class ExceptionHandler(QtCore.QObject):
errorSignal = QtCore.pyqtSignal()
silentSignal = QtCore.pyqtSignal()
def __init__(self):
super(ExceptionHandler, self).__init__()
def handler(self, exctype, value, traceback):
global failure_
self.errorSignal.emit()
failure_ = (exctype, value, traceback)
sys._excepthook(exctype, value, traceback)
class ComponentsList(QtCore.QObject):
'''
Keep track of Components in a list and emit signals.
Methods 'append' and 'remove' are provided and emit signals,
direct access to the list is allowed with 'ComponentList.list',
but not recommended.'''
componentAppended = QtCore.pyqtSignal(object, name="ComponentAppended")
componentRemoved = QtCore.pyqtSignal(object, name="ComponentRemoved")
def __init__(self):
super(ComponentsList, self).__init__()
self.list = []
def append(self, item):
self.list.append(item)
self.componentAppended.emit(item)
def remove(self, item):
self.list.remove(item)
self.componentRemoved.emit(item)
def index(self, item):
return self.list.index(item)
def __delitem__(self, key):
self.list.__delitem__(key)
def __getitem__(self, key):
return self.list.__getitem__(key)
def __setitem__(self, key, value):
self.list.__setitem__(key, value)
def __len__(self):
return len(self.list)
def __repr__(self):
return self.list.__repr__()
class Trace(QtCore.QThread):
""" timer for the IOC measurement"""
##--------Signal Definition--------##
started = QtCore.pyqtSignal()
finished = QtCore.pyqtSignal('PyQt_PyObject')
##--------CONSTRUCTOR--------##
def __init__(self,interval): # t--interval call the hFunction
QtCore.QThread.__init__(self)
self.interval = interval
self.evented = threading.Event() # 引入事件进行定时器的设置
self.trace_points = None
self.trace_time = None
self.trace_value = {}
self.trace_triggerlevel = None
self.trace_offsettime = None
#Execute
def run(self):
self.started.emit()
while not self.evented.is_set():
self.evented.wait(self.interval)
#self.trace_points = caget('SRFLab-010:RFS-PM-01:Trace_points.VAL', as_string=False)
self.trace_time = caget('SRFLab-010:RFS-PM-01:Trace_time.VAL', as_string=False)
self.trace_value = caget('SRFLab-010:RFS-PM-01:Value_trace.VAL', as_string=False)
self.trace_triggerlevel = caget('SRFLab-010:RFS-PM-01:Trace_level_callback_DBM.VAL', as_string=False)
self.trace_offsettime = caget('SRFLab-010:RFS-PM-01:Trace_offset_time_callback.VAL',as_string=False)
self.trace_points = len(self.trace_value)
psen = TraceMeasure()
psen._array = self.trace_value
psen._points = self.trace_points
psen._time = self.trace_time
psen._triggerlevel = self.trace_triggerlevel
psen._offsetTime = self.trace_offsettime
self.finished.emit(psen)
#STOP
def cancel(self):
self.evented.set()
class HelpScreen(QMainWindow):
clicked = QtCore.pyqtSignal()
def __init__(self, parent=None):
super(HelpScreen, self).__init__()
# Object for data persistence
# self.settings = QtCore.QSettings('UHSLC', 'com.uhslc.qcsoft')
# st.SETTINGS.remove("savepath")
self.ui = parent
# If a save path hasn't been defined, give it a home directory
if (st.get_path(st.SAVE_KEY)):
self.ui.lineEditPath.setPlaceholderText(st.get_path(st.SAVE_KEY))
else:
st.SETTINGS.setValue(st.SAVE_KEY, os.path.expanduser('~'))
self.ui.lineEditPath.setPlaceholderText(os.path.expanduser('~'))
self.ui.lineEditLoadPath.setPlaceholderText(st.get_path(st.LOAD_KEY))
# If a fast delivery save path hasn't been defined, give it a home directory
if (st.get_path(st.FD_PATH)):
self.ui.lineEditFDPath.setPlaceholderText(st.get_path(st.FD_PATH))
else:
st.SETTINGS.setValue(st.FD_PATH, os.path.expanduser('~'))
self.ui.lineEditFDPath.setPlaceholderText(os.path.expanduser('~'))
# If a high frequency data save path hasn't been defined, give it a home directory
if (st.get_path(st.HF_PATH)):
self.ui.lineEditHFPath.setPlaceholderText(st.get_path(st.HF_PATH))
else:
st.SETTINGS.setValue(st.HF_PATH, os.path.expanduser('~'))
self.ui.lineEditHFPath.setPlaceholderText(os.path.expanduser('~'))
if st.get_path(st.DIN_PATH):
self.ui.lineEdit_din.setPlaceholderText(st.get_path(st.DIN_PATH))
saveButton = self.ui.pushButton_save_folder
loadButton = self.ui.pushButton_load_folder
dinSave = self.ui.pushButton_din
FDSave = self.ui.pushButton_fd_folder
hf_save = self.ui.pushButton_hf_data
saveButton.clicked.connect(
lambda: self.savePath(self.ui.lineEditPath, st.SAVE_KEY))
loadButton.clicked.connect(
lambda: self.savePath(self.ui.lineEditLoadPath, st.LOAD_KEY))
dinSave.clicked.connect(
lambda: self.saveDIN(self.ui.lineEdit_din, st.DIN_PATH))
FDSave.clicked.connect(
lambda: self.savePath(self.ui.lineEditFDPath, st.FD_PATH))
hf_save.clicked.connect(
lambda: self.savePath(self.ui.lineEditFDPath, st.HF_PATH))
def savePath(self, lineEditObj, setStr):
folder_name = QtWidgets.QFileDialog.getExistingDirectory(
self, 'Select a Folder')
if (folder_name):
st.SETTINGS.setValue(setStr, folder_name)
st.SETTINGS.sync()
lineEditObj.setPlaceholderText(st.get_path(setStr))
lineEditObj.setText("")
else:
pass
def saveDIN(self, lineEditObj, setStr):
filters = "*.din"
if st.DIN_PATH:
path = st.DIN_PATH
else:
path = os.path.expanduser('~')
file_name = QtWidgets.QFileDialog.getOpenFileNames(
self, 'Open File', path, filters)
if (file_name):
st.SETTINGS.setValue(setStr, file_name[0][0])
st.SETTINGS.sync()
lineEditObj.setPlaceholderText(st.get_path(setStr))
lineEditObj.setText("")
else:
pass
class Start(QMainWindow):
clicked = QtCore.pyqtSignal()
def __init__(self, parent=None):
super(Start, self).__init__()
self.ui = parent
self.sens_objects = {
} ## Collection of Sensor objects for station for one month
self.home()
def home(self):
print("HOME CALLED")
# print("static_canvas",self.static_canvas)
self.ui.mplwidget_top.canvas.figure.clf()
self.ui.mplwidget_bottom.canvas.figure.clf()
self._static_ax = self.ui.mplwidget_top.canvas.figure.subplots()
self._static_fig = self.ui.mplwidget_top.canvas.figure
self.pid = -99
self.cid = -98
self.toolbar1 = self._static_fig.canvas.toolbar # Get the toolbar handler
self.toolbar1.update() # Update the toolbar memory
# self._residual_fig = self.ui.mplwidget_bottom.canvas.figure
self._residual_ax = self.ui.mplwidget_bottom.canvas.figure.subplots()
self.ui.save_btn.clicked.connect(self.save_to_ts_files)
self.ui.ref_level_btn.clicked.connect(self.show_ref_dialog)
def make_sensor_buttons(self, sensors):
# Remove all sensor checkbox widgets from the layout
# every time new data is loaded
for i in range(self.ui.verticalLayout_left_top.count()):
item = self.ui.verticalLayout_left_top.itemAt(i)
# self.verticalLayout_left_top.removeWidget(item.widget())
widget = item.widget()
if widget is not None:
widget.deleteLater()
for i in range(self.ui.verticalLayout_bottom.count()):
item = self.ui.verticalLayout_bottom.itemAt(i)
# self.verticalLayout_left_top.removeWidget(item.widget())
widget = item.widget()
if widget is not None:
widget.deleteLater()
# sensors' keys are names of all sensors which carry
# all of the data associated with it
# Make copy of it so we can use its keys and assign radio buttons to it
# If we do not make a copy then the sensors values would get
# overwritten by radio button objects
self.sensor_dict = dict(sensors)
self.sensor_dict2 = dict(sensors)
# Counter added to figure out when the last item was added
# Set alignment of the last item to push all the radio buttons up
counter = len(sensors.items())
for key, value in sensors.items():
counter -= 1
self.sensor_radio_btns = QtWidgets.QRadioButton(key, self)
self.sensor_check_btns = QtWidgets.QCheckBox(key, self)
self.sensor_dict[key] = self.sensor_radio_btns
self.sensor_dict2[key] = self.sensor_check_btns
self.ui.buttonGroup_data.addButton(self.sensor_dict[key])
self.ui.buttonGroup_residual.addButton(self.sensor_dict2[key])
if (counter > 0):
self.ui.verticalLayout_left_top.addWidget(
self.sensor_dict[key])
self.ui.verticalLayout_bottom.addWidget(self.sensor_dict2[key])
else:
self.ui.verticalLayout_left_top.addWidget(
self.sensor_dict[key], 0, QtCore.Qt.AlignTop)
self.ui.verticalLayout_bottom.addWidget(
self.sensor_dict2[key], 0, QtCore.Qt.AlignTop)
self.sensor_dict[key].setText(key)
# radio_btn_HF = QtWidgets.QRadioButton("Minute", self)
# radio_btn_HF.setChecked(True)
self.mode = self.ui.radioButton_Minute.text()
self.sensor_dict["PRD"].setChecked(True)
self.sens_str = "PRD"
# self.sensor_dict2["PRD"].setEnabled(False)
self.sensor_dict2["ALL"].setEnabled(False)
self.plot(all=False)
self.ui.buttonGroup_data.buttonClicked.connect(self.on_sensor_changed)
self.ui.buttonGroup_residual.buttonClicked.connect(
self.on_residual_sensor_changed)
self.ui.buttonGroup_resolution.buttonClicked.connect(
self.on_frequency_changed)
def on_sensor_changed(self, btn):
print(btn.text())
if (btn.text() == "ALL"):
# TODO: plot_all and plot should be merged to one function
self.ui.save_btn.setEnabled(False)
self.ui.ref_level_btn.setEnabled(False)
self.plot(all=True)
else:
self.ui.save_btn.setEnabled(True)
self.ui.ref_level_btn.setEnabled(True)
self.sens_str = btn.text()
self._update_top_canvas(btn.text())
self.ui.lineEdit.setText(
self.sens_objects[self.sens_str].header[0])
self.update_graph_values()
# Update residual buttons and graph when the top sensor is changed
# self.on_residual_sensor_changed(None)
# Clear residual buttons and graph when the top sensor is changed
for button in self.ui.buttonGroup_residual.buttons():
button.setChecked(False)
self._residual_ax.cla()
self._residual_ax.figure.canvas.draw()
# print("ref height:",self.sens_objects[self.sens_str].height)
def on_frequency_changed(self, btn):
print("Frequency changed", btn.text())
self.mode = btn.text()
if (self.mode == "Minute"):
self.sensor_dict2["PRD"].setEnabled(True)
else:
self.sensor_dict2["PRD"].setEnabled(False)
self.on_residual_sensor_changed()
def update_graph_values(self):
# convert 'nans' back to 9999s
nan_ind = np.argwhere(np.isnan(self.browser.data))
self.browser.data[nan_ind] = 9999
# we want the sensor data object to point to self.browser.data and not self.browser.data.copy()
# because when the self.browser.data is modified on the graph
# the sensor data object will automatically be modified as well
self.sens_objects[self.sens_str].data = self.browser.data
def on_residual_sensor_changed(self):
self._residual_ax.cla()
self._residual_ax.figure.canvas.draw()
checkedItems = [
button for button in self.ui.buttonGroup_residual.buttons()
if button.isChecked()
]
if (checkedItems):
for button in checkedItems:
self.calculate_and_plot_residuals(self.sens_str, button.text(),
self.mode)
else:
self._residual_ax.cla()
self._residual_ax.figure.canvas.draw()
def plot(self, all=False):
# Set the data browser object to NoneType on every file load
self.browser = None
self._static_ax.cla()
self._residual_ax.cla()
self._residual_ax.figure.canvas.draw()
if all:
lineEditText = 'No Header -- Plotting all sensors'
sens_objects = self.sens_objects
title = 'Relative levels = signal - average over selected period'
else:
lineEditText = self.sens_objects[self.sens_str].header[0]
sens_objects = [self.sens_str]
title = 'Tide Prediction'
self.ui.lineEdit.setText(lineEditText)
for sens in sens_objects:
## Set 9999s to NaN so they don't show up on the graph
## when initially plotted
## nans are converted back to 9999s when file is saved
if sens == "ALL":
pass
else:
data_flat = self.sens_objects[sens].get_flat_data()
time = self.sens_objects[sens].get_time_vector()
nines_ind = np.where(data_flat == 9999)
data_flat[nines_ind] = float('nan')
if all:
mean = np.nanmean(data_flat)
else:
mean = 0
# t = np.linspace(0, 10, 501)
# t = np.arange(data.size)
t = time
y = data_flat - mean
# self._static_ax.plot(t, np.tan(t), ".")
line, = self._static_ax.plot(
t, y, '-', picker=5, lw=0.5,
markersize=3) # 5 points tolerance
# self._static_fig = self.static_canvas.figure
if all:
line.set_label(sens)
self._static_ax.legend()
self._static_ax.set_title(title)
self._static_ax.autoscale(enable=True, axis='both', tight=True)
self._static_ax.set_xlim([t[0], t[-1]])
self._static_ax.margins(0.05, 0.05)
self.ui.mplwidget_top.canvas.setFocusPolicy(
QtCore.Qt.ClickFocus)
self.ui.mplwidget_top.canvas.setFocus()
self.ui.mplwidget_top.canvas.figure.tight_layout()
# self.toolbar1 = self._static_fig.canvas.toolbar #Get the toolbar handler
self.toolbar1.update() # Update the toolbar memory
self.ui.mplwidget_top.canvas.draw()
def calculate_and_plot_residuals(self, sens_str1, sens_str2, mode):
# resample_freq = min(int(self.sens_objects[sens_str1].rate), int(self.sens_objects[sens_str2].rate))
# _freq = str(resample_freq)+'min'
#
# # Get a date range to create pandas time Series
# # using the sampling frequency of the sensor
# rng1 = date_range(self.sens_objects[sens_str1].date, periods = self.sens_objects[sens_str1].data.size, freq=_freq)
# ts1 = Series(self.sens_objects[sens_str1].data, rng1)
#
# rng2 = date_range(self.sens_objects[sens_str2].date, periods = self.sens_objects[sens_str2].data.size, freq=_freq)
# ts2 = Series(self.sens_objects[sens_str2].data, rng2)
# resample the data and linearly interpolate the missing values
# upsampled = ts.resample(_freq)
# interp = upsampled.interpolate()
if mode == "Hourly":
data_obj = {}
# data_obj["prd"]={'time':filt.datenum2(self.sens_objects["PRD"].get_time_vector()), 'station':'014', 'sealevel':self.sens_objects["PRD"].get_flat_data().copy()}
# for key in self.sens_objects.keys():
# print("KEY", key)
sl_data = self.sens_objects[sens_str1].get_flat_data().copy()
sl_data = self.remove_9s(sl_data)
sl_data = sl_data - int(self.sens_objects[sens_str1].height)
data_obj[sens_str1.lower()] = {
'time':
filt.datenum2(self.sens_objects[sens_str1].get_time_vector()),
'station':
'014',
'sealevel':
sl_data
}
sl_data2 = self.sens_objects[sens_str2].get_flat_data().copy()
sl_data2 = self.remove_9s(sl_data2)
sl_data2 = sl_data2 - int(self.sens_objects[sens_str2].height)
data_obj[sens_str2.lower()] = {
'time':
filt.datenum2(self.sens_objects[sens_str2].get_time_vector()),
'station':
'014',
'sealevel':
sl_data2
}
year = self.sens_objects[sens_str2].date.astype(object).year
month = self.sens_objects[sens_str2].date.astype(object).month
data_hr = filt.hr_process_2(
data_obj, filt.datetime(year, month, 1, 0, 0, 0),
filt.datetime(year, month + 1, 1, 0, 0, 0))
if sens_str1 != "PRD":
hr_resid = data_hr[sens_str1.lower()]["sealevel"] - data_hr[
sens_str2.lower()]["sealevel"]
time = [
filt.matlab2datetime(tval[0])
for tval in data_hr[list(data_hr.keys())[0]]['time']
]
self.generic_plot(self.ui.mplwidget_bottom.canvas,
time,
hr_resid,
sens_str1,
sens_str2,
"Hourly Residual",
is_interactive=False)
else:
self.show_custom_message(
"Warning",
"For hourly residual an actual channel needs to be selected in the top plot."
)
self.generic_plot(
self.ui.mplwidget_bottom.canvas, [0], [0],
sens_str1,
sens_str2,
"Choose a channel in the top plot other than PRD",
is_interactive=False)
else:
newd1 = self.resample2(sens_str1)
newd2 = self.resample2(sens_str2)
# newd1 = ts1.resample(_freq).interpolate()
# newd2 = ts2.resample(_freq).interpolate()
if (newd1.size > newd2.size):
resid = newd2 - newd1[:newd2.size]
else:
resid = newd1 - newd2[:newd1.size]
# time = np.array([self.sens_objects[sens_str1].date + np.timedelta64(i*int(1), 'm') for i in range(resid.size)])
# time = np.arange(resid.size)
time = date_range(self.sens_objects[sens_str1].date,
periods=resid.size,
freq='1min')
self.generic_plot(self.ui.mplwidget_bottom.canvas,
time,
resid,
sens_str1,
sens_str2,
"Residual",
is_interactive=False)
def generic_plot(self, canvas, x, y, sens1, sens2, title, is_interactive):
print("GENERIC PLOT CALLED")
# self._residual_ax = canvas.figure.subplots()
line, = self._residual_ax.plot(x,
y,
'-',
picker=5,
lw=0.5,
markersize=3) # 5 points tolerance
line.set_gid(sens2)
self._residual_fig = canvas.figure
self._residual_ax.set_title(title)
line.set_label(title + ": " + sens1 + " - " + sens2)
self._residual_ax.autoscale(enable=True, axis='both', tight=True)
self._residual_ax.set_xlim([x[0], x[-1]])
self._residual_ax.margins(0.05, 0.05)
self._residual_ax.legend()
if (is_interactive):
self.browser = PointBrowser(x, y, self._residual_ax, line,
self._residual_fig,
self.find_outliers(x, y, sens1))
self.browser.onDataEnd += self.show_message
canvas.mpl_connect('pick_event', self.browser.onpick)
canvas.mpl_connect('key_press_event', self.browser.onpress)
## need to activate focus onto the mpl canvas so that the keyboard can be used
canvas.setFocusPolicy(QtCore.Qt.ClickFocus)
canvas.setFocus()
self._residual_ax.figure.tight_layout()
self.toolbar2 = self._residual_fig.canvas.toolbar # Get the toolbar handler
self.toolbar2.update() # Update the toolbar memory
self._residual_ax.figure.canvas.draw()
def _update_top_canvas(self, sens):
data_flat = self.sens_objects[sens].get_flat_data()
nines_ind = np.where(data_flat == 9999)
# nonines_data = data_flat.copy()
# nonines_data[nines_ind] = float('nan')
# data_flat = nonines_data
# data_flat =data_flat - np.nanmean(data_flat)
if (len(nines_ind[0]) < data_flat.size):
# data_flat[nines_ind] = float('nan')
pass
else:
self.show_custom_message("Warning",
"The following sensor has no data")
self._static_ax.clear()
# disconnect canvas pick and press events when a new sensor is selected
# to eliminate multiple callbacks on sensor change
# self.static_canvas.mpl_disconnect(self.pidP)
# self.static_canvas.mpl_disconnect(self.cidP)
self.ui.mplwidget_top.canvas.mpl_disconnect(self.pid)
self.ui.mplwidget_top.canvas.mpl_disconnect(self.cid)
if self.browser:
self.browser.onDataEnd -= self.show_message
self.browser.disconnect()
# time = np.arange(data_flat.size)
time = self.sens_objects[sens].get_time_vector()
self.line, = self._static_ax.plot(time,
data_flat,
'-',
picker=5,
lw=0.5,
markersize=3)
self._static_ax.set_title(
'select a point you would like to remove and press "D"')
self.browser = PointBrowser(time, data_flat, self._static_ax,
self.line, self._static_fig,
self.find_outliers(time, data_flat, sens))
self.browser.onDataEnd += self.show_message
self.browser.on_sensor_change_update()
# update event ids so that they can be disconnect on next sensor change
self.pid = self.ui.mplwidget_top.canvas.mpl_connect(
'pick_event', self.browser.onpick)
self.cid = self.ui.mplwidget_top.canvas.mpl_connect(
'key_press_event', self.browser.onpress)
## need to activate focus onto the mpl canvas so that the keyboard can be used
self.toolbar1.update()
self.ui.mplwidget_top.canvas.setFocusPolicy(QtCore.Qt.ClickFocus)
self.ui.mplwidget_top.canvas.setFocus()
def find_outliers(self, t, data, sens):
channel_freq = self.sens_objects[sens].rate
_freq = channel_freq + 'min'
nines_ind = np.where(data == 9999)
nonines_data = data.copy()
nonines_data[nines_ind] = float('nan')
# Get a date range to create pandas time Series
# using the sampling frequency of the sensor
rng = date_range(t[0], t[-1], freq=_freq)
ts = Series(nonines_data, rng)
# resample the data and linearly interpolate the missing values
upsampled = ts.resample(_freq)
interp = upsampled.interpolate()
# calculate a window size for moving average routine so the window
# size is always 60 minutes long
window_size = 60 // int(channel_freq)
# calculate moving average including the interolated data
# moving_average removes big outliers before calculating moving average
y_av = self.moving_average(np.asarray(interp.tolist()), window_size)
# y_av = self.moving_average(data, 30)
# missing=np.argwhere(np.isnan(y_av))
# y_av[missing] = np.nanmean(y_av)
# calculate the residual between the actual data and the moving average
# and then find the data that lies outside of sigma*std
residual = nonines_data - y_av
std = np.nanstd(residual)
sigma = 3.0
itemindex = np.where((nonines_data > y_av + (sigma * std))
| (nonines_data < y_av - (sigma * std)))
return itemindex
def moving_average(self, data, window_size):
""" Computes moving average using discrete linear convolution of two one dimensional sequences.
Args:
-----
data (pandas.Series): independent variable
window_size (int): rolling window size
Returns:
--------
ndarray of linear convolution
References:
------------
[1] Wikipedia, "Convolution", http://en.wikipedia.org/wiki/Convolution.
[2] API Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.convolve.html
"""
# REMOVE GLOBAL OUTLIERS FROM MOVING AVERAGE CALCULATION nk
filtered_data = data.copy()
# my_mad=np.nanmedian(np.abs(filtered_data-np.nanmedian(filtered_data)))
# my_mean=np.nanmean(filtered_data)
my_mean = np.nanmean(filtered_data)
my_std = np.nanstd(filtered_data)
# itemindex = np.where(((filtered_data>my_mean+4*my_mad ) | (filtered_data<my_mean-4*my_mad)))
itemindex = np.where(((filtered_data > my_mean + 3 * my_std) |
(filtered_data < my_mean - 3 * my_std)))
filtered_data[itemindex] = np.nanmean(filtered_data)
# Fix boundary effects by adding prepending and appending values to the data
filtered_data = np.insert(
filtered_data, 0,
np.ones(window_size) *
np.nanmean(filtered_data[:window_size // 2]))
filtered_data = np.insert(
filtered_data, filtered_data.size,
np.ones(window_size) *
np.nanmean(filtered_data[-window_size // 2:]))
window = np.ones(int(window_size)) / float(window_size)
# return (np.convolve(filtered_data, window, 'same')[window_size:-window_size],itemindex)
return np.convolve(filtered_data, window,
'same')[window_size:-window_size]
def resample2(self, sens_str):
data = self.sens_objects[sens_str].data.copy()
nines_ind = np.where(data == 9999)
data[nines_ind] = float('nan')
ave = np.nanmean(data)
datas = data[0:-1] - ave # int(self.sens_objects[sens_str].height)
datae = data[1:] - ave # int(self.sens_objects[sens_str].height)
yc = (datae - datas) / int(self.sens_objects[sens_str].rate)
min_data = []
for j in range(0, len(datas)):
for i in range(0, int(self.sens_objects[sens_str].rate)):
min_data.append(float(datas[j] + yc[j]))
# nan_ind = np.argwhere(np.isnan(min_data))
# min_data[nan_ind] = 9999
return np.asarray(min_data)
def show_message(self, *args):
print("SHOW MESSAGE", *args)
# choice = QtWidgets.QMessageBox.information(self, 'The end of data has been reached', 'The end of data has been reached', QtWidgets.QMessageBox.Ok)
self.show_custom_message(*args, *args)
def show_ref_dialog(self):
try:
self.browser
except AttributeError:
self.show_custom_message("Error!",
"Data needs to be loaded first.")
return
else:
if (self.is_digit(str(self.ui.refLevelEdit.text()))):
# text, result = QtWidgets.QInputDialog.getText(self, 'My Input Dialog', 'Enter start date and time:')
date, time, result = DateDialog.getDateTime(self)
ISOstring = date.toString('yyyy-MM-dd') + 'T' + time.toString(
"HH:mm")
if result:
REF_diff = int(str(self.ui.refLevelEdit.text())) - int(
self.sens_objects[self.sens_str].height)
new_REF = REF_diff + int(
self.sens_objects[self.sens_str].height)
# offset the data
self.browser.offset_data(ISOstring, REF_diff)
# format the new reference to a 4 character string (i.e add leading zeros if necessary)
# update the header
new_header = self.sens_objects[self.sens_str].header[0][:60] + '{:04d}'.format(new_REF) + \
self.sens_objects[self.sens_str].header[0][64:]
self.sens_objects[self.sens_str].header[0] = new_header
self.ui.lineEdit.setText(
self.sens_objects[self.sens_str].header[0])
print("Succesfully changed to: ",
str(self.ui.refLevelEdit.text()))
else:
self.show_custom_message("Error!",
"The value entered is not a number.")
return
def is_digit(self, n):
try:
int(n)
return True
except ValueError:
return False
def remove_9s(self, data):
nines_ind = np.where(data == 9999)
data[nines_ind] = float('nan')
return data
def show_custom_message(self, title, descrip):
choice = QtWidgets.QMessageBox.information(self, title, descrip,
QtWidgets.QMessageBox.Ok)
def save_to_ts_files(self):
# Deleting tkey "ALL" from the list of sensors
if "ALL" in self.sens_objects:
del self.sens_objects["ALL"]
if (self.sens_objects):
months = len(
self.sens_objects["PRD"].line_num) # amount of months loaded
# print("Amount of months loaded", months)
assem_data = [
[] for j in range(months)
] # initial an empty list of lists with the number of months
nan_ind = np.argwhere(np.isnan(self.browser.data))
# print("NAN INDICES",nan_ind)
# self.browser.data[nan_ind] = 9999
# self.sens_objects[self.sens_str].data = self.browser.data
# separate PRD from the rest because it has to be saved on the top file
# Because dictionaries are unordered
prd_list = [[] for j in range(months)]
# Cycle through each month loaded
for m in range(months):
# Cycle through each month loaded, where key is the sensor name
# Use value instead of self.sens_objects[key]?
for key, value in self.sens_objects.items():
# Add header
# separate PRD from the rest because it has to be saved on the top file
if (key == "PRD"):
prd_list[m].append(
self.sens_objects[key].header[m].strip("\n"))
else:
assem_data[m].append(
self.sens_objects[key].header[m].strip("\n"))
# The ugly range is calculating start and end line numbers for each month that was Loaded
# so that the data can be saved to separate, monthly files
for i in range(
sum(self.sens_objects[key].line_num[:]) -
sum(self.sens_objects[key].line_num[m:]),
sum(self.sens_objects[key].line_num[:]) -
sum(self.sens_objects[key].line_num[m:]) +
self.sens_objects[key].line_num[m]):
# File formatting is differs based on the sampling rate of a sensor
if (int(self.sens_objects[key].rate) >= 5):
# Get only sealevel reading, without anything else (no time/date etc)
data = ''.join(
'{:5.0f}'.format(e)
for e in self.sens_objects[key].data.flatten()
[i * 12:12 + i * 12].tolist())
# The columns/rows containing only time/data and no sealevel measurements
it_col_formatted = ' ' + self.sens_objects[key].type + ' ' + \
self.sens_objects[key].time_info[i][8:12].strip()[-2:] + \
self.sens_objects[key].time_info[i][12:20]
# assem_data.append(info_time_col[i][0:]+data)
if (key == "PRD"):
prd_list[m].append(''.join(it_col_formatted) +
data)
else:
assem_data[m].append(
''.join(it_col_formatted) + data)
else:
data = ''.join(
'{:4.0f}'.format(e)
for e in self.sens_objects[key].data.flatten()
[i * 15:15 + i * 15].tolist())
it_col_formatted = ' ' + self.sens_objects[key].type + ' ' + \
self.sens_objects[key].time_info[i][8:12].strip()[-2:] + \
self.sens_objects[key].time_info[i][12:20]
# assem_data.append(info_time_col[i][0:]+data)
assem_data[m].append(''.join(it_col_formatted) +
data)
if (key == "PRD"):
prd_list[m].append('9' * 80)
else:
assem_data[m].append('9' * 80)
del data
# find the start date lines of each monp file that was loaded
date_str = self.sens_objects[key].time_info[
sum(self.sens_objects[key].line_num[:]) -
sum(self.sens_objects[key].line_num[m:])]
month_int = int(date_str[12:14][-2:])
month_str = "{:02}".format(month_int)
year_str = date_str[8:12][-2:]
station_num = self.sens_objects[key].type[0:-3]
file_name = 't' + station_num + year_str + month_str
file_extension = '.dat'
try:
with open(
st.get_path(st.SAVE_KEY) + '/' + file_name +
file_extension, 'w') as the_file:
for lin in prd_list[m]:
the_file.write(lin + "\n")
for line in assem_data[m]:
the_file.write(line + "\n")
# Each file ends with two lines of 80 9s that's why adding an additional one
the_file.write('9' * 80 + "\n")
self.show_custom_message(
"Success", "Success \n" + file_name + file_extension +
" Saved to " + st.get_path(st.SAVE_KEY) + "\n")
except IOError as e:
self.show_custom_message(
"Error", "Cannot Save to " + st.get_path(st.SAVE_KEY) +
"\n" + str(e) +
"\n Please select a different path to save to")
self.save_fast_delivery(self.sens_objects)
self.save_mat_high_fq(file_name)
# if result == True:
# print("Succesfully changed to: ", str(self.refLevelEdit.text()))
else:
self.show_custom_message("Warning", "You haven't loaded any data.")
# this function is called for every month of data loaded
def save_mat_high_fq(self, file_name):
import scipy.io as sio
if st.get_path(st.HF_PATH):
save_path = st.get_path(st.HF_PATH)
else:
self.show_custom_message(
"Warning",
"Please select a location where you would like your high "
"frequency matlab data "
"to be saved. Click save again once selected.")
return
for key, value in self.sens_objects.items():
sl_data = self.sens_objects[key].get_flat_data().copy()
sl_data = self.remove_9s(sl_data)
sl_data = sl_data - int(self.sens_objects[key].height)
time = filt.datenum2(self.sens_objects[key].get_time_vector())
data_obj = [time, sl_data]
# transposing the data so that it matches the shape of the UHSLC matlab format
matlab_obj = {
'NNNN': file_name + key.lower(),
file_name + key.lower(): np.transpose(data_obj, (1, 0))
}
try:
sio.savemat(save_path + '/' + file_name + key.lower() + '.mat',
matlab_obj)
self.show_custom_message(
"Success", "Success \n" + file_name + key.lower() +
'.mat' + " Saved to " + st.get_path(st.HF_PATH) + "\n")
except IOError as e:
self.show_custom_message(
"Error", "Cannot Save to high frequency (.mat) data to" +
st.get_path(st.HF_PATH) + "\n" + str(e) +
"\n Please select a different path to save to")
def save_fast_delivery(self, _data):
import scipy.io as sio
# 1. Check if the .din file was added and that it still exist at that path
# b) also check that a save folder is set up
# 2. If it does. load in the primary channel for our station
# 3. If it does not exist, display a warning message on how to add it and that the FD data won't be saved
# 4. Perform filtering and save
din_path = None
save_path = None
if st.get_path(st.DIN_PATH):
din_path = st.get_path(st.DIN_PATH)
else:
self.show_custom_message(
"Warning",
"The fast delivery data cannot be processed because you haven't selected"
"the .din file location. Press F1 to access the menu to select it. And "
"then click the save button again.")
return
if st.get_path(st.FD_PATH):
save_path = st.get_path(st.FD_PATH)
else:
self.show_custom_message(
"Warning",
"Please select a location where you would like your hourly and daily data"
"to be saved. Click save again once selected.")
return
data_obj = {}
station_num = _data["PRD"].type[0:-3]
primary_sensor = filt.get_channel_priority(
din_path, station_num)[0].upper(
) # returns multiple sensor in order of importance
if primary_sensor not in _data:
self.show_custom_message(
"Error", f"Your .din file says that {primary_sensor} "
f"is the primary sensor but the file you have loaded does "
f"not contain that sensor. Hourly and daily data will not be saved."
)
return
sl_data = _data[primary_sensor].get_flat_data().copy()
sl_data = self.remove_9s(sl_data)
sl_data = sl_data - int(_data[primary_sensor].height)
data_obj[primary_sensor.lower()] = {
'time': filt.datenum2(_data[primary_sensor].get_time_vector()),
'station': station_num,
'sealevel': sl_data
}
year = _data[primary_sensor].date.astype(object).year
month = _data[primary_sensor].date.astype(object).month
# Filter to hourly
data_hr = filt.hr_process_2(data_obj,
filt.datetime(year, month, 1, 0, 0, 0),
filt.datetime(year, month + 1, 1, 0, 0, 0))
# for channel parameters see filt.channel_merge function
# We are not actually merging channels here (not needed for fast delivery)
# But we still need to run the data through the merge function, even though we are only using one channel
# in order to get the correct output data format suitable for the daily filter
ch_params = [{primary_sensor.lower(): 0}]
hourly_merged = filt.channel_merge(data_hr, ch_params)
# Note that hourly merged returns a channel attribute which is an array of integers representing channel type.
# used for a particular day of data. In Fast delivery, all the number should be the same because no merge
# int -> channel name mapping is inside of filtering.py var_flag function
data_day = filt.day_119filt(hourly_merged,
_data[primary_sensor].location[0])
month_str = "{:02}".format(month)
hourly_filename = save_path + '/' + 'th' + str(station_num) + str(
year)[-2:] + month_str + '.mat'
daily_filename = save_path + '/' + 'da' + str(station_num) + str(
year)[-2:] + month_str + '.mat'
sio.savemat(hourly_filename, data_hr)
sio.savemat(daily_filename, data_day)
self.show_custom_message(
"Success", "Success \n Hourly and Daily Date Saved to " +
st.get_path(st.FD_PATH) + "\n")
monthly_mean = np.round(np.nanmean(data_day['sealevel'])).astype(int)
# Remove nans, replace with 9999 to match the legacy files
nan_ind = np.argwhere(np.isnan(data_day['sealevel']))
data_day['sealevel'][nan_ind] = 9999
sl_round_up = np.round(data_day['sealevel']).astype(
int) # round up sealevel data and convert to int
# right justify with 5 spaces
sl_str = [str(x).rjust(5, ' ')
for x in sl_round_up] # convert data to string
daily_filename = save_path + '/' + 'da' + str(station_num) + str(
year)[-2:] + month_str + '.dat'
# format the date and name strings to match the legacy .dat format
month_str = str(month).rjust(2, ' ')
station_name = _data[primary_sensor].name.ljust(7)
line_begin_str = f'{station_name}WOC {year}{month_str}'
counter = 1
try:
with open(daily_filename, 'w') as the_file:
for i, sl in enumerate(sl_str):
if i % 11 == 0:
line_str = line_begin_str + str(
counter) + " " + ''.join(sl_str[i:i + 11])
if counter == 3:
line_str = line_str.ljust(75)
final_str = line_str[:-5] + str(monthly_mean)
line_str = final_str
the_file.write(line_str + "\n")
counter += 1
except IOError as e:
self.show_custom_message(
"Error", "Cannot Save to " + daily_filename + "\n" + str(e) +
"\n Please select a different path to save to")
class Variable(QtCore.QObject):
'''
Class that holds a value, using change() emits a signal.
There is no mandatory naming for instances of this Class, however ARTview
components name variable according to the following table, using it is
recommended:
.. _shared_variable:
.. table:: Shared Variables Table
+-----------+-------------------+------------------------------------+
| Name | Function | Valid values |
+===========+===================+====================================+
| Vradar | Hold radar open | :py:class:`pyart.core.Radar` |
| | with pyart | instance |
+-----------+-------------------+------------------------------------+
| Vgrid | Hold grid open or | :py:class:`pyart.core.Grid` |
| | generated with | instance |
| | pyart | |
+-----------+-------------------+------------------------------------+
| Vcontainer| Alias to Vradar or| Radar, Grid or None |
| | Vgrid | |
+-----------+-------------------+------------------------------------+
| Vfield | Name of a Field | string, preferentially in |
| | in radar file | radar.fields.keys(), but there is |
| | | no guarranty |
+-----------+-------------------+------------------------------------+
| Vtilt | Tilt (sweep) of | int between 0 and |
| | a radar file | (number of sweeps) - 1 |
+-----------+-------------------+------------------------------------+
| VlevelZ | Vertical level of | int between 0 and |
| | a grid file | nz - 1 |
+-----------+-------------------+------------------------------------+
| VlevelY | Latitudinal level | int between 0 and |
| | of a grid file | ny - 1 |
+-----------+-------------------+------------------------------------+
| VlevelX | Longitudinal level| int between 0 and |
| | of a grid file | nx - 1 |
+-----------+-------------------+------------------------------------+
| Vlevel | Alias to Vtilt, | positive integer |
| | VlevelZ, VlevelY | |
| | or VlevelX | |
+-----------+-------------------+------------------------------------+
| Vlimits | Limits of display | dict containing keys: 'xmin', |
| | | 'xmax', 'ymin', 'ymax' and holding |
| | | float values |
+-----------+-------------------+------------------------------------+
| Vcolormap | Colormap | dict containing keys: 'vmin' and |
| | | 'vmax' holding float values and key|
| | | 'cmap' holding colormap string name|
+-----------+-------------------+------------------------------------+
|Vgatefilter| Hold pyart |:py:class:`pyart.filters.GateFilter`|
| | GateFilter |instance or None |
+-----------+-------------------+------------------------------------+
|VplotAxes | Hold axes of |:py:class:`matplotlib.axes.Axes` |
| | Matplotlib figure |instance or None |
+-----------+-------------------+------------------------------------+
|VPathInte\ | Hold auxillary |function like :py:func:`~artview.\ |
|riorFunc | function |components.RadarDisplay.\ |
| | |getPathInteriorValues` or None |
+-----------+-------------------+------------------------------------+
|Vfilelist | Hold filenames in |list containing paths to files |
| | current working |(strings) |
| | directory | |
+-----------+-------------------+------------------------------------+
|VRadarCol\ | Cache radars |list of :py:class:`pyart.core.Radar`|
|lection | |instances |
| | | |
+-----------+-------------------+------------------------------------+
|VpyartDis\ | Hold pyart display| instance of one of the classes |
|play | instances | in :py:mod:`pyart.graph` |
| | | |
+-----------+-------------------+------------------------------------+
'''
value = None #: Value of the Variable
valueChanged = QtCore.pyqtSignal(QtCore.QObject, bool, name='ValueChanged')
def __init__(self, value=None):
QtCore.QObject.__init__(self)
self.value = value
def change(self, value, strong=True):
'''
Change the Variable value and emit 'ValueChanged' signal.
Parameters
----------
value :
New Value to be assigned to the variable.
[Optional]
strong : bool, optional
Define if this is a strong or weak change. This is a somewhat
subjective decision: strong is default.
A weak change should be used to indicate to the slot that the
change should not trigger any costly computation.
Reasons for this are: When initializing a variable, it is likely
to change again shortly, or another more important variable is
being changed as well etc.
.. note::
Defining how to respond to strong/weak changes is the
responsibility of the slot, most can just ignore the
difference, but the costly ones should be aware.
Notes
-----
The arguments of the emitted signal are (self, value, strong).
'''
self.value = value
self.valueChanged.emit(self, strong)
def update(self, strong=True):
'''
Emits the 'ValueChanged' signal without changings value. This is
useful when value is changed in place.
Parameters
----------
strong : bool, optional
Define if this is a strong or weak change.
'''
self.valueChanged.emit(self, strong)
