def cmap_builder(name, name2=None, name3=None):
"""return a colormap object compatible with matplotlib
If only parameter **name** is provided, it should be a known matplotlib
colormap name (e.g., jet). If **name2** is provided, then a new colormap
is created going from the color **name** to the color **name2** with a
linear scale. Finally, if **name3** is provided, a linear scaled colormap
is built from color **name** to color **name3** with the intermediate color
being the **name2**
Matplotlib colormap map names
"""
c = Colormap()
# an R colormap
if name and name2 and name3:
return c.cmap_linear(name, name2, name3)
elif name and name2:
return c.cmap_bicolor(name, name2)
elif name == 'heat':
return c.get_cmap_heat()
elif name == 'heat_r':
return c.get_cmap_heat_r()
# matplotlic colormaps
elif name in c.colormaps:
return c.cmap(name)
# some custom diverging colormaps with black in the middle.
elif name in c.diverging_black:
return c.cmap(name)
else:
#valid = c.colormaps + c.diverging_black
txt = "name provided {0} is not recognised. ".format(name)
txt += "\n valid name can be found in colormap.colormap_names"
raise ValueError(txt)
def get_html(self):
# We have 3 colors but sometimes you may have only one or 2.
# This may be an issue with canvasXpress. It seems essential
# to sort the color column so that names are sorted alphabetically
# and to include colors that are present in the sale order
"""try:
self.data.sort_values(by='color', inplace=True)
except:
self.data.sort("color", inplace=True)
"""
# Jinja related
from easydev import get_package_location
env = Environment()
env.loader = jinja2.FileSystemLoader(
get_package_location("gdsctools") + "/gdsctools/data/templates/")
template = env.get_template("scatter.html")
# We need to cireate 20 different colors
from colormap import Colormap
c = Colormap()
cmap = c.cmap_linear("red", "blue", "yellow", N=20)
colors = self.data[self._colname_color]
colors = ["red" for x in self.data[self._colname_color]]
jinja = {}
jinja["colors"] = ["rgba(205,0,0,0.5)"]
jinja["Group"] = colors
jinja['xlabel'] = '"%s"' % self.xlabel
jinja['ylabel'] = '"%s"' % self.ylabel
selection = [self._colname_x, self._colname_y]
text = []
for index in zip(self.data.index):
text.append("<pre>%s</pre>" %
self.data.ix[index][selection].to_string())
jinja['vars'] = text
#self.data.markersize /= (self.data.markersize.max()/3.)
self.data['markersize'] = 20
selection = [self._colname_x, self._colname_y, self._colname_size]
#First value is Y, second is X, following will be used in the
try: # introduced in pandas > 0.16.2
jinja['data'] = self.data[selection].round(3).values.tolist()
except: #for py3.3 on travis
jinja['data'] = np.around(self.data[selection]).values.tolist()
jinja[
'title'] = '"Regression coefficient vs Bayes factor for all drugs"'
jinja['minX'] = self.minX
jinja['minY'] = self.minY
jinja['maxX'] = self.maxX
jinja['maxY'] = self.maxY
self.html = template.render(jinja)
return self.html
def get_html(self):
# We have 3 colors but sometimes you may have only one or 2.
# This may be an issue with canvasXpress. It seems essential
# to sort the color column so that names are sorted alphabetically
# and to include colors that are present in the sale order
"""try:
self.data.sort_values(by='color', inplace=True)
except:
self.data.sort("color", inplace=True)
"""
# Jinja related
from easydev import get_package_location
env = Environment()
env.loader = jinja2.FileSystemLoader(
get_package_location("gdsctools")
+ "/gdsctools/data/templates/")
template = env.get_template("scatter.html")
# We need to cireate 20 different colors
from colormap import Colormap
c = Colormap()
cmap = c.cmap_linear("red", "blue", "yellow", N=20)
colors = self.data[self._colname_color]
colors = ["red" for x in self.data[self._colname_color]]
jinja = {}
jinja["colors"] = ["rgba(205,0,0,0.5)"]
jinja["Group"] = colors
jinja['xlabel'] = '"%s"' % self.xlabel
jinja['ylabel'] = '"%s"' % self.ylabel
text = []
for index in zip(self.data.index):
text.append("<pre>%s</pre>" % self.data.ix[index].to_string())
jinja['vars'] = text
#self.data.markersize /= (self.data.markersize.max()/3.)
self.data['markersize'] = 20
selection = [self._colname_x, self._colname_y, self._colname_size]
#First value is Y, second is X, following will be used in the
try: # introduced in pandas > 0.16.2
jinja['data'] = self.data[selection].round(3).values.tolist()
except: #for py3.3 on travis
jinja['data'] = np.around(self.data[selection]).values.tolist()
jinja['title'] = '"Regression coefficient/ttest/bayes factor for all drugs"'
jinja['minX'] = 0
jinja['minY'] = 0
jinja['maxX'] = 1
jinja['maxY'] = self.data[self._colname_y].max() * 1.1
self.html = template.render(jinja)
return self.html
def cmap_builder(name, name2=None, name3=None):
"""return a colormap object compatible with matplotlib
If only parameter **name** is provided, it should be a known matplotlib
colormap name (e.g., jet). If **name2** is provided, then a new colormap
is created going from the color **name** to the color **name2** with a
linear scale. Finally, if **name3** is provided, a linear scaled colormap
is built from color **name** to color **name3** with the intermediate color
being the **name2**
Matplotlib colormap map names
"""
c = Colormap()
# an R colormap
if name and name2 and name3:
return c.cmap_linear(name, name2, name3)
elif name and name2:
return c.cmap_bicolor(name, name2)
elif name == 'heat':
return c.get_cmap_heat()
elif name == 'heat_r':
return c.get_cmap_heat_r()
# matplotlic colormaps
elif name in c.colormaps:
return c.cmap(name)
# some custom diverging colormaps with black in the middle.
elif name in c.diverging_black:
return c.cmap(name)
elif name.count("_") == 2:
name1, name2, name3 = name.split("_")
return c.cmap_linear(name1, name2, name3)
else:
#valid = c.colormaps + c.diverging_black
txt = "name provided {0} is not recognised. ".format(name)
txt += "\n valid name can be found in colormap.colormap_names"
raise ValueError(txt)
imnum = 1
vidnum = 1
exposure = float(10000.0) # value in microseconds
avg = 0
t0 = time.time()
t, avg1, avg2, avg3, oldt, oldavg1, oldavg2, oldavg3, oldert, olderavg1, olderavg2, olderavg3 = [], [], [], [], [], [], [], [], [], [], [], []
x1, y1, x2, y2 = 0, 0, 640, 480
a1, b1, a2, b2 = 0, 0, 640, 480
c1, d1, c2, d2 = 0, 0, 640, 480
i = 1
red, green, blue = True, False, False
path = 'C:/Users/Palmstrom Lab/Desktop/FRHEED/'
filename = 'default'
#### Define colormap ####
cmp = Colormap()
FRHEEDcmap = cmp.cmap_linear('black', 'green', 'white')
cm.register_cmap(name='RHEEDgreen', cmap=FRHEEDcmap)
cmap = cm.gray
#cmp.test_colormap(FRHEEDcmap)
#### Loading UI file from qt designer ####
form_class = uic.loadUiType("FRHEED backup.ui")[0]
#### Define "shortcut" for queue ####
q = queue.Queue()
#### Define video recording codec ####
fourcc = cv2.VideoWriter_fourcc(*'MP4V')
#### Set default appearance of plots ####
pg.setConfigOption('background', 'w')
pg.setConfigOption('foreground', 0.0)
#### Connect to RHEED camera ####
system = PySpin.System.GetInstance()
cam_list = system.GetCameras()
def make_u_plot(u_fname,
make_single,
make_with_metric,
label_p,
u_zonal=None,
u_zonal_NH=None,
Method=None,
Method_NH=None,
lat_elem_SH=None,
lat_elem_NH=None,
STJ_lat=None,
method_choice=None,
time=None,
file_out_syntax=None):
open_data = False
if open_data:
#open jet data from file
filename = '{0}/STJ_data_{1}.nc'.format(data_out_dir, file_out_syntax)
assert os.path.isfile(
filename
), 'File ' + filename + ' does not exist. Need jet latitude for plotting.'
var_jet = openNetCDF4_get_data(filename)
jet_NH = var_jet['STJ_lat'][:, 0]
jet_SH = var_jet['STJ_lat'][:, 1]
else:
#use data just calculated
jet_NH = STJ_lat[0]
jet_SH = STJ_lat[1]
#open u wind data - on pressure levels
assert os.path.isfile(u_fname), 'File ' + u_fname + ' does not exist.'
var = openNetCDF4_get_data(u_fname)
# check that input pressure is in pascal
if var[label_p].max() < 90000.0:
var[label_p] = var[label_p] * 100.0
lev250 = FindClosestElem(25000, var[label_p], 0)[0]
if 'var131' in var:
uwnd = var['var131'][time, lev250, :, :]
else:
uwnd = var['u'][time, lev250, :, :]
if make_single:
fname_out = '{}/uwind_{}_with_wind_{}.eps'.format(
plot_dir, t_elem[t], file_out_syntax)
fig = plt.figure(figsize=(10, 5))
# wind plot
ax1 = plt.subplot2grid((10, 20), (0, 0), colspan=16, rowspan=9)
# zonal mean
ax2 = plt.subplot2grid((10, 20), (0, 16), colspan=5, rowspan=9)
# colour bar
ax_cb = plt.subplot2grid((10, 20), (9, 0), colspan=20)
save_plot = True
plot_u(plt,
ax1,
ax2,
ax_cb,
jet_NH,
jet_SH,
uwnd,
var,
t,
t_elem,
fname_out,
save_plot,
make_single=make_single)
if make_with_metric:
fname_out = '{}/validation_uwind_{}_{}.eps'.format(
plot_dir, time, file_out_syntax)
fig = plt.figure(figsize=(10, 12))
#SH
ax1 = plt.subplot2grid(
(37, 26), (0, 0), rowspan=22,
colspan=12) #half across/gap/half accross. 18 deep
#NH
ax2 = plt.subplot2grid((37, 26), (0, 12), rowspan=22, colspan=12)
# wind plot
ax3 = plt.subplot2grid((37, 26), (22, 0), rowspan=13, colspan=20)
# zonal mean
ax4 = plt.subplot2grid((37, 26), (22, 20), rowspan=13, colspan=5)
# colour bar
ax_cb = plt.subplot2grid((37, 26), (35, 0), rowspan=2, colspan=37)
bounds = np.arange(-50, 60, 5.0)
cm = Colormap()
cmap = cm.cmap_linear('#0033ff', '#FFFFFF', '#990000') #blue/red
#cmap = pylab.cm.get_cmap('RdBu_r')
PlottingObject = Plotting(Method, 'cby')
plot_cbar = False
#u_zonal = MeanOverDim(data=uwnd, dim=1)
PlottingObject.poly_2PV_line('SH',
u_zonal,
lat_elem_SH,
time,
fig,
ax1,
plot_cbar,
ax_cb,
bounds,
cmap,
plot_type='subplot_all',
pause=False,
click=False,
save_plot=False)
# NH
PlottingObject2 = Plotting(Method_NH, method_choice)
PlottingObject2.poly_2PV_line('NH',
u_zonal_NH,
lat_elem_NH,
time,
fig,
ax2,
plot_cbar,
None,
bounds,
cmap,
plot_type='subplot_all',
pause=False,
click=False,
save_plot=False)
save_plot = True
plot_u(plt,
ax3,
ax4,
ax_cb,
jet_NH,
jet_SH,
uwnd,
var,
bounds,
cmap,
time,
fname_out,
save_plot,
make_single=False)
