148 lines
4.1 KiB
Python
Executable File
148 lines
4.1 KiB
Python
Executable File
#!/usr/bin/env python3
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import csv
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import sys
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from pandas import DataFrame
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from pandas import Grouper
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import seaborn as sns
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from matplotlib import pyplot
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import matplotlib.cm as cm
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jobs = sys.argv[1].split(",")
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prefix = sys.argv[2].split(",")
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fileformat = ".png"
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print("Plotting the job: " + str(sys.argv[1]))
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print("Plotting with prefix: " + str(sys.argv[2]))
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# Color map
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colorMap = { "md_file_create": cm.tab10(0),
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"md_file_delete": cm.tab10(1),
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"md_mod": cm.tab10(2),
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"md_other": cm.tab10(3),
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"md_read": cm.tab10(4),
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"read_bytes": cm.tab10(5),
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"read_calls": cm.tab10(6),
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"write_bytes": cm.tab10(7),
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"write_calls": cm.tab10(8)
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}
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markerMap = { "md_file_create": "^",
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"md_file_delete": "v",
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"md_other": ".",
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"md_mod": "<",
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"md_read": ">",
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"read_bytes": "h",
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"read_calls": "H",
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"write_bytes": "D",
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"write_calls": "d"
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}
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linestyleMap = { "md_file_create": ":",
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"md_file_delete": ":",
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"md_mod": ":",
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"md_other": ":",
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"md_read": ":",
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"read_bytes": "--",
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"read_calls": "--",
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"write_bytes": "-.",
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"write_calls": "-."
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}
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# Plot the timeseries
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def plot(prefix, header, row):
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x = { h : d for (h, d) in zip(header, row)}
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jobid = x["jobid"]
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del x["jobid"]
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result = []
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for k in x:
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timeseries = x[k].split(":")
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timeseries = [ float(x) for x in timeseries]
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if sum(timeseries) == 0:
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continue
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timeseries = [ [k, x, s] for (s,x) in zip(timeseries, range(0, len(timeseries))) ]
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result.extend(timeseries)
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if len(result) == 0:
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print("Empty job! Cannot plot!")
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return
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data = DataFrame(result, columns=["metrics", "segment", "value"])
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groups = data.groupby(["metrics"])
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metrics = DataFrame()
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labels = []
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colors = []
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style = []
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for name, group in groups:
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style.append(linestyleMap[name] + markerMap[name])
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colors.append(colorMap[name])
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if name == "md_file_delete":
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name = "file_delete"
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if name == "md_file_create":
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name = "file_create"
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metrics[name] = [x[2] for x in group.values]
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labels.append(name)
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fsize = (8, 1 + 1.1 * len(labels))
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fsizeFixed = (8, 2)
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fsizeHist = (8, 4)
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pyplot.close('all')
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if len(labels) < 4 :
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ax = metrics.plot(legend=True, sharex=True, grid = True, sharey=True, markersize=10, figsize=fsizeFixed, color=colors, style=style)
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ax.set_ylabel("Value")
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else:
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ax = metrics.plot(subplots=True, legend=False, sharex=True, grid = True, sharey=True, markersize=10, figsize=fsize, color=colors, style=style)
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for (i, l) in zip(range(0, len(labels)), labels):
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ax[i].set_ylabel(l)
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pyplot.xlabel("Segment number")
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pyplot.savefig(prefix + "timeseries" + jobid + fileformat, bbox_inches='tight', dpi=150)
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# Create a facetted grid
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#g = sns.FacetGrid(tips, col="time", margin_titles=True)
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#bins = np.linspace(0, 60, 13)
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#g.map(plt.hist, "total_bill", color="steelblue", bins=bins)
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ax = metrics.hist(sharex=True, grid = True, sharey=True, figsize=fsizeHist, bins=10)
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pyplot.savefig(prefix + "hist" + jobid + fileformat, bbox_inches='tight', dpi=150)
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# Plot first 30 segments
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if len(timeseries) <= 50:
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return
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if len(labels) < 4 :
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ax = metrics.plot(legend=True, xlim=(0,30), sharex=True, grid = True, sharey=True, markersize=10, figsize=fsizeFixed, color=colors, style=style)
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ax.set_ylabel("Value")
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else:
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ax = metrics.plot(subplots=True, xlim=(0,30), legend=False, sharex=True, grid = True, sharey=True, markersize=10, figsize=fsize, color=colors, style=style)
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for (i, l) in zip(range(0, len(labels)), labels):
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ax[i].set_ylabel(l)
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pyplot.xlabel("Segment number")
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pyplot.savefig(prefix + "timeseries" + jobid + "-30" + fileformat, bbox_inches='tight', dpi=150)
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### end plotting function
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#with open('job-io-datasets/datasets/job_codings.csv') as csv_file: # EB: old codings
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with open('./datasets/job_codings_v4.csv') as csv_file: # EB: v3 codings moved to this repo
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csv_reader = csv.reader(csv_file, delimiter=',')
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line_count = 0
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for row in csv_reader:
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if line_count == 0:
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header = row
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line_count += 1
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continue
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job = row[0].strip()
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if not job in jobs:
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continue
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else:
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index = jobs.index(job)
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plot(prefix[index] + "-ks-" + str(index), header, row)
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