#!/usr/bin/env python3

import csv
import sys
from pandas import DataFrame
from pandas import Grouper
from matplotlib import pyplot

jobs = sys.argv[1:]

print("Analyzing the jobs: " + str(jobs))

# Plot the timeseries
def plot(header, row):
  x = { h : d for (h, d) in zip(header, row)}
  jobid = x["jobid"]
  del x["jobid"]
  del x["coding_abs"]
  del x["coding_abs_aggzeros"]

  result = []
  for k in x:
    timeseries = x[k].split(":")
    timeseries = [ float(x) for x in timeseries]
    if sum(timeseries) == 0:
      continue
    timeseries = [ [k, x, s] for (s,x) in zip(timeseries, range(1, len(timeseries))) ]
    result.extend(timeseries)

  data = DataFrame(result, columns=["metrics", "segment", "value"])
  groups = data.groupby(["metrics"])
  metrics = DataFrame()
  labels = []
  for name, group in groups:
    metrics[name] = [x[2] for x in group.values]
    labels.append(name)

  ax = metrics.plot(subplots=True, legend=False, sharex=True, grid = True,  sharey=True, colormap='jet', marker='.', markersize=10)
  for (i, l) in zip(range(0, len(labels)), labels):
    ax[i].set_ylabel(l)

  pyplot.xlabel("Segment number")
  pyplot.savefig("timeseries" + jobid + ".png")

  # Plot first 30 minutes

  ax = metrics.plot(subplots=True, legend=False, sharex=True, grid = True,  sharey=True, colormap='jet', marker='.', markersize=10, xlim=(0,30))
  for (i, l) in zip(range(0, len(labels)), labels):
    ax[i].set_ylabel(l)

  pyplot.xlabel("Segment number")
  pyplot.savefig("timeseries" + jobid + "-30.png")




with open('job-io-datasets/datasets/job_codings.csv') as csv_file:
    csv_reader = csv.reader(csv_file, delimiter=',')
    line_count = 0
    for row in csv_reader:
      if line_count == 0:
        header = row
        line_count += 1
        continue

      if not row[0].strip() in jobs:
        continue
      else:
        plot(header, row)