daq_info["Trans_PD2"] = {"fname": "PD.txt", "units": "V"}
daq_info["TFY_PD9"] = {"fname": "PD9.txt", "units": "V"}
daq_info["I0up_PD3"] = {"fname": "X41.txt", "units": "V"}
daq_info["I0down_PD4"] = {"fname": "X42.txt", "units": "V"}
daq_info["laserpos_h_M27"] = {"fname": "M27.txt", "units": "pulse"}
daq_info["laserpos_v_M28"] = {"fname": "M28.txt", "units": "pulse"}
run_list = []
for k in f.keys():
if k[0:3] == "run":
run_list.append(k)
for dname, v in daq_info.iteritems():
df = pd.read_csv(add_files_dir + v["fname"], header=0, names=["tag", "value"], index_col="tag", )
for r in run_list:
tags = f[str(r) + "/event_info/tag_number_list"]
red_df = df.loc[tags[0]:tags[-1]]
conv_df = btc.convert(dname, red_df[:])
dt = np.float
if v["units"] == "bool" or v["units"] == "pulse":
dt = np.int
tags_dset = fout.create_dataset(str(r) + "/daq_info/" + dname, data=conv_df, chunks=True, dtype=dt)
tags_dset.attrs["units"] = np.string_(v["units"])
# do a write here???
fout.flush()
f.close()
fout.close()
def __call__(self, i):
xs = []
ys = []
dfs = []
self.update_data()
for p_i, plot in enumerate(self.plots):
if self.data_list[p_i][plot['x']] is None:
continue
x = self.data_list[p_i][plot['x']][1]
y = self.data_list[p_i][plot['y']][1]
if plot['x'] == "Mono":
x = beamtime_converter_201406XX.convert("energy", x)
#print x, y
while len(x) != len(y):
if len(x) > len(y):
x.pop()
else:
y.pop()
dfs.append(pd.DataFrame(np.asarray([x, y]).T, columns=[plot['x'], plot['y']], ))
dfs[p_i] = dfs[p_i].set_index(plot['x'])
dfs[p_i] = dfs[p_i].sort_index()
#if not self.average:
xs.append(dfs[p_i].index.values)
ys.append(dfs[p_i].values.flatten())
#else:
# xs.append(dfs[p_i].index.values)
# ys.append(dfs[p_i].mean().values.flatten())
if self.operation == "subtract":
dftot = dfs[1].subtract(dfs[0], fill_value=0)
dftot = dftot.sort_index()
X = dftot.index
Y = dftot.values
else:
X = dfs[0].index
Y = dfs[0].values
#print "Operation not supported, exiting"
#sys.exit(-1)
if self.json_name is not None:
json_file = open(self.json_name, 'w')
json_dict = {}
json_dict["run"] = ""
json_dict["name"] = "-".join(daq_quantities.keys())
json_dict["plot_type"] = "scatter"
json_dict["label_x"] = self.plots[0]['x']
json_dict["label_y"] = self.plots[0]['y']
json_dict["data"] = []
json_dict["data"].append(X.tolist())
json_dict["data"].append(Y.tolist())
json.dump(json_dict, json_file)
json_file.close()
if self.csv_name is not None:
import csv
with open(self.csv_name, 'wb') as csvfile:
spamwriter = csv.writer(csvfile, delimiter=',',
quotechar='|', quoting=csv.QUOTE_MINIMAL)
spamwriter.writerow([self.plots[0]["x"], self.plots[0]["y"]])
for i, x_i in enumerate(X):
spamwriter.writerow([x_i, Y[i]])
csvfile.close()
if len(X) > 0 and len(Y) > 0:
#self.ax.set_xlim(0.9 * float(min(X)), 1.1 * float(max(X)))
self.ax.set_ylim(0.99 * float(min(Y)), 1.01 * float(max(Y)))
if self.average:
dftot_avg = dftot.mean(level=0)
dftot_std = dftot.std(level=0)
avg_x = dftot_avg.index.tolist()
avg_y = dftot_avg[plot['y']].values.tolist()
std_y = dftot_std[plot['y']].values.tolist()
self.line.set_data(avg_x, avg_y)
error_plus = np.array(avg_y) + np.array(std_y)
error_minus = np.array(avg_y) - np.array(std_y)
self.line.set_data(avg_x, avg_y)
for coll in (self.ax.collections):
self.ax.collections.remove(coll)
self.ax.fill_between(avg_x, error_minus, error_plus, alpha=0.5, edgecolor='#CC4F1B', facecolor='#FF9848')
else:
self.line.set_data(X.tolist(), Y.tolist())
for i, l in enumerate(self.line2):
if dfs[i].empty:
continue
if i > 0:
xmin, xmax = self.ax2.get_xlim()
ymin, ymax = self.ax2.get_ylim()
else:
xmin = dfs[i].idxmin()[self.plots[i]['y']]
xmax = dfs[i].idxmax()[self.plots[i]['y']]
ymin = dfs[i].min()[self.plots[i]['y']]
ymax = dfs[i].max()[self.plots[i]['y']]
if xmin > dfs[i].idxmin()[self.plots[i]['y']]:
xmin = dfs[i].idxmin()[self.plots[i]['y']]
if xmax < dfs[i].idxmax()[self.plots[i]['y']]:
xmax = dfs[i].idxmax()[self.plots[i]['y']]
#self.ax2.set_xlim(0.9 * float(xmin), 1.1 * float(xmax))
if ymin > dfs[i].min()[self.plots[i]['y']]:
ymin = dfs[i].min()[self.plots[i]['y']]
if ymax < dfs[i].max()[self.plots[i]['y']]:
ymax = dfs[i].max()[self.plots[i]['y']]
self.ax2.set_ylim(0.99 * float(ymin), 1.01 * float(ymax))
print dfs[i].index.values, dfs[i].values.flatten()
self.line2[i].set_data(dfs[i].index.values, dfs[i].values.flatten())
#self.line2[i].set_data(xs[i], ys[i])
return self.line, self.line2[0], self.line2[1]
def __call__(self, i):
xs = []
ys = []
dfs = []
self.update_data()
for p_i, plot in enumerate(self.plots):
if self.data_list[p_i][plot['x']] is None:
continue
x = self.data_list[p_i][plot['x']][1]
y = self.data_list[p_i][plot['y']][1]
if plot['x'] == "Mono":
x = beamtime_converter_201406XX.convert("energy", x)
#print x, y
while len(x) != len(y):
if len(x) > len(y):
x.pop()
else:
y.pop()
dfs.append(
pd.DataFrame(
np.asarray([x, y]).T,
columns=[plot['x'], plot['y']],
))
dfs[p_i] = dfs[p_i].set_index(plot['x'])
dfs[p_i] = dfs[p_i].sort_index()
#if not self.average:
xs.append(dfs[p_i].index.values)
ys.append(dfs[p_i].values.flatten())
#else:
# xs.append(dfs[p_i].index.values)
# ys.append(dfs[p_i].mean().values.flatten())
if self.operation == "subtract":
dftot = dfs[1].subtract(dfs[0], fill_value=0)
dftot = dftot.sort_index()
X = dftot.index
Y = dftot.values
else:
X = dfs[0].index
Y = dfs[0].values
#print "Operation not supported, exiting"
#sys.exit(-1)
if self.json_name is not None:
json_file = open(self.json_name, 'w')
json_dict = {}
json_dict["run"] = ""
json_dict["name"] = "-".join(list(daq_quantities.keys()))
json_dict["plot_type"] = "scatter"
json_dict["label_x"] = self.plots[0]['x']
json_dict["label_y"] = self.plots[0]['y']
json_dict["data"] = []
json_dict["data"].append(X.tolist())
json_dict["data"].append(Y.tolist())
json.dump(json_dict, json_file)
json_file.close()
if self.csv_name is not None:
import csv
with open(self.csv_name, 'wb') as csvfile:
spamwriter = csv.writer(csvfile,
delimiter=',',
quotechar='|',
quoting=csv.QUOTE_MINIMAL)
spamwriter.writerow([self.plots[0]["x"], self.plots[0]["y"]])
for i, x_i in enumerate(X):
spamwriter.writerow([x_i, Y[i]])
csvfile.close()
if len(X) > 0 and len(Y) > 0:
#self.ax.set_xlim(0.9 * float(min(X)), 1.1 * float(max(X)))
self.ax.set_ylim(0.99 * float(min(Y)), 1.01 * float(max(Y)))
if self.average:
dftot_avg = dftot.mean(level=0)
dftot_std = dftot.std(level=0)
avg_x = dftot_avg.index.tolist()
avg_y = dftot_avg[plot['y']].values.tolist()
std_y = dftot_std[plot['y']].values.tolist()
self.line.set_data(avg_x, avg_y)
error_plus = np.array(avg_y) + np.array(std_y)
error_minus = np.array(avg_y) - np.array(std_y)
self.line.set_data(avg_x, avg_y)
for coll in (self.ax.collections):
self.ax.collections.remove(coll)
self.ax.fill_between(avg_x,
error_minus,
error_plus,
alpha=0.5,
edgecolor='#CC4F1B',
facecolor='#FF9848')
else:
self.line.set_data(X.tolist(), Y.tolist())
for i, l in enumerate(self.line2):
if dfs[i].empty:
continue
if i > 0:
xmin, xmax = self.ax2.get_xlim()
ymin, ymax = self.ax2.get_ylim()
else:
xmin = dfs[i].idxmin()[self.plots[i]['y']]
xmax = dfs[i].idxmax()[self.plots[i]['y']]
ymin = dfs[i].min()[self.plots[i]['y']]
ymax = dfs[i].max()[self.plots[i]['y']]
if xmin > dfs[i].idxmin()[self.plots[i]['y']]:
xmin = dfs[i].idxmin()[self.plots[i]['y']]
if xmax < dfs[i].idxmax()[self.plots[i]['y']]:
xmax = dfs[i].idxmax()[self.plots[i]['y']]
#self.ax2.set_xlim(0.9 * float(xmin), 1.1 * float(xmax))
if ymin > dfs[i].min()[self.plots[i]['y']]:
ymin = dfs[i].min()[self.plots[i]['y']]
if ymax < dfs[i].max()[self.plots[i]['y']]:
ymax = dfs[i].max()[self.plots[i]['y']]
self.ax2.set_ylim(0.99 * float(ymin), 1.01 * float(ymax))
print(dfs[i].index.values, dfs[i].values.flatten())
self.line2[i].set_data(dfs[i].index.values,
dfs[i].values.flatten())
#self.line2[i].set_data(xs[i], ys[i])
return self.line, self.line2[0], self.line2[1]
