|
|
]
|
|
}
|
|
}
|
|
},
|
|
{"$sort": {"date": -1}}, # Get the most recent (closest) document
|
|
{"$limit": 1}, # Only the immediate previous document
|
|
],
|
|
"as": "before_first",
|
|
}
|
|
},
|
|
{
|
|
"$project": {
|
|
"before_first": {
|
|
"$ifNull": [{"$arrayElemAt": ["$before_first", 0]}, ""]
|
|
},
|
|
"last_doc": "$last_doc", # Include the last_doc from the matched period
|
|
}
|
|
},
|
|
]
|
|
|
|
aux_results = list(switches.aggregate(pipeline_before_after))
|
|
|
|
bfirst = aux_results[0]["before_first"]
|
|
|
|
if bfirst:
|
|
bfdate = bfirst["date"].replace(tzinfo=utctz)
|
|
start_delta = round((start - bfdate.astimezone(timezone)).total_seconds())
|
|
|
|
ldoc = aux_results[0]["last_doc"]
|
|
lastdate = ldoc["date"].replace(tzinfo=utctz)
|
|
end_delta = round((end - lastdate.astimezone(timezone)).total_seconds())
|
|
|
|
rows = []
|
|
active_vs_idle = {"Active": 0, "Idle": 0}
|
|
|
|
for result in results:
|
|
if bfirst:
|
|
if result["_id"] == bfirst["workspace"]:
|
|
result["total"] -= start_delta
|
|
|
|
if end < now():
|
|
if result["_id"] == ldoc["workspace"]:
|
|
result["total"] -= ldoc["delta"] - end_delta
|
|
|
|
for result in results:
|
|
if result["total"] > 0:
|
|
rows.append(
|
|
{"ws": result["_id"], "total": convert_seconds(result["total"])}
|
|
)
|
|
if result["_id"] in ["Think", "Plan", "Work"]:
|
|
active_vs_idle["Active"] += result["total"]
|
|
if result["_id"] in ["Away", "Other"]:
|
|
active_vs_idle["Idle"] += result["total"]
|
|
|
|
order = ["Plan", "Think", "Work", "Other", "Away", "Active", "Idle"]
|
|
|
|
rows = sorted(rows, key=lambda x: order.index(x["ws"]))
|
|
|
|
for k, v in active_vs_idle.items():
|
|
rows.append({"ws": k, "total": convert_seconds(v)})
|
|
|
|
return rows
|
|
|
|
|
|
# print(
|
|
# get_period_totals(
|
|
# datetime.today().replace(hour=0, minute=0, second=0, tzinfo=timezone)
|
|
# - timedelta(days=1),
|
|
# datetime.today().replace(hour=23, minute=59, second=59, tzinfo=timezone)
|
|
# - timedelta(days=1),
|
|
# # "ffbe198e",
|
|
# )
|
|
# )
|
|
|
|
# print(
|
|
# get_period_totals(
|
|
# datetime.today().replace(hour=0, minute=0, second=0, tzinfo=timezone),
|
|
# datetime.today().replace(hour=23, minute=59, second=59, tzinfo=timezone),
|
|
# "5fc751ec",
|
|
# )
|
|
# )
|