add a vis directory

gstlal-ugly/share/vis/google_data_server_test

+#!/usr/bin/python
+
+import gviz_api
+import h5py
+from gstlal import llweb
+import os
+import cgi
+import cgitb
+cgitb.enable()
+form = cgi.parse()
+import numpy
+import pyparsing
+
+description = {
+	"time": ("number", "Time"),
+	"latency": ("number", "Latency")
+	}
+
+# we have to get a query according to the google standard
+# queries must be of the form
+# tq = 
+assert "tq" in form
+assert "reqId" in form
+query = form["tq"][0]
+
+#
+# Default values
+#
+
+data_type = "latency_history"
+base_path = "/home/gstlalcbctest/engineering/10/S6/trigs_single_ifo/aggregator"
+latest_path = base_path
+for level in range(7):
+	latest_path += "/%s" % sorted([d for d in os.listdir(path) if len(d) == 1 and d.isdigit()])[-1]
+# The power of 10 that we will be accessing data at
+resolution = 1000
+
+#
+# "SQL" parser.  FIXME. First, google query langauge isn't really SQL and
+# second, if this keeps going we need to switch to a parsing library e.g.,
+# pyparsing.  For now this is likely to be a collection of once-offs.
+#
+
+#
+# Data types
+#
+for data in ("latency_history", "snr_history"):
+	if data in form:
+		data_type = data
+
+print "Content-type: text/plain"
+print
+print query
+
+
+if "tq" in form:
+	if "latency_history" in form["tq"][0]:
+		path += "/median/latency_history.hdf5"
+	elif "snr_history" in form["tq"][0]:
+		path += "/median/snr_history.hdf5"
+	else:
+		path += "/median/latency_history.hdf5"
+else:
+	path += "/median/latency_history.hdf5"
+
+f = h5py.File(path, "r")
+data = []
+
+for t, d in zip(f["time"], f["data"]):
+	#data.append({"time":(float(t),str(t)), "latency":(float(d),str(d))})
+	d = numpy.random.rand()
+	data.append({"time":(float(t),str(t)), "latency":(float(d),str(d))})
+f.close()
+
+data_table = gviz_api.DataTable(description)
+data_table.LoadData(data)
+print "Content-type: text/plain"
+print
+print data_table.ToJSonResponse(columns_order=("time", "latency"), order_by="time")