def get_two_best_sondes(date_str, **kwargs):
sonde_file=kwargs.get('sonde_file', '/data/twpice/darwin.txt')
#outdir=kwargs.get('outdir', '/flurry/home/scollis/bom_mds/dealias/')
sonde_file=kwargs.get('sonde_file', '/data/twpice/darwin.txt')
outdir=kwargs.get('outdir', '/home/scollis/bom_mds/dealias/')
tim_date=num2date(datestr2num(date_str))
sonde_list=read_sounding_within_a_day(sonde_file, tim_date)
launch_dates=[sonde['date_list'][0] for sonde in sonde_list]
#print launch_dates
launch_date_offset=[date2num(sonde['date_list'][0])- date2num(tim_date) for sonde in sonde_list]
sonde_made_it=False
candidate=0
while not(sonde_made_it):
best_sonde=sonde_list[argsort(abs(array(launch_date_offset)))[candidate]]
candidate=candidate+1
sonde_made_it=best_sonde['alt(m)'][-1] > 18000.
if not sonde_made_it: print "Sonde Burst at ", best_sonde['alt(m)'][-1], "m rejecting"
print "Sonde Burst at ", best_sonde['alt(m)'][-1], "m Accepting"
sonde_made_it=False
while not(sonde_made_it):
sec_best_sonde=sonde_list[argsort(abs(array(launch_date_offset)))[candidate]]
candidate=candidate+1
sonde_made_it=sec_best_sonde['alt(m)'][-1] > 18000.
if not sonde_made_it: print "Sonde Burst at ", sec_best_sonde['alt(m)'][-1], "m rejecting"
print "Sonde Burst at ", sec_best_sonde['alt(m)'][-1], "m Accepting"
print 'Time of radar: ', tim_date, ' Time of best sonde_launch: ', best_sonde['date_list'][0], ' Time of sonde_termination: ', best_sonde['date_list'][-1]
print 'Time of radar: ', tim_date, ' Time of second sonde_launch: ', sec_best_sonde['date_list'][0], ' Time of sonde_termination: ', best_sonde['date_list'][-1]
for i in range(len(sonde_list)):
best_sonde=sonde_list[argsort(abs(array(launch_date_offset)))[i]]
print 'Time of radar: ', tim_date, ' Time of best sonde_launch: ', best_sonde['date_list'][0], ' Offset', abs(date2num(best_sonde['date_list'][0])-date2num(tim_date))*24.0
return best_sonde, sec_best_sonde
def generate_filename(self, fld='chl', fldtype="DAY", **kwargs):
"""Generate filename"""
if len(kwargs):
self._timeparams(**kwargs)
ydmax = (pl.date2num(dtm(self.yr, 12, 31)) -
pl.date2num(dtm(self.yr, 1, 1))) + 1
if fldtype == "MC":
self.add_mnclim()
datestr = self.mc_datedict[self.mn]
if "mo" in fldtype.lower():
self.add_filepreflist(fldtype="mo")
datestr = self.mo_fileprefs[self.yr*100 + self.mn]
elif fldtype == "DAY":
datestr = "%i%03i" % (self.yr, self.yd)
elif fldtype == "8D":
yd1 = np.arange(1,365,8)
yd2 = np.arange(8,370,8)
yd2[-1] = ydmax
pos = np.nonzero(self.yd >= yd1)[0].max()
datestr = ("%i%03i%i%03i" %
(self.yr, yd1[pos], self.yr, yd2[pos]))
elif fldtype == "CU":
self.a_cu_url_9km = 'MODISA/Mapped/Cumulative/4km/chlor/'
datestr = max(self._retrieve_datestamps(self.a_cu_url_9km))
else:
raise TypeError, "File average type not included"
return("%s%s.L3m_%s_%s_%s%s.nc" % (self.fp, datestr, fldtype,
self.vc[fld][0], self.res[0],
self.vc[fld][1]))
def main():
print("start program")
countTaxis()
# a figure (chart) where we add the bar's and change the axis properties
fig = figure()
ax = fig.add_subplot(111)
# set the width of the bar to interval-size
barWidth = date2num(intervalDate + intervalDelta) - date2num(intervalDate)
# add a bar with specified values and width
ax.bar(date2num(barList.keys()), barList.values(), width=barWidth)
# set the x-Axis to show the hours
ax.xaxis.set_major_locator(HourLocator())
ax.xaxis.set_major_formatter(DateFormatter("%H:%M"))
ax.xaxis.set_minor_locator(MinuteLocator())
ax.grid(True)
xlabel('Zeit (s)')
ylabel('Quantit' + u'\u00E4' + 't')
title('Menge der Taxis im VLS-Gebiet')
ax.autoscale_view()
# shows the text of the x-axis in a way that it looks nice
fig.autofmt_xdate()
# display the chart
show()
def movie(self):
import matplotlib as mpl
mpl.rcParams['axes.labelcolor'] = 'white'
pl.close(1)
pl.figure(1, (8, 4.5), facecolor='k')
miv = np.ma.masked_invalid
figpref.current()
jd0 = pl.date2num(dtm(2005, 1, 1))
jd1 = pl.date2num(dtm(2005, 12, 31))
mp = projmaps.Projmap('glob')
x, y = mp(self.llon, self.llat)
for t in np.arange(jd0, jd1):
print pl.num2date(t)
self.load(t)
pl.clf()
pl.subplot(111, axisbg='k')
mp.pcolormesh(x,
y,
miv(np.sqrt(self.u**2 + self.v**2)),
cmap=cm.gist_heat)
pl.clim(0, 1.5)
mp.nice()
pl.title(
'%04i-%02i-%02i' % (pl.num2date(t).year, pl.num2date(t).month,
pl.num2date(t).day),
color='w')
pl.savefig('/Users/bror/oscar/norm/%03i.png' % t,
bbox_inches='tight',
facecolor='k',
dpi=150)
def _l3read_hdf(self, fieldname='l3m_data'):
self.vprint( "Reading hdf file")
sd = SD(self.filename, SDC.READ)
ds = sd.select(fieldname)
field = ds[self.j1:self.j2, self.i1:self.i2].copy()
intercept = ds.attributes()['Intercept']
slope = ds.attributes()['Slope']
try:
nanval = ds.attributes()['Fill']
except:
nanval = 65535
try:
base = ds.attributes()['Base']
except KeyError:
base = -999
if 'Scaled Data Maximum' in sd.attributes().keys():
self.maxval = sd.attributes()['Scaled Data Maximum']
self.minval = sd.attributes()['Scaled Data Minimum']
elif 'Suggested Image Scaling Maximum' in sd.attributes().keys():
self.maxval = sd.attributes()['Suggested Image Scaling Maximum']
self.minval = sd.attributes()['Suggested Image Scaling Minimum']
else:
self.minval = self.vc[fld][2]
self.maxval = self.vc[fld][3]
start_iso = (pl.date2num(dtm(
sd.attributes()['Period Start Year'],1,1)) +
sd.attributes()['Period Start Day'] - 1)
end_iso = (pl.date2num(dtm(
sd.attributes()['Period End Year'],1,1)) +
sd.attributes()['Period End Day'] - 1)
self.jd = ((start_iso+end_iso)/2)
self.date = pl.num2date(self.jd)
return field,base,intercept,slope
def plot_coverage(self, siteid):
"""
Makes a bar plot (ganntt like) for the time coverage of datasets at a site
"""
st = io.BytesIO()
with db.session_scope() as session:
import matplotlib
matplotlib.use('Agg', warn=False)
import pylab as plt
import numpy as np
ds = session.query(db.Dataset).filter_by(_site=int(siteid)).order_by(
db.Dataset._source, db.Dataset._valuetype, db.Dataset.start).all()
left = plt.date2num([d.start for d in ds])
right = plt.date2num([d.end for d in ds])
btm = np.arange(-.5, -len(ds), -1)
# return 'left=' + str(left) + ' right=' + str(right) + ' btm=' + str(btm)
fig = plt.figure()
ax = fig.gca()
ax.barh(left=left, width=right - left, bottom=btm,
height=0.9, fc='0.75', ec='0.5')
for l, b, d in zip(left, btm, ds):
ax.text(l, b + .5, '#%i' % d.id, color='k', va='center')
ax.xaxis_date()
ax.set_yticks(btm + .5)
ax.set_yticklabels(
[d.source.name + '/' + d.valuetype.name for d in ds])
ax.set_position([0.3, 0.05, 0.7, 0.9])
ax.set_title('Site #' + siteid)
ax.set_ylim(-len(ds) - .5, .5)
ax.grid()
fig.savefig(st, dpi=100)
return st.getvalue()
def generate_filename(self, fld='chl', fldtype="DAY", **kwargs):
"""Generate filename"""
if len(kwargs):
self._timeparams(**kwargs)
ydmax = (pl.date2num(dtm(self.yr, 12, 31)) -
pl.date2num(dtm(self.yr, 1, 1))) + 1
if fldtype == "MC":
self.add_mnclim()
datestr = self.mc_datedict[self.mn]
if "mo" in fldtype.lower():
self.add_filepreflist(fldtype="mo")
datestr = self.mo_fileprefs[self.yr * 100 + self.mn]
elif fldtype == "DAY":
datestr = "%i%03i" % (self.yr, self.yd)
elif fldtype == "8D":
yd1 = np.arange(1, 365, 8)
yd2 = np.arange(8, 370, 8)
yd2[-1] = ydmax
pos = np.nonzero(self.yd >= yd1)[0].max()
datestr = ("%i%03i%i%03i" % (self.yr, yd1[pos], self.yr, yd2[pos]))
elif fldtype == "CU":
self.a_cu_url_9km = 'MODISA/Mapped/Cumulative/4km/chlor/'
datestr = max(self._retrieve_datestamps(self.a_cu_url_9km))
else:
raise TypeError, "File average type not included"
return ("%s%s.L3m_%s_%s_%s%s.nc" %
(self.fp, datestr, fldtype, self.vc[fld][0], self.res[0],
self.vc[fld][1]))
def plotaverages(data):
months = list(data.apply(set)[7]) #we need a list of all the uniqe months
months1 = []
for row in months:
months1.append(datetime.strptime(
row,
"%Y-%m")) #create a list with unique months as datetime values
medians = []
for i in months: #for each date in months to iterate loop below
dd = data[
data['Month'] ==
i] #change the dataset to only include thos rows with unique months
price = []
for row in dd[
'Price1']: #for each row that corresponds with that unique month
price.append(float(row)) #add it to a list
medians.append(py.median(
price)) #then add the value to a means list, then iterate
x = months1 #plot
y = medians
x, y = (
list(i) for i in zip(*sorted(zip(x, y)))
) #to numerically order both arrays in date order, relating the individual values from each array
plt.plot(pl.date2num(x), y)
plt.xlim(733400, 735400)
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter(
'%m/%Y')) #reformat x axis to show date in correct format
plt.xlabel('Date')
plt.ylabel('Price in Pounds')
plt.title('Monthly Median House Prices')
p = py.polyfit(pl.date2num(x), y, 1) #fit a straight line with order 1
plt.plot(
pl.date2num(x), p[0] * pl.date2num(x) + p[1], 'r-'
) #plot x against the coeffecients ofthe line, p[0]x + p[1] == mx + c
plt.show()
def movie(self):
import matplotlib as mpl
mpl.rcParams['axes.labelcolor'] = 'white'
pl.close(1)
pl.figure(1,(8,4.5),facecolor='k')
miv = np.ma.masked_invalid
figpref.current()
jd0 = pl.date2num(dtm(2005,1,1))
jd1 = pl.date2num(dtm(2005,12,31))
mp = projmaps.Projmap('glob')
x,y = mp(self.llon,self.llat)
for t in np.arange(jd0,jd1):
print pl.num2date(t)
self.load(t)
pl.clf()
pl.subplot(111,axisbg='k')
mp.pcolormesh(x,y,
miv(np.sqrt(self.u**2 +self.v**2)),
cmap=cm.gist_heat)
pl.clim(0,1.5)
mp.nice()
pl.title('%04i-%02i-%02i' % (pl.num2date(t).year,
pl.num2date(t).month,
pl.num2date(t).day),
color='w')
pl.savefig('/Users/bror/oscar/norm/%03i.png' % t,
bbox_inches='tight',facecolor='k',dpi=150)
def plot_1d_comparison(data_dict,style_dict,time_dict,start_date,finish_date,file_path,axis_label,interval=3):
"""
"""
start_time = date2num(datetime.strptime(start_date, "%Y-%m-%d %H:%M:%S"))
finish_time = date2num(datetime.strptime(finish_date, "%Y-%m-%d %H:%M:%S"))
# plot 1d graph...
fig = figure(figsize=(15,8),dpi=90)
ax = fig.add_axes([.05,.12,.9,.85])
max_value = 0.0
for key, data_arr in data_dict.iteritems():
ax.plot(time_dict[key],data_arr,style_dict[key], label=key)
data_arr = vtktools.arr(data_arr)
if data_arr.max() > max_value:
max_value = data_arr.max()
max_value += max_value * 0.1
dateFmt = mpl.dates.DateFormatter('%m/%Y')
ax.xaxis.set_major_formatter(dateFmt)
monthsLoc = mpl.dates.MonthLocator(interval=interval)
ax.xaxis.set_major_locator(monthsLoc)
labels = ax.get_xticklabels()
for label in labels:
label.set_rotation(30)
ax.set_ylim(max_value, 0)
ax.set_xlim(start_time,finish_time)
xlabel('Date (mm/yyyy)')
ylabel(axis_label)
legend(loc=0)
form = file_path.split('.')[-1].strip()
savefig(file_path, dpi=90,format=form)
close(fig)
def _timeparams(self, **kwargs):
"""Calculate time parameters from given values"""
for key in kwargs.keys():
self.__dict__[key] = kwargs[key]
if "date" in kwargs:
self.jd = pl.datestr2num(kwargs['date'])
self.jd = int(self.jd) if self.jd == int(self.jd) else self.jd
elif ('yd' in kwargs) & ('yr' in kwargs):
if self.yd < 1:
self.yr = self.yr -1
ydmax = (pl.date2num(dtm(self.yr, 12, 31)) -
pl.date2num(dtm(self.yr, 1, 1))) + 1
self.yd = ydmax + self.yd
self.jd = self.yd + pl.date2num(dtm(self.yr,1,1)) - 1
elif ('yr' in kwargs) & ('mn' in kwargs) & ('dy' in kwargs):
self.jd = pl.date2num(dtm(self.yr,self.mn,self.dy))
elif not 'jd' in kwargs:
if hasattr(self, 'defaultjd'):
self.jd = self.defaultjd
else:
raise KeyError, "Time parameter missing"
if hasattr(self,'hourlist'):
dd = self.jd-int(self.jd)
ddlist = np.array(self.hourlist).astype(float)/24
ddpos = np.argmin(np.abs(ddlist-dd))
self.jd = int(self.jd) + ddlist[ddpos]
self._jd_to_dtm()
def setTicks(self):
""" set x and y axes major and minor tick locators, formatters and labels """
# define tick locators
self.xMajor = LinearLocator(numticks = 16)
self.xMinor = LinearLocator(numticks = 76)
self.yMajor = LinearLocator(numticks = 5)
self.yMinor = LinearLocator(numticks = 17)
self.starttime = datetime.datetime.today()
self.starttime_tick = time.mktime(self.starttime.timetuple())
self.currenttime = self.starttime + datetime.timedelta(seconds = 3)
self.currenttime_tick = time.mktime(self.currenttime.timetuple())
self.lines = self.axes.plot([self.starttime], [0], 'r-')
# set tick locators
self.axes.xaxis.set_major_locator(self.xMajor)
self.axes.xaxis.set_minor_locator(self.xMinor)
self.axes.yaxis.set_major_locator(self.yMajor)
self.axes.yaxis.set_minor_locator(self.yMinor)
self.axes.set_xlim((date2num(self.starttime),date2num(self.currenttime)))
# create x-axis tick labels (seconds)
self.axes.xaxis.set_ticklabels(self.createXTickLabels(self.currenttime_tick), rotation = 30, ha = "right", size = 'smaller', name = 'Calibri')
self.axes.set_ylim(self.ymin,self.ymax)
# create y-axis tick labels (mV)
self.axes.yaxis.set_ticklabels(self.createYTickLabels(self.ymin), size = 'smaller', name = 'Calibri')
# set the properties of the minor axes
self.axes.grid(color = 'lightgrey', linewidth = 0.05, linestyle = ':', which = 'minor')
# set the properties of the major axes
self.axes.grid(color = 'slategrey', linewidth = 0.5, linestyle = '-', which = 'major')
def setXYlim(self, data, para): for label in self.axes.get_xaxis().get_ticklabels(): label.set_fontsize(9) if data[1][-1] > 0: #正 if data[1][-1] > para["open"]*0.75: self.axes.axhline(y = para["open"], linestyle = "--", linewidth = 0.5, color = "gray") if data[1][-1] > para["stop"]*0.85: self.axes.axhline(y = para["stop"], linestyle = "--", linewidth = 0.5, color = "red") if data[1][-1] < para["close"]*1.15: self.axes.axhline(y = para["close"], linestyle = "--", linewidth = 0.5, color = "green") else: #反 if data[1][-1] < -para["open"]*0.75: self.axes.axhline(y = -para["open"], linestyle = "--", linewidth = 0.5, color = "gray") if data[1][-1] < -para["stop"]*0.85: self.axes.axhline(y = -para["stop"], linestyle = "--", linewidth = 0.5, color = "red") if data[1][-1] > -para["close"]*1.15: self.axes.axhline(y = -para["close"], linestyle = "--", linewidth = 0.5, color = "green") thisDate = copy.copy(data[0][-1]) if data[0][-1].time() <= datetime.time(11,30,0): self.axes.axis(xmin=pylab.date2num(thisDate.replace(hour=9,minute=30,second=0)), xmax=pylab.date2num(thisDate.replace(hour=11,minute=30))) else: self.axes.axis(xmin=pylab.date2num(thisDate.replace(hour=13,minute=0,second=0)), xmax=pylab.date2num(thisDate.replace(hour=15,minute=0))) pass
def plotSceleton(self,
axes,
xlabel='Power',
ylabel='Period',
scaling='log',
min_h=0.,
max_h=1000.):
cw = self._cw
scales = cw.getscales()
pwr = self.getSceleton(cw.getpower())
y = cw.fourierwl * scales
axes.imshow(pwr[0],
cmap=plb.cm.hot_r,
extent=[
plb.date2num(self._x[0]),
plb.date2num(self._x[-1]), y[-1], y[0]
],
aspect='auto',
interpolation=None)
axes.xaxis_date()
axes.imshow(pwr[1],
cmap=plb.cm.hot_r,
extent=[
plb.date2num(self._x[0]),
plb.date2num(self._x[-1]), y[-1], y[0]
],
aspect='auto',
interpolation=None)
axes.xaxis_date()
if scaling == "log":
axes.set_yscale('log')
axes.set_ylim(y[0], y[-1])
def initPlot(self):
""" redraw the canvas to set the initial x and y axes when plotting starts """
self.starttime = datetime.datetime.today()
self.currenttime = self.starttime + datetime.timedelta(seconds=3)
self.endtime = self.starttime + datetime.timedelta(seconds=15)
self.timeaxis = num2date(drange(self.starttime, self.endtime, datetime.timedelta(milliseconds=10)))
self.xvalues.append(self.timeaxis[0])
self.yvalues.append(self.parentPanel.myECG.ecg_leadII[0])
# for counter purposes only
self.ybuffer = self.yvalues
self.lines[0].set_data(self.xvalues, self.yvalues)
self.axes.set_xlim((date2num(self.starttime), date2num(self.currenttime)))
self.axes.xaxis.set_ticklabels(
self.createXTickLabels(self.currenttime), rotation=30, ha="right", size="smaller", name="Calibri"
)
self.samples_counter += 1
self.ysamples_counter += 1
self.buff_counter = 1
def setTicks(self):
""" set x and y axes major and minor tick locators, formatters and labels """
# set tick locators
self.xMajor = LinearLocator(numticks=16)
self.xMinor = LinearLocator(numticks=76)
self.yMajor = LinearLocator(numticks=13)
self.yMinor = LinearLocator(numticks=61)
self.starttime = datetime.datetime.today()
self.currenttime = self.starttime + datetime.timedelta(seconds=3)
self.lines = self.axes.plot([self.starttime], [0], "r-")
self.axes.xaxis.set_major_locator(self.xMajor)
self.axes.xaxis.set_minor_locator(self.xMinor)
self.axes.yaxis.set_major_locator(self.yMajor)
self.axes.yaxis.set_minor_locator(self.yMinor)
self.axes.set_xlim((date2num(self.starttime), date2num(self.currenttime)))
self.axes.xaxis.set_ticklabels(
self.createXTickLabels(self.currenttime), rotation=30, ha="right", size="smaller", name="Calibri"
)
self.axes.set_ylim(self.ymin, self.ymax)
self.axes.yaxis.set_ticklabels(self.createYTickLabels(self.ymin), size="smaller", name="Calibri")
self.axes.grid(color="lightgrey", linewidth=0.05, linestyle=":", which="minor")
self.axes.grid(color="slategrey", linewidth=0.5, linestyle="-", which="major")
def plot(dates,time,mode,temp):
global nfigure, datemin, datemax
v, a, b, c = invers(temp, time-datemin)
amp,dphi=np.sqrt(a**2+b**2),np.arctan2(b,a)
if dphi < 0:
dphi = dphi + 2*np.pi
phase[i,j], amplitude[i,j] = dphi,amp
print('Mean T: {:0.3f} °'.format(np.mean(t)))
print('Vitesse: {:0.3f} °/yr'.format(v))
print('Amplitude: {:0.3f} °'.format(np.sqrt(a**2+b**2)))
print('Phase Shift: {:0.3f} rad'.format(np.arctan2(b,a)))
fit = seasonal(time-datemin,v,a,b,c)
# plot data
fig = plt.figure(nfigure,figsize=(12,5))
ax = fig.add_subplot(1,1,1)
# convert idates to num
x = date2num(dates)
xmin,xmax=datetime.datetime(np.int(datemin), 01, 01),datetime.datetime(datemax, 01, 01)
xlim=date2num(np.array([xmin,xmax]))
# format the ticks
ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y/%m/%d"))
ax.plot(x,temp, '-bo',ms=.5,lw=.1,color='blue',label='ERAI point {}, {}'.format(lat,lon),rasterized=True)
ax.plot(x,fit,'-r',lw=2.0,label='MAAT: {0:.2f}, v: {1:.2f} deg/yrs, dphi:{2:.2f} rad, Amp:{3:.2f} deg'.format(np.mean(temp),v,dphi,amp))
ax.plot(xlim,[0,0],'--',c='black')
fig.autofmt_xdate()
ax.set_xlim(xlim)
ax.set_ylim([-30,30])
plt.xlabel('Time (Year/month/day)')
plt.ylabel('Temperature (deg)')
plt.legend(loc='best')
fig.savefig('temperatures_{}_{}.pdf'.format(lat,lon), format='PDF')
nfigure=+1
# plot mod
fig2 = plt.figure(nfigure,figsize=(7,5))
ax2 = fig2.add_subplot(1,1,1)
ax2.set_xlim([0,1])
ax2.plot(mode,temp, '-bo',ms=.5,lw=.1,color='blue',label='ERAI point {}, {}'.format(lat,lon),rasterized=True)
x = np.arange(0,1,0.01)
fit = seasonal(x,v,a,b,c)
ax2.plot(x,fit,'-r',lw=2.0,label='MAAT: {0:.2f}'.format(np.mean(temp)))
ax2.plot([0,1],[0,0],'--',c='black')
idx = np.argwhere(np.diff(np.sign(fit))).flatten()
for k in range(len(idx)):
ax2.text(x[idx[k]],-25,'{:0.3f}'.format(x[idx[k]]))
ax2.text(x[idx[k]],-28,(datetime.datetime(np.int(datemin), 1, 1) + datetime.timedelta(x[idx[k]]*365.1 - 1)).strftime('%Y/%m/%d'))
ax2.plot([x[idx[k]],x[idx[k]]],[-30,30],'--',c='black')
ax2.set_xticks(np.arange(0, 1, 1./12))
ax2.xaxis.set_major_formatter(ticker.FormatStrFormatter('%0.1f'))
plt.xlabel('Time')
plt.ylabel('Temperature (deg)')
plt.legend(loc='best')
ax2.set_ylim([-30,30])
fig2.savefig('Modtemperatures_{}_{}.pdf'.format(lat,lon), format='PDF')
def save_data_cube(radar1, radar2, ncf_fname):
ncf = NetCDFFile(ncf_fname, "w")
ncf.createDimension("nx", len(radar1["xar"]))
ncf.createDimension("ny", len(radar1["yar"]))
ncf.createDimension("nl", len(radar1["levs"]))
ncf.createDimension("one", 1)
ncf.createDimension("two", 2)
avail_vars_radar1 = set(radar1.keys()) & set(
["VE", "VR", "CZ", "RH", "PH", "ZD", "SW", "KD", "i_comp", "j_comp", "k_comp", "u_array", "v_array", "w_array"]
)
avail_vars_radar2 = set(radar2.keys()) & set(
["VE", "VR", "CZ", "RH", "PH", "ZD", "SW", "KD", "i_comp", "j_comp", "k_comp"]
)
# testme=make_var(ncf, var, 'f', ('nx', 'ny', 'nl'), gp[var])
ncf_varlist_radar1 = [
make_var(ncf, var + "_radar1", "f", ("nx", "ny", "nl"), radar1[var]) for var in avail_vars_radar1
]
ncf_varlist_radar2 = [
make_var(ncf, var + "_radar2", "f", ("nx", "ny", "nl"), radar2[var]) for var in avail_vars_radar2
]
xvar = ncf.createVariable("xar", "f", ("one", "nx"))
for i in range(len(radar1["xar"])):
xvar[0, i] = float(radar1["xar"][i])
yvar = ncf.createVariable("yar", "f", ("one", "ny"))
for j in range(len(radar1["yar"])):
yvar[0, j] = float(radar1["yar"][j])
# yvar.assignValue(array([gp['yar']]))
lvar = ncf.createVariable("levs", "f", ("one", "nl"))
for k in range(len(radar1["levs"])):
lvar[0, k] = float(radar1["levs"][k])
# lvar.assignValue(array([gp['levs']]))
rad1_locvar = ncf.createVariable("radar1_loc", "f", ("one", "two"))
rad2_locvar = ncf.createVariable("radar2_loc", "f", ("one", "two"))
rad1_disvar = ncf.createVariable("radar1_dis", "f", ("one", "two"))
rad2_disvar = ncf.createVariable("radar2_dis", "f", ("one", "two"))
rad1_datvar = ncf.createVariable("radar1_date", "d", ("one", "one"))
rad2_datvar = ncf.createVariable("radar2_date", "d", ("one", "one"))
rad1_locvar[0, 0] = float(radar1["radar_loc"][0])
rad1_locvar[0, 1] = float(radar1["radar_loc"][1])
rad2_locvar[0, 0] = float(radar2["radar_loc"][0])
rad2_locvar[0, 1] = float(radar2["radar_loc"][1])
rad1_disvar[0, 0] = float(radar1["displacement"][0])
rad1_disvar[0, 1] = float(radar1["displacement"][1])
rad2_disvar[0, 0] = float(radar2["displacement"][0])
rad2_disvar[0, 1] = float(radar2["displacement"][1])
rad1_datvar[0, 0] = float(date2num(radar1["date"]))
rad2_datvar[0, 0] = float(date2num(radar2["date"]))
setattr(ncf, "radar1_name", radar1["radar_name"])
setattr(ncf, "radar2_name", radar2["radar_name"])
# ncf_varlist_gp=dict([(var+'_gp',ncf.createVariable(var+'_gp', 'f', ('nx', 'ny', 'nl'))) for var in avail_vars_gp])
# for var in avail_vars_gp:
# for i in range(ncf_varlist_gp[var+'_gp'].shape[0]):
# for j in range(ncf_varlist_gp[var+'_gp'].shape[1]):
# for k in range(ncf_varlist_gp[var+'_gp'].shape[2]):
# ncf_varlist_gp[var+'_gp'][i,j,k]=gp[var][i,j,k]
ncf.close()
def addSlider(self, valmax):
""" put a slider widget to navigate through the whole ECG plot """
### FIX ME: Make all time objects as parameters??? (for flexibility)
### Maybe the self.endtime? Kase constant lagi ang starting point
### added valmax as the endtime parameter
self.axtime = self.figure.add_axes([0.125, 0.1, 0.775, 0.03])
self.time_scroller = matplotlib.widgets.Slider(
self.axtime, "", date2num(self.starttime), date2num(valmax), valinit=date2num(self.starttime)
)
self.time_scroller.on_changed(self.updateWindow)
def Draw01(D2A, StockName, Duration):
#>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
# show as a picture
# x axis, time
Llength = len(D2A) - 1
CXL02 = []
for Ll in range(1, Llength):
CXL02.append(datetime.datetime.strptime(D2A[Ll][0], '%Y-%m-%d'))
CXL02.reverse()
#>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
# y axis, adj close value
CXL03 = []
for Ll in range(1, Llength):
CXL03.append(float(D2A[Ll][2]))
CXL03.reverse()
Label03 = D2A[0][2]
# Buy piont
CXL06 = []
CXL07 = []
for Ll in range(1, Llength):
if D2A[Ll][12] == "BBB":
CXL06.append(datetime.datetime.strptime(D2A[Ll][0], '%Y-%m-%d'))
CXL07.append(float(D2A[Ll][2]))
CXL06.reverse()
CXL07.reverse()
#---------------------------------------
# Sell point
CXL08 = []
CXL09 = []
for Ll in range(1, Llength):
if D2A[Ll][13] == "SSS":
CXL08.append(datetime.datetime.strptime(D2A[Ll][0], '%Y-%m-%d'))
CXL09.append(float(D2A[Ll][2]))
CXL08.reverse()
CXL09.reverse()
#---------------------------------------
# volumn
CXL10 = []
for Ll in range(1, Llength):
CXL10.append(int(D2A[Ll][5]))
CXL10.reverse()
#---------------------------------------
plt.figure(1)
ax1 = plt.subplot(211)
ax2 = plt.subplot(212)
plt.sca(ax1)
plt.plot(CXL02, CXL03, color="black", label=Label03)
plt.plot_date(pylab.date2num(CXL06), CXL07)
plt.plot_date(pylab.date2num(CXL08), CXL09, color="red")
plt.title(StockName + " + Average " + str(Duration) +
" + Buy / Sell point ")
plt.sca(ax2)
plt.plot(CXL02, CXL10)
plt.title(" Vol. ")
plt.show()
def make_hourly(data):
'''
'''
time = data['time']
ntimesnew = int((time[-1] - time[0]).total_seconds() / 3600)
timenew = [time[0] + dt.timedelta(i) / 24 for i in range(ntimesnew)]
for varname, var in data.iteritems():
if (varname == 'time'):
continue
var = sp.interp(pl.date2num(timenew), pl.date2num(time), var)
data[varname] = var
data['time'] = timenew
return 0
def make_hourly(data):
'''
'''
time = data['time']
ntimesnew = int((time[-1]-time[0]).total_seconds() / 3600)
timenew = [time[0]+dt.timedelta(i)/24 for i in range(ntimesnew)]
for varname, var in data.iteritems():
if (varname == 'time'):
continue
var = sp.interp(pl.date2num(timenew), pl.date2num(time), var)
data[varname] = var
data['time'] = timenew
return 0
def extraPlots():
import datetime
from matplotlib.dates import MONDAY, MonthLocator, WeekdayLocator, DateFormatter
#converts dates to format matplotlib understands...
time = P.date2num(data[:,dict['DateTimeUT']])
mondays = WeekdayLocator(MONDAY)
months = MonthLocator(range(1,13, 2), bymonthday=2)
monthsFmt = DateFormatter("%b '%y")
y2007 = datetime.date(2007, 1, 1)
y2008 = datetime.date(2008, 1, 1)
y2007plot = P.date2num(y2007)
y2008plot = P.date2num(y2008)
widening = 5.
fig = P.figure()
P.subplots_adjust(hspace=0.1)
ax = fig.add_subplot(211)
P.title('ALFOSC focus pyramid data')
ax.plot_date(time, telfocusOld, 'wo', xdate = True)
ax.plot_date(time, telfocusCorrected, 'bo')
ax.axhline(medianNew, color = 'b', label = 'New Median', lw = 1., ls = '-')
ax.axhline(medianOld, color = 'r', label ='Old Median', lw = 1., ls = '--')
ax.axvline(y2007plot, color = 'k')
ax.axvline(y2008plot, color = 'k')
ax.legend(shadow = True, loc = 'best')
P.ylabel('Telescope Focus + Median Offset')
P.xlim(min(time)-widening, max(time)+widening)
P.ylim(23300.,23500.)
ax.xaxis.set_major_locator(months)
ax.xaxis.set_major_formatter(monthsFmt)
ax.xaxis.set_minor_locator(mondays)
fig.autofmt_xdate()
bx = fig.add_subplot(212)
bx.plot_date(time, data[:,dict['TempInAirDegC']], fmt='ro', xdate=True)
bx.axhline(0.)
bx.axvline(y2007plot, color = 'k')
bx.axvline(y2008plot, color = 'k')
bx.xaxis.set_major_locator(months)
bx.xaxis.set_major_formatter(monthsFmt)
bx.xaxis.set_minor_locator(mondays)
P.xlim(min(time)-widening, max(time)+widening)
P.ylabel('Temperature In Air (DegC)')
fig.autofmt_xdate()
fig.savefig('foc-pyr_time.png')
P.close()
def extraPlots():
import datetime
from matplotlib.dates import MONDAY, MonthLocator, WeekdayLocator, DateFormatter
#converts dates to format matplotlib understands...
time = P.date2num(data[:, dict['DateTimeUT']])
mondays = WeekdayLocator(MONDAY)
months = MonthLocator(range(1, 13, 2), bymonthday=2)
monthsFmt = DateFormatter("%b '%y")
y2007 = datetime.date(2007, 1, 1)
y2008 = datetime.date(2008, 1, 1)
y2007plot = P.date2num(y2007)
y2008plot = P.date2num(y2008)
widening = 5.
fig = P.figure()
P.subplots_adjust(hspace=0.1)
ax = fig.add_subplot(211)
P.title('ALFOSC focus pyramid data')
ax.plot_date(time, telfocusOld, 'wo', xdate=True)
ax.plot_date(time, telfocusCorrected, 'bo')
ax.axhline(medianNew, color='b', label='New Median', lw=1., ls='-')
ax.axhline(medianOld, color='r', label='Old Median', lw=1., ls='--')
ax.axvline(y2007plot, color='k')
ax.axvline(y2008plot, color='k')
ax.legend(shadow=True, loc='best')
P.ylabel('Telescope Focus + Median Offset')
P.xlim(min(time) - widening, max(time) + widening)
P.ylim(23300., 23500.)
ax.xaxis.set_major_locator(months)
ax.xaxis.set_major_formatter(monthsFmt)
ax.xaxis.set_minor_locator(mondays)
fig.autofmt_xdate()
bx = fig.add_subplot(212)
bx.plot_date(time, data[:, dict['TempInAirDegC']], fmt='ro', xdate=True)
bx.axhline(0.)
bx.axvline(y2007plot, color='k')
bx.axvline(y2008plot, color='k')
bx.xaxis.set_major_locator(months)
bx.xaxis.set_major_formatter(monthsFmt)
bx.xaxis.set_minor_locator(mondays)
P.xlim(min(time) - widening, max(time) + widening)
P.ylabel('Temperature In Air (DegC)')
fig.autofmt_xdate()
fig.savefig('foc-pyr_time.png')
P.close()
def uvmat(self):
hsmat = np.zeros ([20]+list(self.llat.shape)).astype(np.int16)
jd1 = pl.date2num(dtm(2003,1,1))
jd2 = pl.date2num(dtm(2009,12,31))
vlist = np.linspace(0,1.5,21)
for jd in np.arange(jd1,jd2+1):
print pl.num2date(jd)
self.load(jd=jd)
uv = np.sqrt(self.u**2 + self.v**2)
for n,(v1,v2) in enumerate(zip(vlist[:-1],vlist[1:])):
msk = (uv>=v1) & (uv<v2)
hsmat[n,msk] += 1
return hsmat
def uvmat(self):
hsmat = np.zeros([20] + list(self.llat.shape)).astype(np.int16)
jd1 = pl.date2num(dtm(2003, 1, 1))
jd2 = pl.date2num(dtm(2009, 12, 31))
vlist = np.linspace(0, 1.5, 21)
for jd in np.arange(jd1, jd2 + 1):
print pl.num2date(jd)
self.load(jd=jd)
uv = np.sqrt(self.u**2 + self.v**2)
for n, (v1, v2) in enumerate(zip(vlist[:-1], vlist[1:])):
msk = (uv >= v1) & (uv < v2)
hsmat[n, msk] += 1
return hsmat
def plot(prediction_dir):
# get real data
USING_CACHE = True
series_cache_path = './data/series_cache.pkl'
real_data = extractHourlyPower(series_cache_path, USING_CACHE)
# get prediction data
fi = open(prediction_dir,'rb')
prediction = pickle.load(fi)
fi.close()
print('prediction amount: '+str(len(prediction)))
print('prediction sahpe: '+str(numpy.shape(prediction[0])))
# get prediction data
#fi = open('./result/pre_history_average.pkl','rb')
fi = open('./result/pre_ecd-dcd_batch1_lstm1_patience5.pkl','rb')
history_average = pickle.load(fi)
fi.close()
# plot real data
date = [str(real_data[i:i+1].keys().values[0]) for i in range(len(real_data))]
real_power = [i for i in real_data]
real_x = [dt.datetime.strptime(d,'%Y%m%d%H') for d in date]
pylab.plot_date(pylab.date2num(real_x), real_power, linestyle='-', color='black')
# plot prediction data
power_pre = prediction
pre_point_num = len(power_pre)
data_pre = date[-pre_point_num:]
#print(data_pre)
#print(power_pre)
x_pre = [dt.datetime.strptime(d,'%Y%m%d%H') for d in data_pre]
pylab.plot_date(pylab.date2num(x_pre), power_pre, linestyle='-', color='green')
# plot history average
pylab.plot_date(pylab.date2num(x_pre), history_average, linestyle='-', color='red')
# calculate rmse
y_true = real_power[-pre_point_num:]
print('history average rmse: ', sqrt(smet.mean_squared_error(y_true, history_average)))
print('predection rmse: ', sqrt(smet.mean_squared_error(y_true, power_pre)))
#print(sqrt(smet.mean_squared_error(y_true, power_val_pre)))
xlabel(u"date & hour")
ylabel(u"power_used (every hour)")
grid(True)
show()
def refresh(self, fld, fldtype="DAY", jd1=None, jd2=None, delall=False):
""" Read a L3 mapped file and add field to current instance"""
jd1 = pl.datestr2num('2003-01-01') if jd1 is None else jd1
jd2 = int(pl.date2num(dtm.now())) - 1 if jd2 is None else jd2
for jd in np.arange(jd1, jd2):
print " --- %s --- " % pl.num2date(jd).strftime('%Y-%m-%d')
filename = os.path.join(
self.datadir, self.generate_filename(jd,fld,fldtype) + ".nc")
if delall:
for fn in glob.glob(filename + "*"):
print "Deleted %s" % fn
os.remove(fn)
print "Checking files"
if not os.path.isfile(filename[:-3] + '.npz'):
try:
self.load(fld, fldtype, jd=jd, verbose=True)
except IOError:
print "Downloading failed. Trying to remove old files."
try:
os.remove(filename)
except:
pass
try:
self.load(fld,fldtype,jd=jd,verbose=True)
except:
print (" ### Warning! Failed to add %s ###" %
os.path.basename(filename))
print "\n"
else:
print "found"
def plotresult(outflow, wetness):
"Plots the result using matplotlib"
# import some matplotlib functionality
import pylab
import numpy
import cmf.draw
# Make the upper plot
pylab.subplot(211)
# Draw the timeseries. cmf.draw.plot_timeseries is a thin wrapper over pylab.plot_date
cmf.draw.plot_timeseries(outflow)
pylab.title('Groundwater recharge')
pylab.axis('tight')
# Make the lower plot
pylab.subplot(212)
# Convert wetness to a numpy array - for faster analysis
wetness = numpy.array(wetness)
# Make a times/depth contour map
pylab.contourf([pylab.date2num(t.AsPython()) for t in outflow.iter_time()],
c.layers.thickness * 0.5 - c.layers.lower_boundary,
wetness.T,
levels=numpy.linspace(wetness.min(), 1, 50),
cmap=pylab.cm.jet_r,
extend='both',
)
pylab.title('Wetness')
pylab.gca().xaxis_date()
pylab.axis('tight')
pylab.show()
def _jd_to_dtm(self):
dtobj = pl.num2date(self.jd)
njattrlist = ['yr', 'mn', 'dy', 'hr', 'min', 'sec']
dtattrlist = ['year','month','day','hour','minute', 'second']
for njattr,dtattr in zip(njattrlist, dtattrlist):
setattr(self, njattr, getattr(dtobj, dtattr))
self.yd = self.jd - pl.date2num(dtm(self.yr,1,1)) + 1
def make_ticket_history_table(env, dates, sorted_events):
"""
This function takes list of dates in milestone and ticket events
then produce a dictionary with key as milestone event and value as
that list of ticket counts each day.
dates is the numerical array of date in UTC time.
sorted_event is dictionary of events that occurs in milestone
"""
#Initialize the count using key in events
tkt_counts = {'Enter':[], 'Leave':[], 'Finish':[]}
#initialize the table
for date in dates:
#env.log.info("Date:%s" % (num2date(date),))
for key in tkt_counts:
tkt_counts[key].append(0)
#Create dictionary of list that hold ticket count each day in dates
for event in sorted_events:
#Time in epoch time
date = to_datetime(event[0])
#Get the index of this date in the dates list
index = bisect(dates, date2num(date)) - 1
for key in tkt_counts:
tkt_counts[key][index] = tkt_counts[key][index] + len(event[1][key])
return tkt_counts
def calc_mld(files, start, x0=0.0, y0=0.0):
""" Caclulate density-based MLD from a bunch of VTU files
"""
mld = []
times = []
dates = []
for file in files:
try:
os.stat(file)
except:
print("No such file: %s" % file)
sys.exit(1)
# open vtu and derive the field indices of the edge at (x=0,y=0) ordered by depth
u = vtktools.vtu(file)
pos = u.GetLocations()
ind = get_1d_indices(pos, x0, y0)
# from this we can derive the 1D profile of any field like this:
depth = vtktools.arr([-pos[i, 2] for i in ind])
# handle time for different types of plots
time = u.GetScalarField('Time')
times.append(time[0]) # seconds
dates.append(date2num(start +
timedelta(seconds=time[0]))) # integer datetime
# grab density profile and calculate MLD_den (using 2 different deviation parameters
d = u.GetScalarField('Density')
den = vtktools.arr([d[i] * 1000 for i in ind])
mld.append(calc_mld_den(den, depth)) #den0 = 0.03 is default
return mld, times, dates
def __init__(self, projname, casename=None, **kwargs):
super(Trm, self).__init__(projname, casename, **kwargs)
if not hasattr(self, 'trmdir'):
self.trmdir = os.getenv('TRMDIR')
if self.trmdir is None:
raise EnvironmentError, """ Trmdir is not set.
Add TRMDIR=/path/to/tracmass to your local environment
or specify trmdir when calling Trm."""
def parse(od, pn, cn, sfx):
gridfile = '/%s/projects/%s/%s_%s.in' % (od, pn, cn, sfx)
if not os.path.isfile(gridfile):
raise IOError("Can't find the file %s" % gridfile)
return nlt.parse(gridfile)
self.nlgrid = parse(self.trmdir, self.projname, self.projname, "grid")
self.nlrun = parse(self.trmdir, self.projname, self.casename, "run")
if not hasattr(self, 'datadir'): self.datadir = self.nlrun.outDataDir
if not hasattr(self, 'datafile'):
self.datafile = self.nlrun.outDataFile
self.base_iso = pl.date2num(
dtm(self.nlgrid.baseYear, self.nlgrid.baseMon,
self.nlgrid.baseDay)) - 1
self.imt = self.nlgrid.IMT
self.jmt = self.nlgrid.JMT
def __init__(self, ob):
# populate attributes with sounding data, initially this will
# only work with a netcdf variable object (from Sceintific.IO)
# but more objects can be added by simply adding elif..
# PLEASE always populate height in the values['alt'] position and
# append values['date_list'] and datetime
# datetime and date_list[index] are datetime objects
# check if it is a netcdf variable list
if "getValue" in dir(ob[ob.keys()[0]]):
# this is a netcdf variables object
self.datetime = num2date(datestr2num("19700101") + ob["base_time"].getValue() / (24.0 * 60.0 * 60.0))
values = {}
units = {}
longname = {}
for var in ob.keys():
values.update({var: ob[var][:]})
try:
units.update({var: ob[var].units})
except AttributeError:
units.update({var: "no units"})
try:
longname.update({var: ob[var].long_name})
except AttributeError:
longname.update({var: "no longname"})
values.update(
{"date_list": num2date(date2num(self.datetime) + values["time_offset"] / (24.0 * 60.0 * 60.0))}
)
units.update({"date_list": "unitless (object)"})
self.values = values
self.units = units
self.long_name = longname
def _set_time_origin(self):
try:
self._time_counter_origin = self.read_nc_att(
'time_counter', 'time_origin')
ymd, hms = self._time_counter_origin.split(' ')
except Exception:
try:
self._time_counter_origin = self.read_nc_att(
'time_counter', 'units')
junk, junk, ymd, hms = self._time_counter_origin.split(' ')
#print self._time_counter_origin
except Exception:
self._time_counter_origin = self.read_nc_att('time', 'units')
junk, junk, ymd, hms = self._time_counter_origin.split(' ')
#else: Exception
#print self._time_counter_origin
y, mo, d = ymd.split('-')
h, mi, s = hms.split(':')
#print y, mo, d, h, mi, s
try:
time_origin = datetime(int(y),
strptime(mo, '%b').tm_mon, int(d), int(h),
int(mi), int(s))
except Exception:
time_origin = datetime(int(y), int(mo), int(d), int(h), int(mi),
int(s))
self.time_origin = plt.date2num(time_origin)
return self
def calc(self):
p0=3.
a0=15.
ts = wn.TrainStrategy.BFGS
inp = plb.date2num(self.time)
tar = self.value-np.average(self.value)
inp -= np.min(inp)
delta = np.max(inp)-np.min(inp)
k = 4*24/delta
inp *= k
w = wn.Net(10, np.min(inp), np.max(inp), np.average(tar),
a0, .01, p0)
track = w.train(inp, tar, ts, 200, 100000, 1, True, True)
#import pdb; pdb.set_trace()
#tool.show(inp, tar, w, track)
we = wn.Net(10, np.min(inp), np.max(inp), np.average(tar),
a0, .01, p0)
tracke = we.train(inp, tar, ts, 200, 100000, 1, False, False)
plb.title('Суммарная квадратичная ошибка')
plb.plot(tracke['e'][0], label='Обычная вейвсеть')
plb.plot(track['e'][0], linestyle='--', label='Полиморфная вейвсеть')
plb.xlabel('Эпохи')
plb.legend()
print (tracke['e'][-1])
print (track['e'][-1])
plb.show()
tool.show(inp, tar, w, tracke)
sys.exit()
def refresh_chart(self):
# Generate a chart.
from pylab import setp, date2num, DateFormatter
from datetime import datetime
ps = 1e-12
t, dt = self.data
date = array([date2num(datetime.fromtimestamp(x)) for x in t])
self.figure.subplots_adjust(bottom=0.2)
self.plot = self.figure.add_subplot(1, 1, 1)
self.plot.clear()
self.plot.set_xlabel("time")
self.plot.xaxis_date()
setp(self.plot.get_xticklabels(), rotation=90, fontsize=10)
self.plot.set_ylabel("timing error [ps]")
self.plot.grid()
if not all(isnan(dt)):
self.plot.plot(date, (dt - self.nom_delay) / ps, '.')
# Restrict the time range plotted according to the slider.
tmin, tmax = amin(date[~isnan(dt)]), amax(date[~isnan(dt)])
##tmin = tmax - self.fraction*(tmax-tmin)
self.plot.set_xlim(tmin, tmax)
tmin, tmax = self.plot.get_xlim()
if tmax - tmin < 5. / 24 / 60: date_format = "%H:%M:%S"
elif tmax - tmin < 1: date_format = "%H:%M"
else: date_format = "%b %d %H:%M"
self.plot.xaxis.set_major_formatter(DateFormatter(date_format))
if not isnan(self.min_dt): self.plot.set_ylim(ymin=self.min_dt / ps)
if not isnan(self.max_dt): self.plot.set_ylim(ymax=self.max_dt / ps)
self.canvas.draw()
def work_1():
data_in = datetime(2010,6,24,8,00,0)
data_fin = datetime(2010,6,24,22,00,0)
#np.concatenate((dati,dati2))
dati = df.query_db('greenhouse.db','data',data_in,data_fin)
Is = dati['rad_int_sup_solar']
lista_to_filter = df.smooht_Is(Is)
Is_2 = df.smooth_value(Is,lista_to_filter)
tra_P_M = mf.transpiration_P_M(Is_2,dati['rad_int_inf_solar'],0.64,2.96,((dati['temp_1']+dati['temp_2'])/2)+273.15,(dati['RH_1']+dati['RH_2'])/200)
tra_weight = mf.transpiration_from_balance(dati['peso_balanca'],300,2260000)
delta_peso = np.diff(dati['peso_balanca'])
fr,lista_irr,lista_irr_free = mf.find_irrigation_point(delta_peso,dati['data'])
lista_night = mf.remove_no_solar_point(dati['rad_int_sup_solar'],50)
lista_no = list(set(lista_irr+ lista_night))
tran_weight,lista_yes = mf.transpiration_from_balance_irr(dati['peso_balanca'],300,2260000,lista_no)
min_avg = 6
tra_weigh_avg,time_weight = df.avg2(tran_weight,lista_yes,min_avg)
tra_P_M_avg,time_P_M = df.avg2(tra_P_M,lista_yes,min_avg)
data_plot.plot_time_data_2_y_same_axis(dati['data'][time_P_M], tra_P_M_avg, 'tra Penman', tra_weigh_avg, 'trans weight')
RMSE = df.RMSE(tra_P_M_avg, tra_weigh_avg)
print "RMSE is", RMSE
print "RRMSE is", df.RRMSE(RMSE, tra_weigh_avg)
date = dati['data'][time_P_M].astype(object)
dates= pylab.date2num(date)
pylab.plot_date(dates,tra_weigh_avg,'rx')
def time_string_to_pylab_float_time(string):
# I don't want this entire module to be dependent on pylab.
try:
import pylab
except:
print >> sys.stderr, "Couldn't import pylab module."
sys.exit(1)
# strptime can't handle the fractional part. Split that off.
pieces = string.split('.')
date_and_time_part = pieces[0]
time_struct = date_and_time_part_to_time_struct(date_and_time_part)
float_time = pylab.date2num(time_struct)
# Add in the fractional part. Also try to handle the timezone part,
# if any.
if len(pieces) > 1:
remaining_pieces = pieces[1].split()
if len(remaining_pieces) == 1:
pass # No timezone offset
elif len(remaining_pieces) == 2:
pass # Timezone offset; ignore it.
else:
print >> sys.stderr, "Extra stuff in time string ", string
sys.exit(1)
sec_frac = float('.' + remaining_pieces[0])
return float_time + sec_frac / 24 / 60 / 60
else:
return float_time
def __init__(self, projname, casename=None, **kwargs):
super(Trm, self).__init__(projname, casename, **kwargs)
if not hasattr(self, "trmdir"):
self.trmdir = os.getenv("TRMDIR")
if self.trmdir is None:
raise EnvironmentError, """ Trmdir is not set.
Add TRMDIR=/path/to/tracmass to your local environment
or specify trmdir when calling Trm."""
def parse(od, pn, cn, sfx):
gridfile = "/%s/projects/%s/%s_%s.in" % (od, pn, cn, sfx)
if not os.path.isfile(gridfile):
raise IOError("Can't find the file %s" % gridfile)
return nlt.parse(gridfile)
self.nlgrid = parse(self.trmdir, self.projname, self.projname, "grid")
self.nlrun = parse(self.trmdir, self.projname, self.casename, "run")
if not hasattr(self, "datadir"):
self.datadir = self.nlrun.outDataDir
if not hasattr(self, "datafile"):
self.datafile = self.nlrun.outDataFile
self.base_iso = pl.date2num(dtm(self.nlgrid.baseYear, self.nlgrid.baseMon, self.nlgrid.baseDay)) - 1
self.imt = self.nlgrid.IMT
self.jmt = self.nlgrid.JMT
def plot_date_time_graph(db_name,table_name):
format='%d %b %Y %I:%M %p'
conn = sqlite3.connect(os.getcwd()+'/data/'+db_name+'.db')
c=conn.cursor()
date_time_arr=[]
tweet_count=[]
for row in c.execute('SELECT date_posted,time_posted From '+table_name):
date_string= ' '.join(row)
date_time_arr.append(datetime.strptime(date_string, format))
for row in c.execute('SELECT retweets From '+table_name):
tweet_count.append(row[0]+1)
y= np.array(tweet_count)
x=np.array(date_time_arr)
N=len(tweet_count)
colors = np.random.rand(N)
numtime = [date2num(t) for t in x]
# plotting the histogram
ax = figure().gca()
x, y, patches = hist(numtime, bins=50,alpha=.5)
print x,y
# adding the labels for the x axis
tks = [num2date(p.get_x()) for p in patches]
xticks(tks,rotation=40)
# formatting the dates on the x axis
ax.xaxis.set_major_formatter(DateFormatter('%d %b %H:%M'))
ax.set_xlabel('Time(dd-mm HH:MM)', fontsize=16)
ax.set_ylabel('Tweet Count', fontsize=16)
show()
def meter_logs(lines):
lists = (line.lower().split(',') for line in lines)
log = (dict(zip(defaultcolnames, el)) for el in lists)
log = field_map(log, 'Time Stamp',
lambda s: pylab.date2num(datetime.strptime(s, "%Y%m%d%H%M%S")))
log = field_map(log, 'Watts', lambda s: float(s))
return log
def calc_mld(files,start,x0=0.0,y0=0.0):
""" Caclulate density-based MLD from a bunch of VTU files
"""
mld = []
times = []
dates = []
for file in files:
try:
os.stat(file)
except:
print("No such file: %s" % file)
sys.exit(1)
# open vtu and derive the field indices of the edge at (x=0,y=0) ordered by depth
u=vtktools.vtu(file)
pos = u.GetLocations()
ind = get_1d_indices(pos, x0, y0)
# from this we can derive the 1D profile of any field like this:
depth = vtktools.arr([-pos[i,2] for i in ind])
# handle time for different types of plots
time = u.GetScalarField('Time')
times.append(time[0]) # seconds
dates.append( date2num(start + timedelta(seconds=time[0])) ) # integer datetime
# grab density profile and calculate MLD_den (using 2 different deviation parameters
d = u.GetScalarField('Density')
den = vtktools.arr( [d[i] * 1000 for i in ind] )
mld.append( calc_mld_den(den, depth) ) #den0 = 0.03 is default
return mld, times, dates
def make_ticket_history_table(env, dates, sorted_events):
"""
This function takes list of dates in milestone and ticket events
then produce a dictionary with key as milestone event and value as
that list of ticket counts each day.
dates is the numerical array of date in UTC time.
sorted_event is dictionary of events that occurs in milestone
"""
#Initialize the count using key in events
tkt_counts = {'Enter': [], 'Leave': [], 'Finish': []}
#initialize the table
for date in dates:
#env.log.info("Date:%s" % (num2date(date),))
for key in tkt_counts:
tkt_counts[key].append(0)
#Create dictionary of list that hold ticket count each day in dates
for event in sorted_events:
#Time in epoch time
date = to_datetime(event[0])
#Get the index of this date in the dates list
index = bisect(dates, date2num(date)) - 1
for key in tkt_counts:
tkt_counts[key][index] = tkt_counts[key][index] + len(
event[1][key])
return tkt_counts
def get_spot_history(self, instance_type, start=None, end=None, plot=False):
if not utils.is_iso_time(start):
raise exception.InvalidIsoDate(start)
if not utils.is_iso_time(end):
raise exception.InvalidIsoDate(end)
hist = self.conn.get_spot_price_history(start_time=start,
end_time=end,
instance_type=instance_type,
product_description="Linux/UNIX")
if not hist:
raise exception.SpotHistoryError(start,end)
dates = [ utils.iso_to_datetime_tuple(i.timestamp) for i in hist]
prices = [ i.price for i in hist ]
maximum = max(prices)
avg = sum(prices)/len(prices)
log.info("Current price: $%.2f" % hist[-1].price)
log.info("Max price: $%.2f" % maximum)
log.info("Average price: $%.2f" % avg)
if plot:
try:
import pylab
pylab.plot_date(pylab.date2num(dates), prices, linestyle='-')
pylab.xlabel('date')
pylab.ylabel('price (cents)')
pylab.title('%s Price vs Date (%s - %s)' % (instance_type, start, end))
pylab.grid(True)
pylab.show()
except ImportError,e:
log.error("Error importing pylab:")
log.error(str(e))
log.error("please check that matplotlib is installed and that:")
log.error(" $ python -c 'import pylab'")
log.error("completes without error")
def refresh(self, fld, fldtype="DAY", jd1=None, jd2=None, delall=False):
""" Read a L3 mapped file and add field to current instance"""
jd1 = pl.datestr2num('2003-01-01') if jd1 is None else jd1
jd2 = int(pl.date2num(dtm.now())) - 1 if jd2 is None else jd2
for jd in np.arange(jd1, jd2):
print " --- %s --- " % pl.num2date(jd).strftime('%Y-%m-%d')
filename = os.path.join(
self.datadir,
self.generate_filename(jd, fld, fldtype) + ".nc")
if delall:
for fn in glob.glob(filename + "*"):
print "Deleted %s" % fn
os.remove(fn)
print "Checking files"
if not os.path.isfile(filename[:-3] + '.npz'):
try:
self.load(fld, fldtype, jd=jd, verbose=True)
except IOError:
print "Downloading failed. Trying to remove old files."
try:
os.remove(filename)
except:
pass
try:
self.load(fld, fldtype, jd=jd, verbose=True)
except:
print(" ### Warning! Failed to add %s ###" %
os.path.basename(filename))
print "\n"
else:
print "found"
def batch_insert():
import batch
def copy(jd):
tr = traj('jplNOW','ftp','/Volumes/keronHD3/ormOut/')
print pl.num2date(jd), jd
tr.load(jd)
tr.remove_satnans()
if len(tr.x>0):
tr.db_copy()
#batch.jdloop(copy,733773.0, 734138.0,3)
for jd in np.arange(733865.0,734138):
dt1 = pl.date2num(dtm.now())
copy(jd)
dt2 = pl.date2num(dtm.now())
print "----------",dt2-dt1
def predict(recentDate, models):
newDates = []
estCPI = []
for i in range(24):
newDates.append(add_months(recentDate, 1))
recentDate = newDates[-1]
mplDates = pylab.date2num(newDates)
for m in models:
for x in mplDates:
estCPI.append(pylab.polyval(m, x))
fig, ax = pylab.subplots()
pylab.rcParams['lines.linewidth'] = 1
pylab.rcParams['axes.titlesize'] = 10
pylab.rcParams['axes.labelsize'] = 10
pylab.rcParams['xtick.labelsize'] = 8
pylab.rcParams['ytick.labelsize'] = 8
pylab.rcParams['xtick.major.size'] = 8
pylab.rcParams['ytick.major.size'] = 8
pylab.rcParams['lines.markersize'] = 5
pylab.rcParams['legend.numpoints'] = 1
pylab.yticks(pylab.arange(min(estCPI), max(estCPI) + 1, 0.5))
ax.fmt_xdata = pylab.DateFormatter('%Y-%m-%d')
ax.set_title('Projected Canada CPI Inflation')
pylab.xlabel('Date')
fig.autofmt_xdate()
pylab.ylabel('Est. CPI')
pylab.plot(newDates, estCPI)
def refresh(self, jd1=None, jd2=None):
""" Read a L3 mapped file and add field to current instance"""
jd1 = self.jdmin if jd1 is None else jd1
jd2 = int(pl.date2num(dtm.now())) - 1 if jd2 is None else jd2
for jd in np.arange(jd1, jd2 + 1):
filename = os.path.join(self.datadir, self.generate_filename(jd))
print " --- %s --- " % pl.num2date(jd).strftime('%Y-%m-%d')
print "Checking %s" % filename + '.bz2'
if not os.path.isfile(filename + '.bz2'):
try:
self.load(jd=jd, verbose=True)
except IOError:
print "Downloading failed. Trying to remove old files."
try:
os.remove(filename)
except:
pass
try:
os.remove(filename + ".bz2")
except:
pass
try:
self.load(jd=jd, verbose=True)
except:
print(" ### Warning! Failed to add %s ###" %
os.path.basename(filename))
print "\n"
else:
print "found"
def parsedate(s):
if len(s) <= 7:
year, month = s.split("-")
result = datetime.datetime(int(year), int(month), 15)
else:
result = parser.parse(s)
return pylab.date2num(result)
def batch_insert():
import batch
def copy(jd):
tr = traj('jplNOW','ftp','/Volumes/keronHD3/ormOut/')
print pl.num2date(jd), jd
tr.load(jd)
tr.remove_satnans()
if len(tr.x>0):
tr.db_copy()
#batch.jdloop(copy,733773.0, 734138.0,3)
for jd in np.arange(733865.0,734138):
dt1 = pl.date2num(dtm.now())
copy(jd)
dt2 = pl.date2num(dtm.now())
print "----------",dt2-dt1
def plot_dwaf_data(realtime, file_name='data_plot.png', gauge=True):
x = pl.date2num(realtime.date)
y = realtime.q
pl.clf()
pl.figure(figsize=(7.5, 4.5))
pl.rc('text', usetex=True)# TEX fonts
pl.plot_date(x,y,'b-',linewidth=1)
pl.grid(which='major')
pl.grid(which='minor')
if gauge:
pl.ylabel(r'Flow rate (m$^3$s$^{-1}$)')
title = 'Real-time flow -- %s [%s]' % (realtime.station_id[0:6], realtime.station_desc)
else:
title = 'Real-time capacity -- %s [%s]' % (realtime.station_id[0:6], realtime.station_desc)
pl.ylabel('Percentage of F.S.C')
labeled_days = DayLocator(interval=3)
ticked_days = DayLocator()
dayfmt = DateFormatter('%d/%m/%Y')
ax = pl.gca()
ax.xaxis.set_major_locator(labeled_days)
ax.xaxis.set_major_formatter(dayfmt)
ax.xaxis.set_minor_locator(ticked_days)
pl.xticks(fontsize=10)
pl.yticks(fontsize=10)
pl.title(title, fontsize=14)
pl.savefig(file_name, dpi=100)
def calc_mld_tke_files(files,start,x0=0.0,y0=0.0):
""" Caclulate tke-based MLD from a bunch of VTU files
"""
mld = []
times = []
dates = []
for file in files:
try:
os.stat(file)
except:
print "No such file: %s" % file
sys.exit(1)
# open vtu and derive the field indices of the edge at (x=0,y=0) ordered by depth
u=vtktools.vtu(file)
pos = u.GetLocations()
ind = get_1d_indices(pos, x0, y0)
# from this we can derive the 1D profile of any field like this:
depth = vtktools.arr([-pos[i,2] for i in ind])
# handle time for different types of plots
time = u.GetScalarField('Time')
times.append(time[0]) # seconds
dates.append( date2num(start + timedelta(seconds=time[0])) ) # integer datetime
# grab density profile and calculate MLD
d = u.GetScalarField('GLSTurbulentKineticEnergy')
tke = vtktools.arr( [d[i] for i in ind] )
mld.append( calc_mld_tke(tke, depth) )
return mld, times, dates
def refresh(self, jd1=None, jd2=None):
""" Read a L3 mapped file and add field to current instance"""
jd1 = self.jdmin if jd1 is None else jd1
jd2 = int(pl.date2num(dtm.now())) - 1 if jd2 is None else jd2
for jd in np.arange(jd1, jd2+1):
filename = os.path.join(self.datadir, self.generate_filename(jd))
print " --- %s --- " % pl.num2date(jd).strftime('%Y-%m-%d')
print "Checking %s" % filename + '.bz2'
if not os.path.isfile(filename + '.bz2'):
try:
self.load(jd=jd, verbose=True)
except IOError:
print "Downloading failed. Trying to remove old files."
try:
os.remove(filename)
except:
pass
try:
os.remove(filename + ".bz2")
except:
pass
try:
self.load(jd=jd,verbose=True)
except:
print (" ### Warning! Failed to add %s ###" %
os.path.basename(filename))
print "\n"
else:
print "found"
def get_spot_history(self, instance_type,
start=None, end=None, plot=False):
if not utils.is_iso_time(start):
raise exception.InvalidIsoDate(start)
if not utils.is_iso_time(end):
raise exception.InvalidIsoDate(end)
hist = self.conn.get_spot_price_history(start_time=start,
end_time=end,
instance_type=instance_type,
product_description="Linux/UNIX")
if not hist:
raise exception.SpotHistoryError(start, end)
dates = [utils.iso_to_datetime_tuple(i.timestamp) for i in hist]
prices = [i.price for i in hist]
maximum = max(prices)
avg = sum(prices) / len(prices)
log.info("Current price: $%.2f" % hist[-1].price)
log.info("Max price: $%.2f" % maximum)
log.info("Average price: $%.2f" % avg)
if plot:
try:
import pylab
pylab.plot_date(pylab.date2num(dates), prices, linestyle='-')
pylab.xlabel('date')
pylab.ylabel('price (cents)')
pylab.title('%s Price vs Date (%s - %s)' % (instance_type,
start, end))
pylab.grid(True)
pylab.show()
except ImportError, e:
log.error("Error importing pylab:")
log.error(str(e))
log.error("please ensure matplotlib is installed and that:")
log.error(" $ python -c 'import pylab'")
log.error("completes without error")
def plot_2d_data(data,depths,time_secs,start_date,file_path,axis_label,finish_date=None,mld_data=None,max_depth=150,interval=3,minimum=None,maximum=None,spacing=None,colour_scale=cm.jet,dates=None):
"""
"""
# turn given 2d-arrays into numpy arrays (in case they are not already)
data = vtktools.arr(data)
time_secs = vtktools.arr(time_secs)
depths = vtktools.arr(depths)
# convert time profiles in seconds into months
start = datetime.strptime(start_date, "%Y-%m-%d %H:%M:%S")
if (dates == None):
dates = time_secs
i = 0
for time in time_secs:
t = float(time[0].item())
dates[i,:] = date2num(start + timedelta(seconds=t))
i += 1
# see if finishing date is given, default to last time given
if (finish_date != None):
finish = datetime.strptime(finish_date, "%Y-%m-%d %H:%M:%S")
else:
finish = dates[-1][0]
# define min/max and spacing of data if not given (so we see all of the data)
if (minimum == None):
minimum = data.min()
minimum = minimum - (0.1*minimum)
if (maximum == None):
maximum = data.max()
maximum = maximum + (0.1*maximum)
if (spacing == None):
spacing = (maximum - minimum) /256.
# plot 2d colour graph...
fig = figure(figsize=(15,8),dpi=90)
ax = fig.add_axes([.1,.18,.9,.7])
cs=ax.contour(dates, depths, data, arange(minimum,maximum,spacing),cmap=colour_scale)
cs=ax.contourf(dates, depths, data, arange(minimum,maximum,spacing),cmap=colour_scale)
pp=colorbar(cs,format='%.2f')
if(mld_data!=None):
ax.plot(dates[:,0],mld_data,'w', alpha=0.7)
dateFmt = mpl.dates.DateFormatter('%m/%Y')
ax.xaxis.set_major_formatter(dateFmt)
monthsLoc = mpl.dates.MonthLocator(interval=interval)
ax.xaxis.set_major_locator(monthsLoc)
labels = ax.get_xticklabels()
for label in labels:
label.set_rotation(30)
ax.set_ylim(max_depth, 0)
ax.set_xlim(start,finish)
pp.set_label(axis_label)
xlabel('Date (mm/yyyy)')
ylabel('Depth (m)')
form = file_path.split('.')[-1].strip()
savefig(file_path, dpi=90,format=form)
close(fig)
