def standard_axis(fig, subplot, sharex=None, sharey=None,
hasx=False, hasy=True):
sys.stderr.write("This method is deprecated. "
"Use stfio_plot.StandardAxis instead.\n")
try:
iter(subplot)
if isinstance(subplot, matplotlib.gridspec.GridSpec):
ax1 = Subplot(
fig, subplot, frameon=False, sharex=sharex, sharey=sharey)
else:
ax1 = Subplot(
fig, subplot[0], subplot[1], subplot[2], frameon=False,
sharex=sharex, sharey=sharey)
except:
ax1 = Subplot(
fig, subplot, frameon=False, sharex=sharex, sharey=sharey)
fig.add_axes(ax1)
ax1.axis["right"].set_visible(False)
ax1.axis["top"].set_visible(False)
if not hasx:
ax1.axis["bottom"].set_visible(False)
if not hasy:
ax1.axis["left"].set_visible(False)
return ax1
def Recip_space(sample):
"""
Set up general reciprocal space grid for plotting Miller indicies in a general space.
Would be cool if returned fig object had a custom transformation so that all data added
to plot after it has been created can be given in miller indicies
grid for custom transform.
"""
def tr(x, y):
x, y = np.asarray(x), np.asarray(y)
return x, y - x
def inv_tr(x, y):
x, y = np.asarray(x), np.asarray(y)
return x, y + x
grid_helper = GridHelperCurveLinear((tr, inv_tr))
fig = plt.figure(1, figsize=(7, 4))
ax = Subplot(fig, 1, 1, 1, grid_helper=grid_helper)
rlatt = sample.star_lattice
[xs, ys, zs] = sample.StandardSystem
fig.add_subplot(ax)
ax.grid(True)
return
def curvelinear_test1(fig):
"""
grid for custom transform.
"""
def tr(x, y):
sgn = np.sign(x)
x, y = np.abs(np.asarray(x)), np.asarray(y)
return sgn*x**.5, y
def inv_tr(x,y):
sgn = np.sign(x)
x, y = np.asarray(x), np.asarray(y)
return sgn*x**2, y
extreme_finder = angle_helper.ExtremeFinderCycle(20, 20,
lon_cycle = None,
lat_cycle = None,
lon_minmax = None, #(0, np.inf),
lat_minmax = None,
)
grid_helper = GridHelperCurveLinear((tr, inv_tr),
extreme_finder=extreme_finder)
ax1 = Subplot(fig, 111, grid_helper=grid_helper)
fig.add_subplot(ax1)
ax1.imshow(np.arange(25).reshape(5,5),
vmax = 50, cmap=plt.cm.gray_r,
interpolation="nearest",
origin="lower")
grid_helper.grid_finder.grid_locator1._nbins = 6
grid_helper.grid_finder.grid_locator2._nbins = 6
def plot_bars(bar_list, tick_labels, tick_positions=None, x_label='', y_label='', title='', legend_names=[], loc=2):
# plot values
fig = plt.figure()
fig.suptitle(title)
ax = Subplot(fig, 111)
ax = fig.add_subplot(ax)
plt.ylabel(y_label)
plt.xlabel(x_label)
# plot each dataset
width = .9/len(bar_list) # the width of the bars
ind = np.array(range(len(bar_list[0])))
color_list = ['#a6cee3', '#1f78b4', '#b2df8a', '#33a02c', '#fb9a99', '#e31a1c', '#fdbf6f', '#ff7f00', '#cab2d6', '#6a3d9a', '#ffff99', '#b15928']
recs = []
for i in range(len(bar_list)):
recs.append(ax.bar(ind + (width * i), bar_list[i], width, color=color_list[i], linewidth=0))
# calculate where the labels should go
if tick_positions is not None:
ax.set_xticks(np.array(tick_positions) + .5)
else:
ax.set_xticks(ind + .5)
xtickNames = ax.set_xticklabels(tick_labels)
# adjust how the axis shows
ax.axis["bottom"].major_ticklabels.set_pad(30)
ax.axis["bottom"].label.set_pad(20)
ax.axis["bottom"].major_ticklabels.set_rotation(30)
ax.axis["bottom"].major_ticks.set_visible(False)
ax.axis["top"].set_visible(False)
# add a legend
if len(legend_names) == len(recs):
legend_names = tuple([ln.title() for ln in legend_names])
legend_colors = tuple(r[0] for r in recs)
ax.legend( legend_colors, legend_names, frameon=False, loc=loc )
plt.gcf().subplots_adjust(bottom=0.19) # adjust the bottom of the plot
plt.show()
def scatter_plots(gmns=[],gms=[],matt=[],average=False, avmatt=False):
'''
will plot to scatters of the RMSDs of GMnoscaled vs GMscaled and GMnoscaled vs MATT
'''
if gms:
r,pval=pearsonr(gmns,gms)
line=r'''
$r$=%.2f, pval=%.4f
$R^2$=%.2f'''%(r,pval,r**2)
fig = plt.figure()
ax = Subplot(fig, 1,1,1)
fig.add_subplot(ax)
ax.axis["right"].set_visible(False)
ax.axis["top"].set_visible(False)
ax.axis([min(gmns), max(gmns)+0.5,min(gms),max(gms)+0.5])
plt.plot(range(int(max(gmns))+3),'r')
plt.scatter(gmns,gms)
ax.annotate(line,xy=(min(gmns)+0.5,max(gms)), xytext=(min(gmns)+0.5,max(gms)))
if avmatt:
plt.xlabel("Average Procrustes RMSD without scaling")
plt.ylabel("Average Procrustes RMSD with scaling")
plt.savefig('AvGMnsvsAvGMs.png')
else:
plt.xlabel("Procrustes RMSD without scaling")
plt.ylabel("Procrustes RMSD with scaling")
plt.savefig('GMnsvsGMs.png')#, transparent=True)
r,pval=pearsonr(gmns,gms)
line=r'''
$r$=%.2f, pval=%.4f
$R^2$=%.2f'''%(r,pval,r**2)
fig2 = plt.figure()
ax2 = Subplot(fig2, 1,1,1)
fig2.add_subplot(ax2)
ax2.axis["right"].set_visible(False)
ax2.axis["top"].set_visible(False)
ax2.axis([min(matt),max(matt)+0.5, min(gmns), max(gmns)+0.5])
plt.plot(range(int(max(gmns))+3),'r')
plt.scatter(matt,gmns)
ax2.annotate(line,xy=(min(gmns)+0.5,max(gms)), xytext=(min(gmns)+0.5,max(gms)))
if average:
plt.xlabel("Number of Core residues")
plt.ylabel("Difference of average RMSD (mean(MATT) - mean(procrustes))")
plt.savefig('diffvscore.png')
elif avmatt:
plt.xlabel("Average MATT RMSD")
plt.ylabel("Average Procrustes RMSD without scaling")
plt.savefig('AvMATTvsAvgmns.png')
else:
plt.xlabel("MATT alignment RMSD")
plt.ylabel("Procrustes RMSD without scaling")
plt.savefig('MattvsGMns.png')#, transparent=True)
def write_legend(data,
figsize,
mind,
maxd,
redblue,
num_yrs,
ylabel,
xlabel,
facecolor='0.8',
linewidth=1.):
"""Write tiny legend to be plotted on map
"""
from matplotlib import pyplot as plt
figleg = plt.figure(figsize=figsize)
axleg = Subplot(figleg, 111)
figleg.add_subplot(axleg)
axleg.axis['top'].set_visible(False)
axleg.axis['bottom'].set_visible(False)
axleg.axis['right'].set_visible(False)
yloc = plt.MaxNLocator(5)
axleg.yaxis.set_major_locator(yloc)
plt.ylim((mind, maxd))
plt.fill_between(range(num_yrs),
0.,
data,
where=data >= 0.,
facecolor=redblue[0],
lw=0,
alpha=0.95)
plt.fill_between(range(num_yrs),
0.,
data,
where=data <= 0.,
facecolor=redblue[1],
lw=0,
alpha=0.95)
font = {'size': '6'}
mpl.rc('font', **font)
plt.ylabel(ylabel, fontsize=6)
axleg.text(-30., mind * 1.5, xlabel, fontsize=6, verticalalignment='top')
plt.savefig('tinyfig' + '12' + '.png',
format="png",
dpi=300,
bbox_inches='tight',
transparent=True)
plt.close()
mpl.rcdefaults()
return
def draw_Vollmer(self):
self.plotFigure.clf()
fontsize = self.settings.general_settings['fontsize']
self.plotaxes = axes = Subplot(self.plotFigure, 111, clip_on='True',\
xlim=(-0.1,1.05), ylim=(-0.1,1.05), autoscale_on='True',\
label='vollmer', aspect='equal', adjustable='box', anchor='SW')
self.plotFigure.add_subplot(axes)
self.plotaxes.format_coord = self.read_vollmer
axes.axis['right'].set_visible(False)
axes.axis['top'].set_visible(False)
axes.axis['bottom'].set_visible(False)
axes.axis['left'].set_visible(False)
tr1 = Line2D((0, 1), (0, 0), c='black')
axes.add_line(tr1)
tr2 = Line2D((0, 0.5), (0, sqrt3_2), c='black')
axes.add_line(tr2)
tr3 = Line2D((1, 0.5), (0, sqrt3_2), c='black')
axes.add_line(tr3)
for i in [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]:
diag = Line2D((i / 2, 1.0 - i / 2), (sqrt3_2 * i, sqrt3_2 * i),
c='grey',
lw=0.5)
axes.add_line(diag)
diag2 = Line2D((i / 2, i), (sqrt3_2 * i, 0), c='grey', lw=0.5)
axes.add_line(diag2)
diag3 = Line2D((i, i + (1 - i) / 2), (0, sqrt3_2 - sqrt3_2 * i),
c='grey',
lw=0.5)
axes.add_line(diag3)
axes.text(-0.08,-0.05,'Point',family='sans-serif',\
size=fontsize,horizontalalignment='left' )
axes.text(0.97,-0.05,'Girdle',family='sans-serif',\
size=fontsize,horizontalalignment='left' )
axes.text(0.5,0.88,'Random',family='sans-serif',\
size=fontsize,horizontalalignment='center' )
for i in [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]:
axes.text((1-i)/2, sqrt3_2*(1-i)-0.01, '%d' % (i*100), \
family='sans-serif', size=fontsize, \
horizontalalignment='right', color='grey', rotation='60')
axes.text(i, -0.02,'%d' % (i*100), family='sans-serif', \
size=fontsize, horizontalalignment='center', \
verticalalignment='top', color='grey')
axes.text(1.0-i/2, sqrt3_2*i-0.01,'%d' % (i*100) , \
family='sans-serif', size=fontsize, \
horizontalalignment='left', color='grey', rotation='-60')
for plot_item in self.plot_list:
x = plot_item.G + (plot_item.R / 2.)
y = plot_item.R * sqrt3_2
element, = axes.plot(x, y, linestyle='',\
**plot_item.point_settings)
if plot_item.legend:
self.legend_items.append((element, plot_item.legend_text))
axes.set_xlim(-0.1, 1.05)
axes.set_ylim(-0.1, 1.05)
def draw_one_attribute_histogram(shp_file,field_name,attribute, output,color='grey',hatch=""):
"""
draw the figure of one attribute's histograms
Args:
shp_file: shape file path
attribute_name: name of attribute
output: output the figure
Returns: True if successful, False otherwise
"""
values = read_attribute(shp_file,field_name)
if field_name == 'INarea': # m^2 to km^2
values = [item/1000000.0 for item in values]
fig_obj = plt.figure() # create a new figure
ax = Subplot(fig_obj, 111)
fig_obj.add_subplot(ax)
# n, bins, patches = plt.hist(values, bins="auto", alpha=0.75,ec="black") # ec means edge color
n, bins, patches = ax.hist(values, bins="auto", alpha=0.75, ec="black",linewidth='1.5',color=color,hatch=hatch)
# print(n,bins,patches)
# n_label = [str(i) for i in n]
# plt.hist(values, bins="auto", alpha=0.75, ec="black",label=n_label)
# plt.gcf().subplots_adjust(bottom=0.15) # reserve space for label
# plt.xlabel(attribute,fontsize=15)
# # plt.ylabel("Frequency")
# plt.ylabel("Number",fontsize=15) #
# plt.title('Histogram of '+attribute)
# plt.text(60, .025, r'$\mu=100,\ \sigma=15$')
# plt.axis([40, 160, 0, 0.03])
# hide the right and top boxed axis
ax.axis["right"].set_visible(False)
ax.axis["top"].set_visible(False)
# plt.grid(True)
plt.savefig(output)
basic.outputlogMessage("Output figures to %s"%os.path.abspath(output))
basic.outputlogMessage("ncount: " + str(n))
basic.outputlogMessage("bins: "+ str(bins))
def curvelinear_test1(fig):
"""
grid for custom transform.
"""
def tr(x, y):
sgn = np.sign(x)
x, y = np.abs(np.asarray(x)), np.asarray(y)
return sgn * x**.5, y
def inv_tr(x, y):
sgn = np.sign(x)
x, y = np.asarray(x), np.asarray(y)
return sgn * x**2, y
extreme_finder = angle_helper.ExtremeFinderCycle(
20,
20,
lon_cycle=None,
lat_cycle=None,
# (0, np.inf),
lon_minmax=None,
lat_minmax=None,
)
grid_helper = GridHelperCurveLinear((tr, inv_tr),
extreme_finder=extreme_finder)
ax1 = Subplot(fig, 111, grid_helper=grid_helper)
# ax1 will have a ticks and gridlines defined by the given
# transform (+ transData of the Axes). Note that the transform of
# the Axes itself (i.e., transData) is not affected by the given
# transform.
fig.add_subplot(ax1)
ax1.imshow(np.arange(25).reshape(5, 5),
vmax=50,
cmap=plt.cm.gray_r,
interpolation="nearest",
origin="lower")
# tick density
grid_helper.grid_finder.grid_locator1._nbins = 6
grid_helper.grid_finder.grid_locator2._nbins = 6
def curvelinear_test1(fig):
"""
grid for custom transform.
"""
def tr(x, y):
sgn = np.sign(x)
x, y = np.abs(np.asarray(x)), np.asarray(y)
return sgn*x**.5, y
def inv_tr(x, y):
sgn = np.sign(x)
x, y = np.asarray(x), np.asarray(y)
return sgn*x**2, y
extreme_finder = angle_helper.ExtremeFinderCycle(20, 20,
lon_cycle=None,
lat_cycle=None,
# (0, np.inf),
lon_minmax=None,
lat_minmax=None,
)
grid_helper = GridHelperCurveLinear((tr, inv_tr),
extreme_finder=extreme_finder)
ax1 = Subplot(fig, 111, grid_helper=grid_helper)
# ax1 will have a ticks and gridlines defined by the given
# transform (+ transData of the Axes). Note that the transform of
# the Axes itself (i.e., transData) is not affected by the given
# transform.
fig.add_subplot(ax1)
ax1.imshow(np.arange(25).reshape(5, 5),
vmax=50, cmap=plt.cm.gray_r,
interpolation="nearest",
origin="lower")
# tick density
grid_helper.grid_finder.grid_locator1._nbins = 6
grid_helper.grid_finder.grid_locator2._nbins = 6
def draw_two_attribute_scatter(shp_file,field_name_1,field_name_2, output,color='grey',hatch=""):
"""
draw a scatter of two attributes
Args:
shp_file: shape file path
field_name_1: x
field_name_2: y
output: save file path
color:
hatch:
Returns:True if successful, False otherwise
"""
x_values = read_attribute(shp_file,field_name_1)
y_values = read_attribute(shp_file,field_name_2)
fig_obj = plt.figure() # create a new figure
ax = Subplot(fig_obj, 111)
fig_obj.add_subplot(ax)
# n, bins, patches = plt.hist(values, bins="auto", alpha=0.75,ec="black") # ec means edge color
# n, bins, patches = ax.hist(values, bins="auto", alpha=0.75, ec="black",linewidth='3',color=color,hatch=hatch)
plt.scatter(x_values, y_values,marker='^',color=color)
# print(n,bins,patches)
# n_label = [str(i) for i in n]
# plt.hist(values, bins="auto", alpha=0.75, ec="black",label=n_label)
# plt.gcf().subplots_adjust(bottom=0.15) # reserve space for label
# plt.xlabel(attribute,fontsize=15)
# # plt.ylabel("Frequency")
# plt.ylabel("Number",fontsize=15) #
# # plt.title('Histogram of '+attribute)
# # plt.text(60, .025, r'$\mu=100,\ \sigma=15$')
# # plt.axis([40, 160, 0, 0.03])
# marked area values
area_range = [1500]
for area in area_range:
ax.axvline(x=area,color='k',linewidth=0.8,linestyle='--')
ax.text(area+100, 0.55, '%d $\mathrm{m^2}$'%area, rotation=90,fontsize=20)
# hide the right and top boxed axis
# ax.axis["right"].set_visible(False)
# ax.axis["top"].set_visible(False)
# plt.grid(True)
plt.savefig(output)
basic.outputlogMessage("Output figures to %s"%os.path.abspath(output))
# basic.outputlogMessage("ncount: " + str(n))
# basic.outputlogMessage("bins: "+ str(bins))
# plt.show()
pass
def add_figs(self, identifier1, identifier2, identifier3):
for id1 in identifier1:
for id2 in identifier2:
if id2 not in self._figs[id1].keys():
self._figs[id1].update({id2: {}})
self._axs[id1].update({id2: {}})
for id3 in identifier3:
if id3 not in self._figs[id1][id2].keys():
self._figs[id1][id2].update({id3: plt.figure()})
self._axs[id1][id2].update(
{id3: Subplot(self._figs[id1][id2][id3], 111)})
self._figs[id1][id2][id3].add_subplot(
self._axs[id1][id2][id3])
def plottraces(atfobj):
# plt.figure creates a matplotlib.figure.Figure instance
fig = plt.figure()
rect = fig.patch # a rectangle instance
rect.set_alpha(0) # set the background of the figure to be transparent
#use the weird Subplot imported from the toolkits to make your axes container?
#then add this axes to to figure explicitly
ax1 = Subplot(fig,111)
ax1.patch.set_alpha(0) # set the background of the plotting axes to be transparent
fig.add_subplot(ax1)
# this weird Subplot imported from the toolkits let you indivdually set the axis lines to be invisible
# now I am hiding all of the axis lines
ax1.axis["right"].set_visible(False)
ax1.axis["top"].set_visible(False)
ax1.axis["bottom"].set_visible(False)
ax1.axis["left"].set_visible(False)
ax1.plot(atfobj.data[:,1],color = 'black')
ax1.plot(atfobj.data[:,2]+0.06, color = 'blue')
return fig
def plot_test():
# style
fig = plt.figure()
ax = Subplot(fig, 111)
fig.add_subplot(ax)
ax.axis["right"].set_visible(False)
ax.axis["top"].set_visible(False)
# values
plt.plot([1, 2, 3, 4])
# context
plt.ylabel('some numbers')
plt.show()
def draw_Flinn(self):
self.plotFigure.clf()
fontsize = self.settings.general_settings['fontsize']
self.plotaxes = axes = Subplot(self.plotFigure, 111, clip_on='True',\
xlim=(-0.2,7.2), ylim=(-0.2,7.2), autoscale_on='True',\
xlabel='ln(S2/S3)', ylabel='ln(S1/S2)', label='flinn',\
aspect='equal', adjustable='box',anchor='W')
self.plotFigure.add_subplot(axes)
self.plotaxes.format_coord = self.read_flinn
axes.axis['right'].set_visible(False)
axes.axis['top'].set_visible(False)
for i in [0.2, 0.5, 1.0, 2.0, 5.0]:
if i <= 1.0:
diag = Line2D((0, 7.0), (0, (i * 7.0)), c='grey', lw=0.5)
axes.add_line(diag)
else:
diag = Line2D((0, (7.0 / i)), (0, 7.0), c='grey', lw=0.5)
axes.add_line(diag)
for j in [2, 4, 6]:
diag2 = Line2D((0, j), (j, 0), c='grey', lw=0.5)
axes.add_line(diag2)
axes.text(6.25,0.05,'K = 0',family='sans-serif',size=fontsize, \
horizontalalignment='left',color='grey')
axes.text(0.15,6.1,'K = inf.',family='sans-serif',size=fontsize, \
horizontalalignment='left',color='grey',rotation='vertical')
axes.text(6.45,6.4,'K = 1',family='sans-serif',size=fontsize, \
horizontalalignment='center',color='grey',rotation='45')
axes.text(3.2,6.4,'K = 2',family='sans-serif',size=fontsize, \
horizontalalignment='center',color='grey',rotation='63.5')
axes.text(1.2,6.4,'K = 5',family='sans-serif',size=fontsize, \
horizontalalignment='center',color='grey',rotation='78.7')
axes.text(6.4,3.1,'K = 0.5',family='sans-serif',size=fontsize, \
horizontalalignment='center',color='grey',rotation='26.6')
axes.text(6.5,1.3,'K = 0.2',family='sans-serif',size=fontsize, \
horizontalalignment='center',color='grey',rotation='11.3')
axes.text(2.6,3.35,'C = 6',family='sans-serif',size=fontsize, \
horizontalalignment='center',color='grey',rotation='-45')
axes.text(1.75,2.2,'C = 4',family='sans-serif',size=fontsize, \
horizontalalignment='center',color='grey',rotation='-45')
axes.text(3.5,3.75,'Girdle/Cluster Transition',family='sans-serif',\
size=fontsize, horizontalalignment='left',\
verticalalignment='bottom',\
color='grey',rotation='45')
axes.text(6.5,7.2,'CLUSTERS',family='sans-serif',size=fontsize, \
horizontalalignment='right',verticalalignment='bottom',\
color='grey')
axes.text(7.2,6.5,'GIRDLES',family='sans-serif',size=fontsize, \
horizontalalignment='left',verticalalignment='top',\
color='grey',rotation='-90')
for plot_item in self.plot_list:
element, = axes.plot(plot_item.kx, plot_item.ky, linestyle='',\
**plot_item.point_settings)
if plot_item.legend:
self.legend_items.append((element, plot_item.legend_text))
axes.set_xlim(0.0, 7.2)
axes.set_ylim(0.0, 7.2)
def draw_Flinn(self):
self.plotFigure.clf()
fontsize = self.settings.general_settings["fontsize"]
self.plotaxes = axes = Subplot(
self.plotFigure,
111,
clip_on="True",
xlim=(-0.2, 7.2),
ylim=(-0.2, 7.2),
autoscale_on="True",
xlabel="ln(S2/S3)",
ylabel="ln(S1/S2)",
label="flinn",
aspect="equal",
adjustable="box",
anchor="W",
)
self.plotFigure.add_subplot(axes)
self.plotaxes.format_coord = self.read_flinn
axes.axis["right"].set_visible(False)
axes.axis["top"].set_visible(False)
for i in [0.2, 0.5, 1.0, 2.0, 5.0]:
if i <= 1.0:
diag = Line2D((0, 7.0), (0, (i * 7.0)), c="grey", lw=0.5)
axes.add_line(diag)
else:
diag = Line2D((0, (7.0 / i)), (0, 7.0), c="grey", lw=0.5)
axes.add_line(diag)
for j in [2, 4, 6]:
diag2 = Line2D((0, j), (j, 0), c="grey", lw=0.5)
axes.add_line(diag2)
axes.text(
6.25,
0.05,
"K = 0",
family="sans-serif",
size=fontsize,
horizontalalignment="left",
color="grey",
)
axes.text(
0.15,
6.1,
"K = inf.",
family="sans-serif",
size=fontsize,
horizontalalignment="left",
color="grey",
rotation="vertical",
)
axes.text(
6.45,
6.4,
"K = 1",
family="sans-serif",
size=fontsize,
horizontalalignment="center",
color="grey",
rotation="45",
)
axes.text(
3.2,
6.4,
"K = 2",
family="sans-serif",
size=fontsize,
horizontalalignment="center",
color="grey",
rotation="63.5",
)
axes.text(
1.2,
6.4,
"K = 5",
family="sans-serif",
size=fontsize,
horizontalalignment="center",
color="grey",
rotation="78.7",
)
axes.text(
6.4,
3.1,
"K = 0.5",
family="sans-serif",
size=fontsize,
horizontalalignment="center",
color="grey",
rotation="26.6",
)
axes.text(
6.5,
1.3,
"K = 0.2",
family="sans-serif",
size=fontsize,
horizontalalignment="center",
color="grey",
rotation="11.3",
)
axes.text(
2.6,
3.35,
"C = 6",
family="sans-serif",
size=fontsize,
horizontalalignment="center",
color="grey",
rotation="-45",
)
axes.text(
1.75,
2.2,
"C = 4",
family="sans-serif",
size=fontsize,
horizontalalignment="center",
color="grey",
rotation="-45",
)
axes.text(
3.5,
3.75,
"Girdle/Cluster Transition",
family="sans-serif",
size=fontsize,
horizontalalignment="left",
verticalalignment="bottom",
color="grey",
rotation="45",
)
axes.text(
6.5,
7.2,
"CLUSTERS",
family="sans-serif",
size=fontsize,
horizontalalignment="right",
verticalalignment="bottom",
color="grey",
)
axes.text(
7.2,
6.5,
"GIRDLES",
family="sans-serif",
size=fontsize,
horizontalalignment="left",
verticalalignment="top",
color="grey",
rotation="-90",
)
for plot_item in self.plot_list:
element, = axes.plot(
plot_item.kx,
plot_item.ky,
linestyle="",
**plot_item.point_settings
)
if plot_item.legend:
self.legend_items.append((element, plot_item.legend_text))
axes.set_xlim(0.0, 7.2)
axes.set_ylim(0.0, 7.2)
def generate_graph(chromosome, nearest_supers, FIELD_WIDTH, FIELD_HEIGHT,
graph_title, n):
chromosome = [ int(i) for i in chromosome ]
w = (FIELD_WIDTH)/4.0
h = (FIELD_HEIGHT)/4.0
fig = pylab.figure(figsize=(w+1,h))
#fig = pylab.figure(figsize=(2.5,3))
#ax = pylab.subplot(111)
ax = Subplot(fig, 111)
fig.add_subplot(ax)
#ax.axis["right"].set_visible(False)
#ax.axis["top"].set_visible(False)
#ax.axis["bottom"].set_visible(False)
#ax.axis["left"].set_visible(False)
super_x = [ coords[i][0] for i in range(len(chromosome))
if (chromosome[i] == 1) ]
super_y = [ coords[i][1] for i in range(len(chromosome))
if chromosome[i] == 1 ]
sensor_x = [ coords[i][0] for i in range(len(chromosome))
if chromosome[i] == 0 ]
sensor_y = [ coords[i][1] for i in range(len(chromosome))
if chromosome[i] == 0 ]
target_x = 0
target_y = 0
#pylab.grid(True)
for i, node in enumerate(chromosome):
if node == 1:
ax.plot([coords[i][0], 0], [coords[i][1], 0], '--',lw=.85,
color='red')
if not node and nearest_supers[i] >= 0:
ax.plot([coords[i][0],
coords[nearest_supers[i]][0]],
[coords[i][1],
coords[nearest_supers[i]][1]],
'-', lw=.85, color='blue')
ax.plot(sensor_x, sensor_y, 'go', label=r'sensor')
ax.plot(super_x, super_y, 'bo', label=r'super')
ax.plot(target_x, target_y, 'ro', label=r'target')
#add_ranges(ax, chromosome)
#draw_clusters(ax, chromosome, nearest_supers)
pylab.xticks(pylab.arange(0, FIELD_WIDTH+1, 1), color='white')
pylab.yticks(pylab.arange(0, FIELD_HEIGHT+1, 1), color='white')
#ax.set_xticklabels([])
#ax.set_yticklabels([])
#pylab.title(graph_title)
pylab.xlim((-1, FIELD_WIDTH+1))
pylab.ylim((-1, FIELD_HEIGHT+1))
ax.set_aspect(1)
#legend(loc='right', bbox_to_anchor=(2,1))
#box = ax.get_position()
#ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
#ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
#box = ax.get_position()
#ax.set_position([box.x0, box.y0 + box.height * 0.1,
# box.width, box.height * 0.9])
#ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05),
# fancybox=True, shadow=True, ncol=5,numpoints=1)
#LEGEND
#box = ax.get_position()
#ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
#ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), numpoints=1)
filename = "/home/milleraj/Desktop/genetic_graphs/chrom%d.pdf" % n
print filename
pylab.savefig(filename)
def PlotVollmer(self, event):
#initialize the plot areas
self.dataFigure.clf()
axes = Subplot(self.dataFigure, 111, clip_on='True',xlim=(-0.1,1.05), ylim=(-0.1,1.05),autoscale_on='True',label='vollmer',aspect='equal', adjustable='box',anchor='SW')
self.dataFigure.add_subplot(axes)
axes.axis["right"].set_visible(False)
axes.axis["top"].set_visible(False)
axes.axis["bottom"].set_visible(False)
axes.axis["left"].set_visible(False)
try:
sqrt3_2 = 0.866025 #m_sqrt(3)/2
tr1 = Line2D((0,1),(0,0),c='black')
axes.add_line(tr1)
tr2 = Line2D((0,0.5),(0,sqrt3_2),c='black')
axes.add_line(tr2)
tr3 = Line2D((1,0.5),(0,sqrt3_2),c='black')
axes.add_line(tr3)
for i in [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]:
diag = Line2D((i/2,1.0-i/2),(sqrt3_2*i,sqrt3_2*i),c='grey',lw=0.5)
axes.add_line(diag)
diag2 = Line2D((i/2,i),(sqrt3_2*i,0),c='grey',lw=0.5)
axes.add_line(diag2)
diag3 = Line2D((i,i+(1-i)/2),(0,sqrt3_2-sqrt3_2*i),c='grey',lw=0.5)
axes.add_line(diag3)
axes.text(-0.08,-0.05,'Point',family='sans-serif',size=self.fontSize,horizontalalignment='left' )
axes.text(0.97,-0.05,'Girdle',family='sans-serif',size=self.fontSize,horizontalalignment='left' )
axes.text(0.5,0.88,'Random',family='sans-serif',size=self.fontSize,horizontalalignment='center' )
# label axes values
for i in [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]:
axes.text((1-i)/2, sqrt3_2*(1-i)-0.01, '%d' % (i*100), family='sans-serif', size=self.fontSize, horizontalalignment='right', color='grey', rotation='60')
axes.text(i, -0.02,'%d' % (i*100), family='sans-serif', size=self.fontSize, horizontalalignment='center', verticalalignment='top', color='grey')
axes.text(1.0-i/2, sqrt3_2*i-0.01,'%d' % (i*100) , family='sans-serif', size=self.fontSize, horizontalalignment='left', color='grey', rotation='-60')
# ternary plot (from wikipedia)
# P = (0,0)
# G = (1,0)
# R = (1/2, sqrt(3)/2)
# given (P,G,R):
# x = G + R/2
# y = (sqrt(3)/2) * R
if len(self.idxPlan) == 0 and len(self.idxLin) == 0: # in case we have only one one opened file but it is not checked
raise AttributeError
else:
for i in range(len(self.idxPlan)):
x = self.PeigenList[i]["G"] + (self.PeigenList[i]["R"] / 2)
y = self.PeigenList[i]["R"] * sqrt3_2
axes.plot(x,y, self.PProps[i]["PoleSymb"], c=self.PProps[i]["PolColor"], ms=self.PProps[i]["polespin"],label='%s n=%d' % (self.Pname[i],self.Pndata[i]))
for j in range(len(self.idxLin)):
x = self.LeigenList[j]["G"] + (self.LeigenList[j]["R"] / 2)
y = self.LeigenList[j]["R"] * sqrt3_2
axes.plot(x,y, self.LProps[j]["LineSymb"], c=self.LProps[j]["LinColor"], ms=self.LProps[j]["linespin"],label='%s n=%d' % (self.Lname[j],self.Lndata[j]))
axes.legend(bbox_to_anchor=(0.97, 0.8), loc=2, prop=FontProperties(size=self.fontSize),numpoints=1)
axes.set_xlim(-0.1,1.05)
axes.set_ylim(-0.1,1.05)
self.dataCanvas.draw()
except AttributeError:
self.dataFigure.clf()
dlg = wx.MessageDialog(None, _('No file(s) selected (checked).\n\n'), _('Oooops!'), wx.OK|wx.ICON_ERROR)
dlg.ShowModal()
dlg.Destroy()
pass
def draw_Vollmer(self):
self.plotFigure.clf()
fontsize = self.settings.general_settings["fontsize"]
self.plotaxes = axes = Subplot(
self.plotFigure,
111,
clip_on="True",
xlim=(-0.1, 1.05),
ylim=(-0.1, 1.05),
autoscale_on="True",
label="vollmer",
aspect="equal",
adjustable="box",
anchor="SW",
)
self.plotFigure.add_subplot(axes)
self.plotaxes.format_coord = self.read_vollmer
axes.axis["right"].set_visible(False)
axes.axis["top"].set_visible(False)
axes.axis["bottom"].set_visible(False)
axes.axis["left"].set_visible(False)
tr1 = Line2D((0, 1), (0, 0), c="black")
axes.add_line(tr1)
tr2 = Line2D((0, 0.5), (0, sqrt3_2), c="black")
axes.add_line(tr2)
tr3 = Line2D((1, 0.5), (0, sqrt3_2), c="black")
axes.add_line(tr3)
for i in [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]:
diag = Line2D(
(i / 2, 1.0 - i / 2),
(sqrt3_2 * i, sqrt3_2 * i),
c="grey",
lw=0.5,
)
axes.add_line(diag)
diag2 = Line2D((i / 2, i), (sqrt3_2 * i, 0), c="grey", lw=0.5)
axes.add_line(diag2)
diag3 = Line2D(
(i, i + (1 - i) / 2),
(0, sqrt3_2 - sqrt3_2 * i),
c="grey",
lw=0.5,
)
axes.add_line(diag3)
axes.text(
-0.08,
-0.05,
"Point",
family="sans-serif",
size=fontsize,
horizontalalignment="left",
)
axes.text(
0.97,
-0.05,
"Girdle",
family="sans-serif",
size=fontsize,
horizontalalignment="left",
)
axes.text(
0.5,
0.88,
"Random",
family="sans-serif",
size=fontsize,
horizontalalignment="center",
)
for i in [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]:
axes.text(
(1 - i) / 2,
sqrt3_2 * (1 - i) - 0.01,
"%d" % (i * 100),
family="sans-serif",
size=fontsize,
horizontalalignment="right",
color="grey",
rotation="60",
)
axes.text(
i,
-0.02,
"%d" % (i * 100),
family="sans-serif",
size=fontsize,
horizontalalignment="center",
verticalalignment="top",
color="grey",
)
axes.text(
1.0 - i / 2,
sqrt3_2 * i - 0.01,
"%d" % (i * 100),
family="sans-serif",
size=fontsize,
horizontalalignment="left",
color="grey",
rotation="-60",
)
for plot_item in self.plot_list:
x = plot_item.G + (plot_item.R / 2.0)
y = plot_item.R * sqrt3_2
element, = axes.plot(
x, y, linestyle="", **plot_item.point_settings
)
if plot_item.legend:
self.legend_items.append((element, plot_item.legend_text))
axes.set_xlim(-0.1, 1.05)
axes.set_ylim(-0.1, 1.05)
def generate_graph(chromosome, nearest_supers, FIELD_WIDTH, FIELD_HEIGHT,
graph_title, n):
chromosome = [int(i) for i in chromosome]
w = (FIELD_WIDTH) / 4.0
h = (FIELD_HEIGHT) / 4.0
fig = pylab.figure(figsize=(w + 1, h))
#fig = pylab.figure(figsize=(2.5,3))
#ax = pylab.subplot(111)
ax = Subplot(fig, 111)
fig.add_subplot(ax)
#ax.axis["right"].set_visible(False)
#ax.axis["top"].set_visible(False)
#ax.axis["bottom"].set_visible(False)
#ax.axis["left"].set_visible(False)
super_x = [
coords[i][0] for i in range(len(chromosome)) if (chromosome[i] == 1)
]
super_y = [
coords[i][1] for i in range(len(chromosome)) if chromosome[i] == 1
]
sensor_x = [
coords[i][0] for i in range(len(chromosome)) if chromosome[i] == 0
]
sensor_y = [
coords[i][1] for i in range(len(chromosome)) if chromosome[i] == 0
]
target_x = 0
target_y = 0
#pylab.grid(True)
for i, node in enumerate(chromosome):
if node == 1:
ax.plot([coords[i][0], 0], [coords[i][1], 0],
'--',
lw=.85,
color='red')
if not node and nearest_supers[i] >= 0:
ax.plot([coords[i][0], coords[nearest_supers[i]][0]],
[coords[i][1], coords[nearest_supers[i]][1]],
'-',
lw=.85,
color='blue')
ax.plot(sensor_x, sensor_y, 'go', label=r'sensor')
ax.plot(super_x, super_y, 'bo', label=r'super')
ax.plot(target_x, target_y, 'ro', label=r'target')
#add_ranges(ax, chromosome)
#draw_clusters(ax, chromosome, nearest_supers)
pylab.xticks(pylab.arange(0, FIELD_WIDTH + 1, 1), color='white')
pylab.yticks(pylab.arange(0, FIELD_HEIGHT + 1, 1), color='white')
#ax.set_xticklabels([])
#ax.set_yticklabels([])
#pylab.title(graph_title)
pylab.xlim((-1, FIELD_WIDTH + 1))
pylab.ylim((-1, FIELD_HEIGHT + 1))
ax.set_aspect(1)
#legend(loc='right', bbox_to_anchor=(2,1))
#box = ax.get_position()
#ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
#ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
#box = ax.get_position()
#ax.set_position([box.x0, box.y0 + box.height * 0.1,
# box.width, box.height * 0.9])
#ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05),
# fancybox=True, shadow=True, ncol=5,numpoints=1)
#LEGEND
#box = ax.get_position()
#ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
#ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), numpoints=1)
filename = "/home/milleraj/Desktop/genetic_graphs/chrom%d.pdf" % n
print filename
pylab.savefig(filename)
def PlotFlinn(self, event):
#initialize the plot areas
self.dataFigure.clf()
axes = Subplot(self.dataFigure, 111, clip_on='True',xlim=(-0.2,7.2), ylim=(-0.2,7.2),autoscale_on='True',xlabel='ln(S2/S3)',ylabel='ln(S1/S2)',label='flinn',aspect='equal', adjustable='box',anchor='W')
self.dataFigure.add_subplot(axes)
axes.axis["right"].set_visible(False)
axes.axis["top"].set_visible(False)
try:
# plot lines of K
for i in [0.2, 0.5, 1.0, 2.0, 5.0]:
if i <= 1.0:
diag = Line2D((0,7.0),(0,(i*7.0)),c='grey',lw=0.5)
axes.add_line(diag)
else:
diag = Line2D((0,(7.0/i)),(0,7.0),c='grey',lw=0.5)
axes.add_line(diag)
# plot lines of C
for j in [2,4,6]:
diag2 = Line2D((0,j),(j,0),c='grey',lw=0.5)
axes.add_line(diag2)
# texts
axes.text(6.25,0.05,'K = 0',family='sans-serif',size=self.fontSize,horizontalalignment='left',color='grey')
axes.text(0.15,6.1,'K = inf.',family='sans-serif',size=self.fontSize,horizontalalignment='left',color='grey',rotation='vertical')
axes.text(6.45,6.4,'K = 1',family='sans-serif',size=self.fontSize,horizontalalignment='center',color='grey',rotation='45')
axes.text(3.2,6.4,'K = 2',family='sans-serif',size=self.fontSize,horizontalalignment='center',color='grey',rotation='63.5')
axes.text(1.2,6.4,'K = 5',family='sans-serif',size=self.fontSize,horizontalalignment='center',color='grey',rotation='78.7')
axes.text(6.4,3.1,'K = 0.5',family='sans-serif',size=self.fontSize,horizontalalignment='center',color='grey',rotation='26.6')
axes.text(6.5,1.3,'K = 0.2',family='sans-serif',size=self.fontSize,horizontalalignment='center',color='grey',rotation='11.3')
axes.text(2.6,3.35,'C = 6',family='sans-serif',size=self.fontSize,horizontalalignment='center',color='grey',rotation='-45')
axes.text(1.75,2.2,'C = 4',family='sans-serif',size=self.fontSize,horizontalalignment='center',color='grey',rotation='-45')
axes.text(3.5,3.75,'Girdle/Cluster Transition',family='sans-serif',size=self.fontSize,horizontalalignment='left',verticalalignment='bottom',color='grey',rotation='45')
axes.text(6.5,7.2,'CLUSTERS',family='sans-serif',size=self.fontSize,horizontalalignment='right',verticalalignment='bottom',color='grey')
axes.text(7.2,6.5,'GIRDLES',family='sans-serif',size=self.fontSize,horizontalalignment='left',verticalalignment='top',color='grey',rotation='-90')
# plot the selected (checked) files
# propsPList = [pdata, itemName, PolColor, symbPoles, polespin,...
# propsLList = [pdata, itemName, LinColor, LineSymb, linespin,...
if len(self.idxPlan) == 0 and len(self.idxLin) == 0: # in case we have only one one opened file but it is not checked
raise AttributeError
else:
for i in range(len(self.idxPlan)):
axes.plot(self.PeigenList[i]["K_x"],self.PeigenList[i]["K_y"], self.PProps[i]["PoleSymb"], c=self.PProps[i]["PolColor"], ms=self.PProps[i]["polespin"], label='%s n=%d' % (self.Pname[i],self.Pndata[i]))
for j in range(len(self.idxLin)):
axes.plot(self.LeigenList[j]["K_x"],self.LeigenList[j]["K_y"], self.LProps[j]["LineSymb"], c=self.LProps[j]["LinColor"], ms=self.LProps[j]["linespin"], label='%s n=%d' % (self.Lname[j],self.Lndata[j]))
axes.legend(bbox_to_anchor=(1.1, 1), loc=2, prop=FontProperties(size='small'),numpoints=1)
#set the axes limits and draws the stuff
axes.set_xlim(0.0,7.2)
axes.set_ylim(0.0,7.2)
self.dataCanvas.draw()
except AttributeError:
self.dataFigure.clf()
dlg = wx.MessageDialog(None, 'No file(s) selected (checked).\n\n', 'Oooops!', wx.OK|wx.ICON_ERROR)
dlg.ShowModal()
dlg.Destroy()
pass
def scatter_plots(gmns=[], gms=[], matt=[], average=False, avmatt=False):
'''
will plot to scatters of the RMSDs of GMnoscaled vs GMscaled and GMnoscaled vs MATT
'''
if gms:
r, pval = pearsonr(gmns, gms)
line = r'''
$r$=%.2f, pval=%.4f
$R^2$=%.2f''' % (r, pval, r**2)
fig = plt.figure()
ax = Subplot(fig, 1, 1, 1)
fig.add_subplot(ax)
ax.axis["right"].set_visible(False)
ax.axis["top"].set_visible(False)
ax.axis([min(gmns), max(gmns) + 0.5, min(gms), max(gms) + 0.5])
plt.plot(range(int(max(gmns)) + 3), 'r')
plt.scatter(gmns, gms)
ax.annotate(line,
xy=(min(gmns) + 0.5, max(gms)),
xytext=(min(gmns) + 0.5, max(gms)))
if avmatt:
plt.xlabel("Average Procrustes RMSD without scaling")
plt.ylabel("Average Procrustes RMSD with scaling")
plt.savefig('AvGMnsvsAvGMs.png')
else:
plt.xlabel("Procrustes RMSD without scaling")
plt.ylabel("Procrustes RMSD with scaling")
plt.savefig('GMnsvsGMs.png') #, transparent=True)
r, pval = pearsonr(gmns, gms)
line = r'''
$r$=%.2f, pval=%.4f
$R^2$=%.2f''' % (r, pval, r**2)
fig2 = plt.figure()
ax2 = Subplot(fig2, 1, 1, 1)
fig2.add_subplot(ax2)
ax2.axis["right"].set_visible(False)
ax2.axis["top"].set_visible(False)
ax2.axis([min(matt), max(matt) + 0.5, min(gmns), max(gmns) + 0.5])
plt.plot(range(int(max(gmns)) + 3), 'r')
plt.scatter(matt, gmns)
ax2.annotate(line,
xy=(min(gmns) + 0.5, max(gms)),
xytext=(min(gmns) + 0.5, max(gms)))
if average:
plt.xlabel("Number of Core residues")
plt.ylabel(
"Difference of average RMSD (mean(MATT) - mean(procrustes))")
plt.savefig('diffvscore.png')
elif avmatt:
plt.xlabel("Average MATT RMSD")
plt.ylabel("Average Procrustes RMSD without scaling")
plt.savefig('AvMATTvsAvgmns.png')
else:
plt.xlabel("MATT alignment RMSD")
plt.ylabel("Procrustes RMSD without scaling")
plt.savefig('MattvsGMns.png') #, transparent=True)
## ax.axis[direction].set_visible(False)
## ax.set_xticks([])
## ax.set_yticks([])
## x = np.linspace(0, 2., 100)
## ax.plot(x, np.sin(x*np.pi))
## #fig.savefig('different_axis.pdf')
## plt.show()
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid.axislines import Subplot
fig = plt.figure()
ax = Subplot(fig, 111)
fig.add_subplot(ax)
ax.axis["right"].set_visible(False)
ax.axis["left"].set_visible(False)
ax.axis["top"].set_visible(False)
ax.axis["bottom"].set_visible(False)
x = np.linspace(0, 2., 100)
ax.plot(x, np.sin(x*np.pi))
plt.tight_layout()
fig.savefig('different_axis.pdf')
#plt.show()
def write_legend2(data,
upper,
lower,
figsize,
mind,
maxd,
redblue,
num_yrs,
ylabel,
xlabel,
facecolor='0.8',
linewidth=1.,
which_legend='subbasins'):
"""Write tiny legend to be plotted on map
"""
from matplotlib import pyplot as plt
figleg = plt.figure(figsize=figsize)
axleg = Subplot(figleg, 111)
figleg.add_subplot(axleg)
axleg.axis['top'].set_visible(False)
axleg.axis['bottom'].set_visible(False)
axleg.axis['right'].set_visible(False)
yloc = plt.MaxNLocator(5)
axleg.yaxis.set_major_locator(yloc)
plt.ylim((mind, maxd))
plt.xlim(0, num_yrs)
if redblue[0] == 'blue':
cmap = ListedColormap(['r', 'b'])
else:
cmap = ListedColormap(['b', 'r'])
norm = BoundaryNorm([-1e-3, 0, 1.e-3],
cmap.N) # cmap.N is number of items in the colormap
x, y = interp(range(num_yrs), data, 0., float(num_yrs - 1), 3 * num_yrs)
plt.fill_between(range(num_yrs),
upper,
lower,
facecolor='0.6',
lw=0,
alpha=1.)
lc = colorline(x,
y,
z=y,
cmap=cmap,
norm=norm,
linewidth=linewidth,
maxd=maxd,
mind=mind)
plt.gca().add_collection(lc)
font = {'size': '9'}
mpl.rc('font', **font)
plt.ylabel(ylabel, fontsize=6)
axleg.text(-30., mind * 1.5, xlabel, fontsize=6, verticalalignment='top')
if which_legend == 'subbasins':
plt.savefig('tinyfig' + '12' + '.png',
format="png",
dpi=300,
bbox_inches='tight',
transparent=True)
elif which_legend == 'EFs':
plt.savefig('tinyfig' + '8' + '.png',
format="png",
dpi=300,
bbox_inches='tight',
transparent=True)
plt.close()
mpl.rcdefaults()
return
dates = []
emails = []
reader = csv.reader(open('dasovich-dates.csv', 'r'))
reader.next() # skip header
for row in reader:
dates.append(datetime.strptime(row[0], '%Y-%m'))
# dates.append(row[0])
emails.append(float(row[1]))
# PLOT
fig = plt.figure()
ax = Subplot(fig, 111)
fig.add_subplot(ax)
ax.axis["right"].set_visible(False)
ax.axis["top"].set_visible(False)
plt.plot_date(dates, emails, xdate=True, ydate=False, linestyle='-', color='#333333')
ax.grid(which='major', color='#999999')
plt.xticks(rotation='vertical')
plt.title('Monthly Email History for Jeff Dasovich')
plt.ylabel('Number of Emails')
plt.savefig('foo.pdf', transparent=True)
#!/usr/bin/env python import matplotlib.pyplot as plt from mpl_toolkits.axes_grid.axislines import Subplot fig1 = plt.figure(1, (3, 3), facecolor='white') ax1 = Subplot(fig1, 111) fig1.add_subplot(ax1) ax1.axis["right"].set_visible(False) ax1.axis["top"].set_visible(False) plt.show()
def plot_df_f0s():
fig = plt.figure(1, (3, 3))
ax = Subplot(fig, 111)
fig.add_subplot(ax)
ax.axis["right"].set_visible(False)
ax.axis["top"].set_visible(False)
x = np.linspace(-333.3, 1498.5, num=55)
# x = range(1, 56)
for true_means, list_sems in zip(all_true_means, all_list_sems):
ax.errorbar(x, true_means, yerr=list_sems, elinewidth=3, linewidth=4)
ax.axvspan(0, 400, color="y", alpha=0.5, lw=0)
ax.set_xticks([-200, 0, 200, 400, 600, 800, 1000, 1200, 1400])
ax.set_xlabel("Time (ms)")
ax.set_ylabel(r"$\Delta F/F_0$(%)")
ax.xaxis.set_tick_params(width=2, length=7)
ax.yaxis.set_tick_params(width=2, length=7)
# x_axis = np.linspace(3.333, 26.6667, num=800)
# for df_df0 in list_df_f0:
# plt.plot(df_df0)
ax.set_ylim([-1.5, 2.5])
ax.set_xlim([-340, 1500])
# axes color.... jeff didn't like it :(
# plt.axhline(linewidth=3, color='k')
# plt.axvline(linewidth=3, color='k')
# plt.xlim([3.333,26.6667])
# plt.plot([83, 83], [-0.015, 0.025], 'k-', lw=2)
# plt.plot([203, 203], [-0.015, 0.025], 'k-', lw=2)
# plt.plot([505, 505], [-0.015, 0.025], 'k-', lw=2)
# plt.axvspan(5, 17, color='y', alpha=0.5, lw=0)
# plt.axvspan(505, 517, color='y', alpha=0.5, lw=0)
# for i in range(1, len(timings)):
# plt.plot([timings[i], timings[i]], [-0.015, 0.025], 'k-', lw=2)
plt.show()
import matplotlib.pyplot as plt from mpl_toolkits.axes_grid.axislines import Subplot fig = plt.figure(1, (3, 3)) ax = Subplot(fig, 111) fig.add_subplot(ax) ax.axis["right"].set_visible(False) ax.axis["top"].set_visible(False) plt.show()
def curvelinear_test1(fig):
"""
grid for custom transform.
"""
def tr(x, y):
x, y = np.asarray(x), np.asarray(y)
return x, y - x
def inv_tr(x, y):
x, y = np.asarray(x), np.asarray(y)
return x, y + x
grid_helper = GridHelperCurveLinear((tr, inv_tr))
ax1 = Subplot(fig, 1, 2, 1, grid_helper=grid_helper)
# ax1 will have a ticks and gridlines defined by the given
# transform (+ transData of the Axes). Note that the transform of
# the Axes itself (i.e., transData) is not affected by the given
# transform.
fig.add_subplot(ax1)
xx, yy = tr([3, 6], [5.0, 10.])
ax1.plot(xx, yy)
ax1.set_aspect(1.)
ax1.set_xlim(0, 10.)
ax1.set_ylim(0, 10.)
ax1.grid(True)
def curvelinear_test1(fig):
"""
grid for custom transform.
"""
def tr(x, y):
x, y = np.asarray(x), np.asarray(y)
return x, y-x
def inv_tr(x,y):
x, y = np.asarray(x), np.asarray(y)
return x, y+x
grid_helper = GridHelperCurveLinear((tr, inv_tr))
ax1 = Subplot(fig, 1, 2, 1, grid_helper=grid_helper)
# ax1 will have a ticks and gridlines defined by the given
# transform (+ transData of the Axes). Note that the transform of
# the Axes itself (i.e., transData) is not affected by the given
# transform.
fig.add_subplot(ax1)
xx, yy = tr([3, 6], [5.0, 10.])
ax1.plot(xx, yy)
ax1.set_aspect(1.)
ax1.set_xlim(0, 10.)
ax1.set_ylim(0, 10.)
ax1.axis["t"]=ax1.new_floating_axis(0, 3.)
ax1.axis["t2"]=ax1.new_floating_axis(1, 7.)
ax1.grid(True)
def curvelinear_test1(fig):
"""
grid for custom transform.
"""
def tr(x, y):
x, y = np.asarray(x), np.asarray(y)
return x, y-x
def inv_tr(x,y):
x, y = np.asarray(x), np.asarray(y)
return x, y+x
grid_helper = GridHelperCurveLinear((tr, inv_tr))
ax1 = Subplot(fig, 1, 2, 1, grid_helper=grid_helper)
fig.add_subplot(ax1)
xx, yy = tr([3, 6], [5.0, 10.])
ax1.plot(xx, yy)
ax1.set_aspect(1.)
ax1.set_xlim(0, 10.)
ax1.set_ylim(0, 10.)
ax1.grid(True)
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid.axislines import Subplot
# name the output file
pdfname = 'python_plot.pdf'
# read input file: first column (X values)
# second column (Y values)
x, y, size = np.loadtxt('bubble.dat', unpack=True)
size = size * 250.
# create plot
fig = plt.figure(1, (8,5), facecolor='white')
ax = Subplot(fig, 111)
fig.add_subplot(ax)
# make axes invisible
ax.axis["right"].set_visible(False)
ax.axis["top"].set_visible(False)
ax.scatter(x, y, c='b', s=size, alpha=1.0, lw = 0)
pl.ylabel('peak time rank number')
pl.xlabel(r'log($\nu$) [ghz]')
pl.ylim([0.0, 8.0])
pl.xlim([0.0, 2.5])
pl.savefig(pdfname)
