5 # @purpose Process TCS data
13 import re # Regular expressions - for removing comments
14 import odict #ordered dictionary
17 import Gnuplot, Gnuplot.funcutils
19 gnuplot = Gnuplot.Gnuplot()
21 def GetData(filename):
22 input_file = open(filename, "r")
24 for line in input_file:
25 line = re.sub("#.*", "", line).strip("\r\n ")
28 data.append(map(lambda e : float(e), line.split("\t")))
34 def AverageAllDataSets(directory=".", function=DoNothing):
36 for f in os.listdir(directory):
37 if os.path.isdir(directory+"/"+str(f)) == True:
39 for datafile in os.listdir(directory+"/"+str(f)):
40 if datafile.split(".")[1] == "dat":
41 data_set.append(GetData(f))
43 avg = Average(data_set)
44 dirs.update({f : avg})
47 def GetDataSets(directory="."):
49 for f in os.listdir(directory):
50 if os.path.isdir(directory+"/"+str(f)) == False:
51 if (len(f.split(".")) > 1 and f.split(".")[1] == "dat"):
52 d = GetData(directory+"/"+str(f))
59 def Derivative(data, a=1, b=2, sigma=None,step=1):
65 for i in range(0, len(data),step):
66 result[len(result)-1] = [d for d in data[i]]
68 dE[0] = data[i][a] - data[i-step][a]
69 dI[0] = data[i][b] - data[i-step][b]
73 if (i < len(data)-step):
74 dE[1] = data[i+step][a] - data[i][a]
75 dI[1] = data[i+step][b] - data[i][b]
80 #print str(data[i]) + " ["+str(sigma)+"] = " + str(data[i][int(abs(sigma))])
82 if dI[0] != None: dI[0] -= 0.5*data[i][int(abs(sigma))]
83 if dI[1] != None: dI[1] -= 0.5*data[i][int(abs(sigma))]
85 if dI[0] != None: dI[0] += 0.5*data[i][int(abs(sigma))]
86 if dI[1] != None: dI[1] += 0.5*data[i][int(abs(sigma))]
100 deltaI /= float(count)
101 deltaE /= float(count)
105 result[len(result)-1][b] = (deltaI / deltaE)
107 result[len(result)-1][b] = 0.0
110 return result[0:len(result)-1]
112 def MaxNormalise(data, u=1):
113 result = copy.deepcopy(data)
116 maxval = max(data, key = lambda e : e[u])[u]
123 def Average(data_sets, u=1):
128 #print "Already have " + str(p[u])
130 for i in range(0, len(p)):
131 avg[p[u]][0][i] += p[i]
133 #print "Create key for " + str(p[u])
134 avg.update({p[u] : [p, 1]})
137 for i in range(0, len(avg[a][0])):
138 avg[a][0][i] /= float(avg[a][1])
139 return map(lambda e : e[1][0], sorted(avg.items(), key = lambda e : e[0]))
141 def FullWidthAtHalfMax(data, u=1):
142 maxval = max(data, key = lambda e : e[u])
143 peak = data.index(maxval)
147 for i in range(1, len(data)/2):
149 if (peak-i > 0 and data[peak-i] < 0.50*maxval):
150 lhs = data[peak-i][u]
152 if (peak+i < len(data) and data[peak+i] < 0.50*maxval):
154 if lhs != None and rhs != None:
156 if rhs == None or lhs == None:
157 return abs(data[len(data)-1][0] - data[0][0])
159 return abs(rhs - lhs)
161 def SaveData(filename, data):
162 out = open(filename, "w", 0)
164 for i in range(0, len(a)):
170 def CalibrateData(data, ammeter_scale=1e-6):
171 for i in range(0, len(data)):
172 data[i][1] = 16.8 * float(data[i][1]) / 4000.0
173 data[i][2] = ammeter_scale * 0.170 * float(data[i][2]) / 268.0
174 data[i][3] = ammeter_scale * 0.170 * float(data[i][3]) / 268.0
177 def ShowTCS(filename, calibrate=True, normalise=False, show_error=False, plot=gnuplot.plot,w="lp", step=1):
178 if type(filename) == type(""):
179 data = GetData(filename)
185 data = CalibrateData(data)
186 units = ["V", "uA / V"]
188 units = ["DAC counts", "ADC counts / DAC counts"]
191 gnuplot("set ylabel \"dI(E)/dE ("+str(units[1])+")\"")
193 data = MaxNormalise(data)
194 gnuplot("set ylabel \"dI(E)/dE (normalised)\"")
196 gnuplot("set title \"S(E)\"")
197 gnuplot("set xlabel \"U ("+str(units[0])+")\"")
200 d = Derivative(data, 1, 2, step=step)
202 plot(Gnuplot.Data(d, using="2:3", with_=w,title="S(E) : " + str(filename)))
204 error1 = Derivative(data, 1, 2, -3,step=step)
205 error2 = Derivative(data, 1, 2, +3,step=step)
206 gnuplot.replot(Gnuplot.Data(error1, using="2:3", with_=w,title="Error : Low bound"))
207 gnuplot.replot(Gnuplot.Data(error2, using="2:3", with_=w, title="Error : Upper bound"))
211 def ShowData(filename,calibrate=True, normalise=False, show_error=False, plot=gnuplot.plot,w="lp", step=1):
212 if type(filename) == type(""):
213 data = GetData(filename)
221 data = CalibrateData(data)
224 units = ["DAC counts", "ADC counts"]
227 gnuplot("set ylabel \"I(E) ("+str(units[1])+")\"")
229 data = MaxNormalise(data)
230 gnuplot("set ylabel \"I(E) (normalised)\"")
232 gnuplot("set title \"S(E)\"")
233 gnuplot("set xlabel \"U ("+str(units[0])+")\"")
236 #d = Derivative(data, 1, 2, step=step)
238 plot(Gnuplot.Data(data, using="2:3", with_=w,title="S(E) : " + str(filename)))
240 error1 = copy.deepcopy(data)
241 error2 = copy.deepcopy(data)
242 for i in range(len(data)):
244 error1[i][2] -= 0.50*float(data[i][3])
245 error2[i][2] += 0.50*float(data[i][3])
246 gnuplot.replot(Gnuplot.Data(error1, using="2:3", with_=w,title="Error : Low bound"))
247 gnuplot.replot(Gnuplot.Data(error2, using="2:3", with_=w, title="Error : Upper bound"))
253 if (len(sys.argv) < 2):
254 sys.stderr.write(sys.argv[0] + " - Require arguments (filename)\n")
258 gnuplot("set style data lp")
259 gnuplot("set key outside right")
260 #gnuplot("set title \"Au on Si (50min 3.5A 3-6 e-8mbar)\"")
261 #gnuplot("set xlabel \"E (DAC Counts)\"")
262 #gnuplot("set ylabel \"S(E) (ADC/DAC Counts)\"")
263 #gnuplot("set term postscript colour")
264 #gnuplot("set output \"test.eps\"")
265 for i in range(1, len(sys.argv)):
266 if (len(tcs[i-1]) > 0):
267 gnuplot.replot(Gnuplot.Data(tcs[i-1], title=sys.argv[i], with_="lp"))
269 # Now average the data
275 sys.stdout.write(str(a[0]) + "\t" + str(a[1]) + "\t" + str(a[1]) + "\n")
276 gnuplot.replot(Gnuplot.Data(avg, title="Average", with_="l lw 2"))
278 sys.stdout.write("Save averaged data as (blank for no save): ")
279 filename = sys.stdin.readline().strip(" \r\n\t")
281 SaveData(filename, avg)
286 if __name__ == "__main__":