def plot_3d_scatter(self, fig, ax, x, y, z, title='', xlabel='', ylabel='', zlabel='',
title_font={}, axis_font={}, tick_font={}):
if not title_font:
title_font = title_font_default
if not axis_font:
axis_font = axis_font_default
cmap = plt.cm.jet
h = plt.scatter(x, y, c=z, cmap=cmap)
ax.set_aspect(1./ax.get_data_ratio()) # make axes square
cbar = plt.colorbar(h, orientation='vertical', aspect=30, shrink=0.9)
if xlabel:
ax.set_xlabel(xlabel.replace("_", " "), labelpad=10, **axis_font)
if ylabel:
ax.set_ylabel(ylabel.replace("_", " "), labelpad=10, **axis_font)
if zlabel:
cbar.ax.set_ylabel(zlabel.replace("_", " "), labelpad=10, **axis_font)
if tick_font:
ax.tick_params(**tick_font)
if title:
ax.set_title(title.replace("_", " "), **title_font)
ax.grid(True)
plt.tight_layout()
def plot_stacked_time_series(self, fig, ax, x, y, z, title='', ylabel='',
cbar_title='', title_font={}, axis_font={}, tick_font = {},
**kwargs):
if not title_font:
title_font = title_font_default
if not axis_font:
axis_font = axis_font_default
z = np.ma.array(z, mask=np.isnan(z))
h = plt.pcolormesh(x, y, z, shading='gouraud', **kwargs)
# h = plt.pcolormesh(x, y, z, **kwargs)
if ylabel:
ax.set_ylabel(ylabel.replace("_", " "), **axis_font)
if title:
ax.set_title(title.replace("_", " "), **title_font)
plt.axis([x.min(), x.max(), y.min(), y.max()])
ax.xaxis_date()
date_list = mdates.num2date(x)
self.get_time_label(ax, date_list)
fig.autofmt_xdate()
ax.invert_yaxis()
cbar = plt.colorbar(h)
if cbar_title:
cbar.ax.set_ylabel(cbar_title, **axis_font)
ax.grid(True)
if tick_font:
ax.tick_params(**tick_font)
plt.tight_layout()
def plot_profile(self,
fig,
ax,
x,
y,
xlabel='',
ylabel='',
axis_font={},
tick_font={},
scatter=False,
**kwargs):
if not axis_font:
axis_font = axis_font_default
if scatter:
ppl.scatter(ax, x, y, **kwargs)
else:
ppl.plot(ax, x, y, **kwargs)
if xlabel:
ax.set_xlabel(xlabel.replace("_", " "), labelpad=5, **axis_font)
if ylabel:
ax.set_ylabel(ylabel.replace("_", " "), labelpad=11, **axis_font)
if tick_font:
ax.tick_params(**tick_font)
ax.xaxis.set_label_position('top') # this moves the label to the top
ax.xaxis.set_ticks_position('top')
ax.xaxis.get_major_locator()._nbins = 5
ax.grid(True)
plt.tight_layout()
def plot_days(days):
nplots = len(days)
fig, subplots = plt.subplots(nplots, figsize=(10, 50))
for i, day in enumerate(days):
subplot = subplots[i]
plot_day(day, subplot)
plt.tight_layout()
plt.savefig('./plots/best.png')
def plot_days(days):
nplots = len(days)
fig, subplots = plt.subplots(nplots, figsize=(10, 50))
for i, day in enumerate(days):
subplot = subplots[i]
plot_day(day, subplot)
plt.tight_layout()
plt.savefig('./plots/best.png')
def plot_1d_quiver(self,
fig,
ax,
time,
u,
v,
title='',
ylabel='',
title_font={},
axis_font={},
tick_font={},
legend_title="Magnitude",
start=None,
end=None,
**kwargs):
if not title_font:
title_font = title_font_default
if not axis_font:
axis_font = axis_font_default
# Plot quiver
magnitude = (u**2 + v**2)**0.5
maxmag = max(magnitude)
ax.set_ylim(-maxmag, maxmag)
dx = time[-1] - time[0]
if start and end:
ax.set_xlim(start - 0.05 * dx, end + 0.05 * dx)
else:
ax.set_xlim(time[0] - 0.05 * dx, time[-1] + 0.05 * dx)
# ax.fill_between(time, magnitude, 0, color='k', alpha=0.1)
# # Fake 'box' to be able to insert a legend for 'Magnitude'
# p = ax.add_patch(plt.Rectangle((1, 1), 1, 1, fc='k', alpha=0.1))
# leg1 = ax.legend([p], [legend_title], loc='lower right')
# leg1._drawFrame = False
# # 1D Quiver plot
q = ax.quiver(time, 0, u, v, **kwargs)
plt.quiverkey(q,
0.2,
0.05,
0.2,
r'$0.2 \frac{m}{s}$',
labelpos='W',
fontproperties={'weight': 'bold'})
ax.xaxis_date()
date_list = mdates.num2date(time)
self.get_time_label(ax, date_list)
fig.autofmt_xdate()
if ylabel:
ax.set_ylabel(ylabel.replace("_", " "), labelpad=20, **axis_font)
if tick_font:
ax.tick_params(**tick_font)
ax.set_title(title.replace("_", " "), **title_font)
plt.tight_layout()
def plot_time_series(self, fig, is_timeseries, ax, x, y, fill=False, title='', xlabel='', ylabel='',
title_font={}, axis_font={}, tick_font={}, scatter=False, qaqc=[], events={}, **kwargs):
if not title_font:
title_font = title_font_default
if not axis_font:
axis_font = axis_font_default
if scatter:
ppl.scatter(ax, x, y, **kwargs)
else:
h = ppl.plot(ax, x, y, **kwargs)
if is_timeseries:
self.get_time_label(ax, x)
fig.autofmt_xdate()
else:
ax.set_xlabel(xlabel.replace("_", " "), **axis_font)
if ylabel:
ax.set_ylabel(ylabel.replace("_", " "), **axis_font)
if title:
ax.set_title(title.replace("_", " "), **title_font)
ax.grid(True)
if fill:
miny = min(ax.get_ylim())
if not scatter:
ax.fill_between(x, y, miny+1e-7, facecolor = h[0].get_color(), alpha=0.15)
else:
ax.fill_between(x, y, miny+1e-7, facecolor = axis_font_default['color'], alpha=0.15)
if events:
ylim = ax.get_ylim()
for event in events['events']:
time = datestr2num(event['start_date'])
x = np.array([time, time])
h = ax.plot(x, ylim, '--', label=event['class'])
legend = ax.legend()
if legend:
for label in legend.get_texts():
label.set_fontsize(10)
if len(qaqc) > 0:
bad_data = np.where(qaqc > 0)
h = ppl.plot(ax, x[bad_data], y[bad_data],
marker='o',
mfc='none',
linestyle='None',
markersize=6,
markeredgewidth=2,
mec='r')
# plt.tick_params(axis='both', which='major', labelsize=10)
if tick_font:
ax.tick_params(**tick_font)
plt.tight_layout()
def plot_1d_quiver(self, fig, ax, time, u, v, title='', ylabel='',
title_font={}, axis_font={}, tick_font={}, key_units='m/s',
legend_title="Magnitude", start=None, end=None, **kwargs):
if not title_font:
title_font = title_font_default
if not axis_font:
axis_font = axis_font_default
# Plot quiver
magnitude = (u**2 + v**2)**0.5
maxmag = max(magnitude)
ax.set_ylim(-maxmag, maxmag)
dx = time[-1] - time[0]
if start and end:
ax.set_xlim(start - 0.05 * dx, end + 0.05 * dx)
else:
ax.set_xlim(time[0] - 0.05 * dx, time[-1] + 0.05 * dx)
# ax.fill_between(time, magnitude, 0, color='k', alpha=0.1)
# # Fake 'box' to be able to insert a legend for 'Magnitude'
# p = ax.add_patch(plt.Rectangle((1, 1), 1, 1, fc='k', alpha=0.1))
# leg1 = ax.legend([p], [legend_title], loc='lower right')
# leg1._drawFrame = False
mean_val = np.mean(magnitude)
# Quick conversion of most popular key_units
if key_units == 'm s-1':
key_units = 'm/s'
key_str = '{0:.2f} {1}'.format(mean_val, key_units)
# 1D Quiver plot
q = ax.quiver(time, 0, u, v, **kwargs)
plt.quiverkey(q, 0.1, 0.05, mean_val,
key_str,
labelpos='W',
fontproperties={'weight': 'light',
'style': 'italic',
'size': 'small',
'stretch': 'condensed'})
ax.xaxis_date()
date_list = mdates.num2date(time)
self.get_time_label(ax, date_list)
fig.autofmt_xdate()
if ylabel:
ax.set_ylabel(ylabel.replace("_", " "), labelpad=20, **axis_font)
if tick_font:
ax.tick_params(**tick_font)
ax.set_title(title.replace("_", " "), **title_font)
plt.tight_layout()
def plot_ts_diagram(self, ax, sal, temp, xlabel='Salinity', ylabel='Temperature', title='',
axis_font={}, title_font={}, tick_font={}, **kwargs):
if not axis_font:
axis_font = axis_font_default
if not title_font:
title_font = title_font_default
sal = np.ma.array(sal, mask=np.isnan(sal))
temp = np.ma.array(temp, mask=np.isnan(temp))
if len(sal) != len(temp):
raise Exception('Sal and Temp arrays are not the same size!')
# Figure out boudaries (mins and maxs)
smin = sal.min() - (0.01 * sal.min())
smax = sal.max() + (0.01 * sal.max())
tmin = temp.min() - (0.1 * temp.max())
tmax = temp.max() + (0.1 * temp.max())
# Calculate how many gridcells we need in the x and y dimensions
xdim = round((smax-smin)/0.1+1, 0)
ydim = round((tmax-tmin)+1, 0)
# Create empty grid of zeros
dens = np.zeros((ydim, xdim))
# Create temp and sal vectors of appropiate dimensions
ti = np.linspace(1, ydim-1, ydim)+tmin
si = np.linspace(1, xdim-1, xdim)*0.1+smin
# Loop to fill in grid with densities
for j in range(0, int(ydim)):
for i in range(0, int(xdim)):
dens[j, i] = sw.dens(si[i], ti[j], 0)
# Substract 1000 to convert to sigma-t
dens = dens - 1000
# Plot data
cs = plt.contour(si, ti, dens, linestyles='dashed', colors='k')
plt.clabel(cs, fontsize=12, inline=1, fmt='%1.0f') # Label every second level
ppl.scatter(ax, sal, temp, **kwargs)
ax.set_xlabel(xlabel.replace("_", " "), labelpad=10, **axis_font)
ax.set_ylabel(ylabel.replace("_", " "), labelpad=10, **axis_font)
ax.set_title(title.replace("_", " "), **title_font)
ax.set_aspect(1./ax.get_data_ratio()) # make axes square
if tick_font:
ax.tick_params(**tick_font)
plt.tight_layout()
def layerActivations(model, data, labels):
print('Visualizing activations with tSNE...')
if not os.path.exists(cc.cfg['plots']['layer_activations_dir']):
os.makedirs(cc.cfg['plots']['layer_activations_dir'])
numLabels = cc.cfg['plots']['layer_activations_label_cap']
data = data[:cc.cfg['plots']['layer_activations_points_cap']]
labels = labels[:numLabels,:cc.cfg['plots']['layer_activations_points_cap']]
subplotCols = numLabels
subplotRows = len(model.layers)-1
subplotIdx = 1
plt.figure(figsize=(5*subplotCols,5*subplotRows))
for i in range(1,len(model.layers)):
print('Running tSNE for layer {}/{}'.format(i+1,len(model.layers)))
func = K.function([model.layers[0].input], [model.layers[i].output])
out = func([data])[0]
tsneModel = TSNE(n_components = 2, random_state = 0)
tsneOut = tsneModel.fit_transform(out).T
# labeledTsneOut = np.hstack((tsneOut, labels[0].reshape(-1,1)))
for j in range(numLabels):
plt.subplot(subplotRows, subplotCols, subplotIdx)
plt.title('{} / {}'.format(model.layers[i].name,cc.exp['params']['data']['labels'][j]))
plt.scatter(tsneOut[0],tsneOut[1],c=labels[j],cmap = 'plasma')
subplotIdx += 1
# tsneDF = pd.DataFrame(labeledTsneOut, columns = ('a', 'b', 'c'))
# plot = tsneDF.plot.scatter(x = 'a', y = 'b', c = 'c', cmap = 'plasma')
plt.tight_layout()
plt.savefig('{}/activations.png'.format(cc.cfg['plots']['layer_activations_dir']))
plt.close()
print('...done')
def plot_1d_quiver(self, fig, ax, time, u, v, title='', ylabel='',
title_font={}, axis_font={}, tick_font={},
legend_title="Magnitude", start=None, end=None, **kwargs):
if not title_font:
title_font = title_font_default
if not axis_font:
axis_font = axis_font_default
# Plot quiver
magnitude = (u**2 + v**2)**0.5
maxmag = max(magnitude)
ax.set_ylim(-maxmag, maxmag)
dx = time[-1] - time[0]
if start and end:
ax.set_xlim(start - 0.05 * dx, end + 0.05 * dx)
else:
ax.set_xlim(time[0] - 0.05 * dx, time[-1] + 0.05 * dx)
# ax.fill_between(time, magnitude, 0, color='k', alpha=0.1)
# # Fake 'box' to be able to insert a legend for 'Magnitude'
# p = ax.add_patch(plt.Rectangle((1, 1), 1, 1, fc='k', alpha=0.1))
# leg1 = ax.legend([p], [legend_title], loc='lower right')
# leg1._drawFrame = False
# # 1D Quiver plot
q = ax.quiver(time, 0, u, v, **kwargs)
plt.quiverkey(q, 0.2, 0.05, 0.2,
r'$0.2 \frac{m}{s}$',
labelpos='W',
fontproperties={'weight': 'bold'})
ax.xaxis_date()
date_list = mdates.num2date(time)
self.get_time_label(ax, date_list)
fig.autofmt_xdate()
if ylabel:
ax.set_ylabel(ylabel.replace("_", " "), labelpad=20, **axis_font)
if tick_font:
ax.tick_params(**tick_font)
ax.set_title(title.replace("_", " "), **title_font)
plt.tight_layout()
def histograms(modelLogger):
if not cc.cfg['plots']['histograms']:
return
if not os.path.exists(cc.cfg['plots']['histograms_dir']):
os.makedirs(cc.cfg['plots']['histograms_dir'])
cntEpochs = len(modelLogger.epochLogs)
cntLayers = len(modelLogger.epochLogs[-1]['weights'])
logVals = [
{'name':'weights','color':'blue'},
{'name':'updates','color':'red'},
{'name':'ratios','color':'green'}
]
subplotRows = len(logVals)
subplotCols = cntLayers
for x in range(cntEpochs):
subplotIdx = 1
plt.figure(figsize=(5*subplotCols,5*subplotRows))
plt.suptitle('Histograms per layer, epoch {}/{}'.format(x,cntEpochs-1), fontsize=14)
for logVal in logVals:
for i,layer in enumerate(modelLogger.epochLogs[x][logVal['name']]):
histmin = layer.min()
histmax = layer.max()
plt.subplot(subplotRows, subplotCols, subplotIdx)
plt.title('{}, {}'.format(modelLogger.model.layers[modelLogger.loggedLayers[i]].name,logVal['name']))
plt.hist(layer, range = (histmin, histmax), bins = 30, color = logVal['color'])
subplotIdx+=1
plt.tight_layout()
plt.subplots_adjust(top=0.9)
plt.savefig('{}/hist_{e:03d}.png'.format(cc.cfg['plots']['histograms_dir'], e = x))
plt.close()
def plot_profile(self, fig, ax, x, y, xlabel='', ylabel='',
axis_font={}, tick_font={}, scatter=False, **kwargs):
if not axis_font:
axis_font = axis_font_default
if scatter:
ppl.scatter(ax, x, y, **kwargs)
else:
ppl.plot(ax, x, y, **kwargs)
if xlabel:
ax.set_xlabel(xlabel.replace("_", " "), labelpad=5, **axis_font)
if ylabel:
ax.set_ylabel(ylabel.replace("_", " "), labelpad=11, **axis_font)
if tick_font:
ax.tick_params(**tick_font)
ax.xaxis.set_label_position('top') # this moves the label to the top
ax.xaxis.set_ticks_position('top')
ax.xaxis.get_major_locator()._nbins = 5
ax.grid(True)
plt.tight_layout()
def plot_stacked_time_series(self,
fig,
ax,
x,
y,
z,
title='',
ylabel='',
cbar_title='',
title_font={},
axis_font={},
tick_font={},
**kwargs):
if not title_font:
title_font = title_font_default
if not axis_font:
axis_font = axis_font_default
z = np.ma.array(z, mask=np.isnan(z))
h = plt.pcolormesh(x, y, z, shading='gouraud', **kwargs)
# h = plt.pcolormesh(x, y, z, **kwargs)
if ylabel:
ax.set_ylabel(ylabel.replace("_", " "), **axis_font)
if title:
ax.set_title(title.replace("_", " "), **title_font)
plt.axis([x.min(), x.max(), y.min(), y.max()])
ax.xaxis_date()
date_list = mdates.num2date(x)
self.get_time_label(ax, date_list)
fig.autofmt_xdate()
ax.invert_yaxis()
cbar = plt.colorbar(h)
if cbar_title:
cbar.ax.set_ylabel(cbar_title)
ax.grid(True)
if tick_font:
ax.tick_params(**tick_font)
plt.tight_layout()
def plot_3d_scatter(self,
fig,
ax,
x,
y,
z,
title='',
xlabel='',
ylabel='',
zlabel='',
title_font={},
axis_font={},
tick_font={}):
if not title_font:
title_font = title_font_default
if not axis_font:
axis_font = axis_font_default
cmap = plt.cm.jet
h = plt.scatter(x, y, c=z, cmap=cmap)
ax.set_aspect(1. / ax.get_data_ratio()) # make axes square
cbar = plt.colorbar(h, orientation='vertical', aspect=30, shrink=0.9)
if xlabel:
ax.set_xlabel(xlabel.replace("_", " "), labelpad=10, **axis_font)
if ylabel:
ax.set_ylabel(ylabel.replace("_", " "), labelpad=10, **axis_font)
if zlabel:
cbar.ax.set_ylabel(zlabel.replace("_", " "),
labelpad=10,
**axis_font)
if tick_font:
ax.tick_params(**tick_font)
if title:
ax.set_title(title.replace("_", " "), **title_font)
ax.grid(True)
plt.tight_layout()
def plot_ts_diagram(self,
ax,
sal,
temp,
xlabel='Salinity',
ylabel='Temperature',
title='',
axis_font={},
title_font={},
tick_font={},
**kwargs):
if not axis_font:
axis_font = axis_font_default
if not title_font:
title_font = title_font_default
sal = np.ma.array(sal, mask=np.isnan(sal))
temp = np.ma.array(temp, mask=np.isnan(temp))
if len(sal) != len(temp):
raise Exception('Sal and Temp arrays are not the same size!')
# Figure out boudaries (mins and maxs)
smin = sal.min() - (0.01 * sal.min())
smax = sal.max() + (0.01 * sal.max())
tmin = temp.min() - (0.1 * temp.max())
tmax = temp.max() + (0.1 * temp.max())
# Calculate how many gridcells we need in the x and y dimensions
xdim = round((smax - smin) / 0.1 + 1, 0)
ydim = round((tmax - tmin) + 1, 0)
# Create empty grid of zeros
dens = np.zeros((ydim, xdim))
# Create temp and sal vectors of appropiate dimensions
ti = np.linspace(1, ydim - 1, ydim) + tmin
si = np.linspace(1, xdim - 1, xdim) * 0.1 + smin
# Loop to fill in grid with densities
for j in range(0, int(ydim)):
for i in range(0, int(xdim)):
dens[j, i] = sw.dens(si[i], ti[j], 0)
# Substract 1000 to convert to sigma-t
dens = dens - 1000
# Plot data
cs = plt.contour(si, ti, dens, linestyles='dashed', colors='k')
plt.clabel(cs, fontsize=12, inline=1,
fmt='%1.0f') # Label every second level
ppl.scatter(ax, sal, temp, **kwargs)
ax.set_xlabel(xlabel.replace("_", " "), labelpad=10, **axis_font)
ax.set_ylabel(ylabel.replace("_", " "), labelpad=10, **axis_font)
ax.set_title(title.replace("_", " "), **title_font)
ax.set_aspect(1. / ax.get_data_ratio()) # make axes square
if tick_font:
ax.tick_params(**tick_font)
plt.tight_layout()
def main():
description = """Simple bar plot of table. Input is a table from a file or
standard input."""
parser = argparse.ArgumentParser(
description=description,
formatter_class=RawTextHelpFormatter
)
parser.add_argument(
'-f',
'--filename',
action='store',
metavar='table.csv',
help='''A table of the format:
x_title, y_title, Main_title
x_label1, value1
x_label2, value2''',
required=False,
dest='filename',
)
args = parser.parse_args()
if args.filename:
filename = args.filename.strip()
else:
parser.print_help()
sys.exit(1)
y = []
x = []
for line in open(filename, "r").readlines():
line = line.strip()
res = re.search("[0-9]+", line)
if not res:
line = line.split(",")
x_lab = line[0]
y_lab = line[1]
main = line[2]
else:
line = line.split(",")
if len(line) < 2:
y.append(float(line[0]))
else:
x.append(line[0])
y.append(float(line[1]))
print(line)
if len(x) < 1:
x = range(1, len(y) + 1)
plt.rc('font', **{'family': 'DejaVu Sans'})
fig, ax = plt.subplots(1, figsize=(8, 6))
width = 0.2
ind = np.arange(len(y))
xdata = ind + 0.05 + width
ax.bar(ind, y)
ax.set_xticks(ind + 0.5)
ax.set_xticklabels(x)
ax.autoscale()
ax.set_title(
main,
fontdict={'fontsize': 20},
)
plt.ylabel(y_lab, fontdict={'fontsize': 15})
plt.xlabel(x_lab, fontdict={'fontsize': 15})
plt.tick_params(axis="y", which="major", labelsize=10)
plt.tick_params(axis="x", which="major", labelsize=10)
ppl.bar(ax, np.arange(len(y)), y, grid="y")
plt.tight_layout()
print("The plot has been saved as ``out.png``")
fig.savefig("out.png")
import numpy as np
import pandas as pd
from datetime import *
from prettyplotlib import plt
a001=pd.read_csv('A001.txt',index_col=[0],parse_dates=[0])
r001=pd.read_csv('R001.txt',index_col=[0],parse_dates=[0])
df=pd.DataFrame({'TAIEX':a001['cp'],'R001':r001['cp']})
ax=df.plot()
plt.tight_layout()
plt.show()
ret1=pd.DataFrame({'TAIFEX':a001['cp'],'TotRet':r001['cp']})
ret1=(np.log(ret1/ret1.shift(1))*100).dropna()
df=ret1.groupby(lambda x:x.year).sum()
df['diff']=df['TotRet']-df['TAIFEX']
df
r001['r1'] = np.log(r001['cp'] / r001['cp'].shift(1))*100
r001['r5'] = pd.stats.moments.rolling_sum(r001['r1'], 5)
r001['r20'] = pd.stats.moments.rolling_sum(r001['r1'], 20)
r001.dropna().head()
ret = r001[['r1','r5','r20']].dropna()
def plot_multiple_streams(self, fig, ax, datasets, colors, axis_font={}, title_font={},
tick_font={}, width_in=8.3 , plot_qaqc=0, scatter=False, **kwargs):
# Plot a timeseries with multiple y-axes using multiple streams from uFrame
#
# Acknowledgment: This function is based on code written by Jae-Joon Lee,
# URL= http://matplotlib.svn.sourceforge.net/viewvc/matplotlib/trunk/matplotlib/
# examples/pylab_examples/multiple_yaxis_with_spines.py?revision=7908&view=markup
#
# http://matplotlib.org/examples/axes_grid/demo_parasite_axes2.html
if not axis_font:
axis_font = axis_font_default
if not title_font:
title_font = title_font_default
n_vars = len(datasets)
if n_vars > 6:
raise Exception('This code currently handles a maximum of 6 independent variables.')
elif n_vars < 2:
raise Exception('This code currently handles a minimum of 2 independent variables.')
if scatter:
kwargs['marker'] = 'o'
# Generate the plot.
# Use twinx() to create extra axes for all dependent variables except the first
# (we get the first as part of the ax axes).
y_axis = n_vars * [0]
y_axis[0] = ax
for i in range(1, n_vars):
y_axis[i] = ax.twinx()
ax.spines["top"].set_visible(False)
self.make_patch_spines_invisible(y_axis[1])
self.set_spine_direction(y_axis[1], "top")
# Define the axes position offsets for each 'extra' axis
spine_directions = ["left", "right", "left", "right", "left", "right"]
# Adjust the axes left/right accordingly
if n_vars >= 4:
if width_in < 8.3:
# set axis location
offset = [1.2, -0.2, 1.40, -0.40]
# overwrite width
l_mod = 0.3
r_mod = 0.8
else:
offset = [1.10, -0.10, 1.20, -0.20]
l_mod = 0.5
r_mod = 1.2
plt.subplots_adjust(left=l_mod, right=r_mod)
elif n_vars == 3:
offset = [1.20, -0.20, 1.40, -0.40]
plt.subplots_adjust(left=0.0, right=0.7)
count = 0
for i in range(2, n_vars):
y_axis[i].spines[spine_directions[count+1]].set_position(("axes", offset[count]))
self.make_patch_spines_invisible(y_axis[i])
self.set_spine_direction(y_axis[i], spine_directions[count+1])
count += 1
# Plot the data
legend_handles = []
legend_labels = []
for ind, data in enumerate(datasets):
xlabel = data['x_field'][0]
ylabel = data['y_field'][0]
xdata = data['x'][xlabel]
ydata = data['y'][ylabel]
# Handle the QAQC data
qaqc = data['qaqc'][ylabel]
if plot_qaqc >= 10:
# Plot all of the qaqc flags results
# qaqc_data = data['qaqc'][ylabel]
pass
elif plot_qaqc >= 1:
# This is a case where the user wants to plot just one of the 9 QAQC tests
ind = np.where(qaqc != plot_qaqc)
qaqc[ind] = 0
else:
qaqc = []
h, = y_axis[ind].plot(xdata, ydata, colors[ind], label=data['title'], **kwargs)
if len(qaqc) > 0:
bad_data = np.where(qaqc > 0)
y_axis[ind].plot(xdata[bad_data], ydata[bad_data],
marker='o',
mfc='none',
linestyle='None',
markersize=6,
markeredgewidth=2,
mec='r')
# Label the y-axis and set text color:
# Been experimenting with other ways to handle tick labels with spines
y_axis[ind].yaxis.get_major_formatter().set_useOffset(False)
y_axis[ind].set_ylabel(ylabel.replace("_", " "), labelpad=10, **axis_font)
y_axis[ind].yaxis.label.set_color(colors[ind])
y_axis[ind].spines[spine_directions[ind]].set_color(colors[ind])
if tick_font:
labelsize = tick_font['labelsize']
y_axis[ind].tick_params(axis='y', labelsize=labelsize, colors=colors[ind])
legend_handles.append(h)
legend_labels.append(data['title'][0:20])
self.get_time_label(ax, xdata)
fig.autofmt_xdate()
ax.legend(legend_handles, legend_labels)
# ax.tick_params(axis='x', labelsize=10)
# ax.set_title(title.replace("_", " "), y=1.05, **title_font)
ax.grid(True)
plt.tight_layout()
def plot_multiple_yaxes(self, fig, ax, xdata, ydata, colors, title, units=[], scatter=False,
axis_font={}, title_font={}, tick_font={}, width_in=8.3, qaqc={}, **kwargs):
# Plot a timeseries with multiple y-axes
#
# ydata is a python dictionary of all the data to plot. Key values are used as plot labels
#
# Acknowledgment: This function is based on code written by Jae-Joon Lee,
# URL= http://matplotlib.svn.sourceforge.net/viewvc/matplotlib/trunk/matplotlib/
# examples/pylab_examples/multiple_yaxis_with_spines.py?revision=7908&view=markup
#
# http://matplotlib.org/examples/axes_grid/demo_parasite_axes2.html
if not axis_font:
axis_font = axis_font_default
if not title_font:
title_font = title_font_default
n_vars = len(ydata)
if n_vars > 6:
raise Exception('This code currently handles a maximum of 6 independent variables.')
elif n_vars < 2:
raise Exception('This code currently handles a minimum of 2 independent variables.')
if scatter:
kwargs['marker'] = 'o'
# Generate the plot.
# Use twinx() to create extra axes for all dependent variables except the first
# (we get the first as part of the ax axes).
y_axis = n_vars * [0]
y_axis[0] = ax
for i in range(1, n_vars):
y_axis[i] = ax.twinx()
ax.spines["top"].set_visible(False)
self.make_patch_spines_invisible(y_axis[1])
self.set_spine_direction(y_axis[1], "top")
# Define the axes position offsets for each 'extra' axis
spine_directions = ["left", "right", "left", "right", "left", "right"]
# Adjust the axes left/right accordingly
if n_vars >= 4:
if width_in < 8.3:
# set axis location
offset = [1.2, -0.2, 1.40, -0.40]
# overwrite width
l_mod = 0.3
r_mod = 0.8
else:
offset = [1.10, -0.10, 1.20, -0.20]
l_mod = 0.5
r_mod = 1.2
plt.subplots_adjust(left=l_mod, right=r_mod)
elif n_vars == 3:
offset = [1.20, -0.20, 1.40, -0.40]
plt.subplots_adjust(left=0.0, right=0.7)
count = 0
for i in range(2, n_vars):
y_axis[i].spines[spine_directions[count+1]].set_position(("axes", offset[count]))
self.make_patch_spines_invisible(y_axis[i])
self.set_spine_direction(y_axis[i], spine_directions[count+1])
count += 1
# Plot the data
for ind, key in enumerate(ydata):
y_axis[ind].plot(xdata[key], ydata[key], colors[ind], **kwargs)
if len(qaqc[key]) > 0:
bad_data = np.where(qaqc[key] > 0)
y_axis[ind].plot(xdata[key][bad_data], ydata[key][bad_data],
marker='o',
mfc='none',
linestyle='None',
markersize=6,
markeredgewidth=2,
mec='r')
# Label the y-axis and set text color:
# Been experimenting with other ways to handle tick labels with spines
y_axis[ind].yaxis.get_major_formatter().set_useOffset(False)
y_axis[ind].set_ylabel(key.replace("_", " ") + ' (' + units[ind] + ')', labelpad=10, **axis_font)
y_axis[ind].yaxis.label.set_color(colors[ind])
y_axis[ind].spines[spine_directions[ind]].set_color(colors[ind])
if tick_font:
labelsize = tick_font['labelsize']
y_axis[ind].tick_params(axis='y', labelsize=labelsize, colors=colors[ind])
self.get_time_label(ax, xdata['time'])
fig.autofmt_xdate()
# ax.tick_params(axis='x', labelsize=10)
ax.set_title(title.replace("_", " "), y=1.05, **title_font)
ax.grid(True)
plt.tight_layout()
def plot_multiple_xaxes(self, ax, xdata, ydata, colors, ylabel='Depth (m)', title='', title_font={},
axis_font={}, tick_font={}, width_in=8.3, **kwargs):
# Acknowledgment: This function is based on code written by Jae-Joon Lee,
# URL= http://matplotlib.svn.sourceforge.net/viewvc/matplotlib/trunk/matplotlib/
# examples/pylab_examples/multiple_yaxis_with_spines.py?revision=7908&view=markup
if not title_font:
title_font = title_font_default
if not axis_font:
axis_font = axis_font_default
n_vars = len(xdata)
if n_vars > 6:
raise Exception('This code currently handles a maximum of 6 independent variables.')
elif n_vars < 2:
raise Exception('This code currently handles a minimum of 2 independent variables.')
# Generate the plot.
# Use twiny() to create extra axes for all dependent variables except the first
# (we get the first as part of the ax axes).
x_axis = n_vars * [0]
x_axis[0] = ax
for i in range(1, n_vars):
x_axis[i] = ax.twiny()
ax.spines["top"].set_visible(False)
self.make_patch_spines_invisible(x_axis[1])
self.set_spine_direction(x_axis[1], "top")
offset = [1.10, -0.1, -0.20, 1.20]
spine_directions = ["top", "bottom", "bottom", "top", "top", "bottom"]
count = 0
for i in range(2, n_vars):
x_axis[i].spines[spine_directions[count]].set_position(("axes", offset[count]))
self.make_patch_spines_invisible(x_axis[i])
self.set_spine_direction(x_axis[i], spine_directions[count])
count += 1
# Adjust the axes left/right accordingly
if n_vars >= 4:
plt.subplots_adjust(bottom=0.2, top=0.8)
elif n_vars == 3:
plt.subplots_adjust(bottom=0.0, top=0.8)
# Label the y-axis:
ax.set_ylabel(ylabel, **axis_font)
for ind, key in enumerate(xdata):
x_axis[ind].plot(xdata[key], ydata, colors[ind], **kwargs)
# Label the x-axis and set text color:
x_axis[ind].set_xlabel(key.replace("_", " "), **axis_font)
x_axis[ind].xaxis.label.set_color(colors[ind])
x_axis[ind].spines[spine_directions[ind]].set_color(colors[ind])
for obj in x_axis[ind].xaxis.get_ticklines():
# `obj` is a matplotlib.lines.Line2D instance
obj.set_color(colors[ind])
obj.set_markeredgewidth(2)
for obj in x_axis[ind].xaxis.get_ticklabels():
obj.set_color(colors[ind])
ax.invert_yaxis()
ax.grid(True)
if tick_font:
ax.tick_params(**tick_font)
ax.set_title(title.replace("_", " "), y=1.23, **title_font)
plt.tight_layout()
def plot_multiple_xaxes(self,
ax,
xdata,
ydata,
colors,
ylabel='Depth (m)',
title='',
title_font={},
axis_font={},
tick_font={},
width_in=8.3,
**kwargs):
# Acknowledgment: This function is based on code written by Jae-Joon Lee,
# URL= http://matplotlib.svn.sourceforge.net/viewvc/matplotlib/trunk/matplotlib/
# examples/pylab_examples/multiple_yaxis_with_spines.py?revision=7908&view=markup
if not title_font:
title_font = title_font_default
if not axis_font:
axis_font = axis_font_default
n_vars = len(xdata)
if n_vars > 6:
raise Exception(
'This code currently handles a maximum of 6 independent variables.'
)
elif n_vars < 2:
raise Exception(
'This code currently handles a minimum of 2 independent variables.'
)
# Generate the plot.
# Use twiny() to create extra axes for all dependent variables except the first
# (we get the first as part of the ax axes).
x_axis = n_vars * [0]
x_axis[0] = ax
for i in range(1, n_vars):
x_axis[i] = ax.twiny()
ax.spines["top"].set_visible(False)
self.make_patch_spines_invisible(x_axis[1])
self.set_spine_direction(x_axis[1], "top")
offset = [1.10, -0.1, -0.20, 1.20]
spine_directions = ["top", "bottom", "bottom", "top", "top", "bottom"]
count = 0
for i in range(2, n_vars):
x_axis[i].spines[spine_directions[count]].set_position(
("axes", offset[count]))
self.make_patch_spines_invisible(x_axis[i])
self.set_spine_direction(x_axis[i], spine_directions[count])
count += 1
# Adjust the axes left/right accordingly
if n_vars >= 4:
plt.subplots_adjust(bottom=0.2, top=0.8)
elif n_vars == 3:
plt.subplots_adjust(bottom=0.0, top=0.8)
# Label the y-axis:
ax.set_ylabel(ylabel, **axis_font)
for ind, key in enumerate(xdata):
x_axis[ind].plot(xdata[key], ydata, colors[ind], **kwargs)
# Label the x-axis and set text color:
x_axis[ind].set_xlabel(key.replace("_", " "), **axis_font)
x_axis[ind].xaxis.label.set_color(colors[ind])
x_axis[ind].spines[spine_directions[ind]].set_color(colors[ind])
for obj in x_axis[ind].xaxis.get_ticklines():
# `obj` is a matplotlib.lines.Line2D instance
obj.set_color(colors[ind])
obj.set_markeredgewidth(2)
for obj in x_axis[ind].xaxis.get_ticklabels():
obj.set_color(colors[ind])
ax.invert_yaxis()
ax.grid(True)
if tick_font:
ax.tick_params(**tick_font)
ax.set_title(title.replace("_", " "), y=1.23, **title_font)
plt.tight_layout()
def plot_multiple_yaxes(self,
fig,
ax,
xdata,
ydata,
colors,
title,
units=[],
scatter=False,
axis_font={},
title_font={},
tick_font={},
width_in=8.3,
qaqc={},
**kwargs):
# Plot a timeseries with multiple y-axes
#
# ydata is a python dictionary of all the data to plot. Key values are used as plot labels
#
# Acknowledgment: This function is based on code written by Jae-Joon Lee,
# URL= http://matplotlib.svn.sourceforge.net/viewvc/matplotlib/trunk/matplotlib/
# examples/pylab_examples/multiple_yaxis_with_spines.py?revision=7908&view=markup
#
# http://matplotlib.org/examples/axes_grid/demo_parasite_axes2.html
if not axis_font:
axis_font = axis_font_default
if not title_font:
title_font = title_font_default
n_vars = len(ydata)
if n_vars > 6:
raise Exception(
'This code currently handles a maximum of 6 independent variables.'
)
elif n_vars < 2:
raise Exception(
'This code currently handles a minimum of 2 independent variables.'
)
if scatter:
kwargs['marker'] = 'o'
# Generate the plot.
# Use twinx() to create extra axes for all dependent variables except the first
# (we get the first as part of the ax axes).
y_axis = n_vars * [0]
y_axis[0] = ax
for i in range(1, n_vars):
y_axis[i] = ax.twinx()
ax.spines["top"].set_visible(False)
self.make_patch_spines_invisible(y_axis[1])
self.set_spine_direction(y_axis[1], "top")
# Define the axes position offsets for each 'extra' axis
spine_directions = ["left", "right", "left", "right", "left", "right"]
# Adjust the axes left/right accordingly
if n_vars >= 4:
if width_in < 8.3:
# set axis location
offset = [1.2, -0.2, 1.40, -0.40]
# overwrite width
l_mod = 0.3
r_mod = 0.8
else:
offset = [1.10, -0.10, 1.20, -0.20]
l_mod = 0.5
r_mod = 1.2
plt.subplots_adjust(left=l_mod, right=r_mod)
elif n_vars == 3:
offset = [1.20, -0.20, 1.40, -0.40]
plt.subplots_adjust(left=0.0, right=0.7)
count = 0
for i in range(2, n_vars):
y_axis[i].spines[spine_directions[count + 1]].set_position(
("axes", offset[count]))
self.make_patch_spines_invisible(y_axis[i])
self.set_spine_direction(y_axis[i], spine_directions[count + 1])
count += 1
# Plot the data
for ind, key in enumerate(ydata):
y_axis[ind].plot(xdata[key], ydata[key], colors[ind], **kwargs)
if len(qaqc[key]) > 0:
bad_data = np.where(qaqc[key] > 0)
y_axis[ind].plot(xdata[key][bad_data],
ydata[key][bad_data],
marker='o',
mfc='none',
linestyle='None',
markersize=6,
markeredgewidth=2,
mec='r')
# Label the y-axis and set text color:
# Been experimenting with other ways to handle tick labels with spines
y_axis[ind].yaxis.get_major_formatter().set_useOffset(False)
y_axis[ind].set_ylabel(key.replace("_", " ") + ' (' + units[ind] +
')',
labelpad=10,
**axis_font)
y_axis[ind].yaxis.label.set_color(colors[ind])
y_axis[ind].spines[spine_directions[ind]].set_color(colors[ind])
if tick_font:
labelsize = tick_font['labelsize']
y_axis[ind].tick_params(axis='y',
labelsize=labelsize,
colors=colors[ind])
self.get_time_label(ax, xdata['time'])
fig.autofmt_xdate()
# ax.tick_params(axis='x', labelsize=10)
ax.set_title(title.replace("_", " "), y=1.05, **title_font)
ax.grid(True)
plt.tight_layout()
def plot_multiple_streams(self,
fig,
ax,
datasets,
colors,
axis_font={},
title_font={},
tick_font={},
width_in=8.3,
plot_qaqc=0,
scatter=False,
**kwargs):
# Plot a timeseries with multiple y-axes using multiple streams from uFrame
#
# Acknowledgment: This function is based on code written by Jae-Joon Lee,
# URL= http://matplotlib.svn.sourceforge.net/viewvc/matplotlib/trunk/matplotlib/
# examples/pylab_examples/multiple_yaxis_with_spines.py?revision=7908&view=markup
#
# http://matplotlib.org/examples/axes_grid/demo_parasite_axes2.html
if not axis_font:
axis_font = axis_font_default
if not title_font:
title_font = title_font_default
n_vars = len(datasets)
if n_vars > 6:
raise Exception(
'This code currently handles a maximum of 6 independent variables.'
)
elif n_vars < 2:
raise Exception(
'This code currently handles a minimum of 2 independent variables.'
)
if scatter:
kwargs['marker'] = 'o'
# Generate the plot.
# Use twinx() to create extra axes for all dependent variables except the first
# (we get the first as part of the ax axes).
y_axis = n_vars * [0]
y_axis[0] = ax
for i in range(1, n_vars):
y_axis[i] = ax.twinx()
ax.spines["top"].set_visible(False)
self.make_patch_spines_invisible(y_axis[1])
self.set_spine_direction(y_axis[1], "top")
# Define the axes position offsets for each 'extra' axis
spine_directions = ["left", "right", "left", "right", "left", "right"]
# Adjust the axes left/right accordingly
if n_vars >= 4:
if width_in < 8.3:
# set axis location
offset = [1.2, -0.2, 1.40, -0.40]
# overwrite width
l_mod = 0.3
r_mod = 0.8
else:
offset = [1.10, -0.10, 1.20, -0.20]
l_mod = 0.5
r_mod = 1.2
plt.subplots_adjust(left=l_mod, right=r_mod)
elif n_vars == 3:
offset = [1.20, -0.20, 1.40, -0.40]
plt.subplots_adjust(left=0.0, right=0.7)
count = 0
for i in range(2, n_vars):
y_axis[i].spines[spine_directions[count + 1]].set_position(
("axes", offset[count]))
self.make_patch_spines_invisible(y_axis[i])
self.set_spine_direction(y_axis[i], spine_directions[count + 1])
count += 1
# Plot the data
legend_handles = []
legend_labels = []
for ind, data in enumerate(datasets):
xlabel = data['x_field'][0]
ylabel = data['y_field'][0]
xdata = data['x'][xlabel]
ydata = data['y'][ylabel]
# Handle the QAQC data
qaqc = data['qaqc'][ylabel]
if plot_qaqc >= 10:
# Plot all of the qaqc flags results
# qaqc_data = data['qaqc'][ylabel]
pass
elif plot_qaqc >= 1:
# This is a case where the user wants to plot just one of the 9 QAQC tests
ind = np.where(qaqc != plot_qaqc)
qaqc[ind] = 0
else:
qaqc = []
h, = y_axis[ind].plot(xdata,
ydata,
colors[ind],
label=data['title'],
**kwargs)
if len(qaqc) > 0:
bad_data = np.where(qaqc > 0)
y_axis[ind].plot(xdata[bad_data],
ydata[bad_data],
marker='o',
mfc='none',
linestyle='None',
markersize=6,
markeredgewidth=2,
mec='r')
# Label the y-axis and set text color:
# Been experimenting with other ways to handle tick labels with spines
y_axis[ind].yaxis.get_major_formatter().set_useOffset(False)
y_axis[ind].set_ylabel(ylabel.replace("_", " "),
labelpad=10,
**axis_font)
y_axis[ind].yaxis.label.set_color(colors[ind])
y_axis[ind].spines[spine_directions[ind]].set_color(colors[ind])
if tick_font:
labelsize = tick_font['labelsize']
y_axis[ind].tick_params(axis='y',
labelsize=labelsize,
colors=colors[ind])
legend_handles.append(h)
legend_labels.append(data['title'][0:20])
self.get_time_label(ax, xdata)
fig.autofmt_xdate()
ax.legend(legend_handles, legend_labels)
# ax.tick_params(axis='x', labelsize=10)
# ax.set_title(title.replace("_", " "), y=1.05, **title_font)
ax.grid(True)
plt.tight_layout()
def plot_time_series(self,
fig,
is_timeseries,
ax,
x,
y,
fill=False,
title='',
xlabel='',
ylabel='',
title_font={},
axis_font={},
tick_font={},
scatter=False,
qaqc=[],
events={},
**kwargs):
if not title_font:
title_font = title_font_default
if not axis_font:
axis_font = axis_font_default
if scatter:
ppl.scatter(ax, x, y, **kwargs)
else:
h = ppl.plot(ax, x, y, **kwargs)
if is_timeseries:
self.get_time_label(ax, x)
fig.autofmt_xdate()
else:
ax.set_xlabel(xlabel.replace("_", " "), **axis_font)
if ylabel:
ax.set_ylabel(ylabel.replace("_", " "), **axis_font)
if title:
ax.set_title(title.replace("_", " "), **title_font)
ax.grid(True)
if fill:
miny = min(ax.get_ylim())
if not scatter:
ax.fill_between(x,
y,
miny + 1e-7,
facecolor=h[0].get_color(),
alpha=0.15)
else:
ax.fill_between(x,
y,
miny + 1e-7,
facecolor=axis_font_default['color'],
alpha=0.15)
if events:
ylim = ax.get_ylim()
for event in events['events']:
time = datestr2num(event['start_date'])
x = np.array([time, time])
h = ax.plot(x, ylim, '--', label=event['class'])
legend = ax.legend()
if legend:
for label in legend.get_texts():
label.set_fontsize(10)
if len(qaqc) > 0:
bad_data = np.where(qaqc > 0)
h = ppl.plot(ax,
x[bad_data],
y[bad_data],
marker='o',
mfc='none',
linestyle='None',
markersize=6,
markeredgewidth=2,
mec='r')
# plt.tick_params(axis='both', which='major', labelsize=10)
if tick_font:
ax.tick_params(**tick_font)
plt.tight_layout()
