def multiplot_pumpper(pumpfname, perfname, keyfile, errors, savefig, figname, ylim, ylabel,
ylabelper, figw, figh, figdpi, yaxisticks, ymin, barwidth, lw, fontsz, lpx, lpy):
matplotlib.rc('axes', linewidth=lw)
matplotlib.rc('axes.formatter', limits = [-6, 6])
# Sets font properties.
fontv = mpl.font_manager.FontProperties()
# Uncomment line below to set the font to verdana; the default matplotlib font is very
#similar (just slightly narrower).
fontv = mpl.font_manager.FontProperties(fname='/usr/share/matplotlib/mpl-data/fonts/ttf/arial.ttf')
fontv.set_size(fontsz)
#Creates a figure of the indicated size and dpi.
fig1 = plt.figure(figsize=(figw, figh), dpi=figdpi, facecolor='w', edgecolor='k')
origconds = []
conds = []
means = []
stdevs = []
stderrs = []
ns = []
# With this data, all the condition names are different.
# Loads keys in order of plotting from keyfile.
keylist = cmn.load_keys(keyfile)
### PLOTS PUMP FREQUENCY DATA ###
# Loads data from pumpfname into a dictionary and generates lists from that data.
dictmeans = mp.loadmeans(pumpfname)
for condition in keylist:
mean, stdev, sterr, n = dictmeans[condition]
means.append(mean)
stdevs.append(stdev)
stderrs.append(sterr)
conds.append(condition)
origconds.append(condition)
if keyfile == 'keyfile_s':
conds = [cond.replace('/100 mM sucrose', '') for cond in conds]
if keyfile == 'keyfile_c':
conds = [cond.replace('24 hours/', '') for cond in conds]
conds = [cond.replace(' sucrose', '') for cond in conds]
# Defines coordinates and colors for each bar.
barnum = len(keylist)
lastbar = (3*barnum*barwidth)-2*barwidth # X-coordinate of last bar
x_gen1 = np.linspace(0.5*barwidth, lastbar, barnum).tolist()
x_list = x_gen1
colors = np.tile('k', barnum).tolist()
#Coordinates where the xlabels will be listed.
truebarw = barwidth-(0.05*barwidth)
xlabel_list = [x + truebarw for x in x_list]
#Plots the bar plot.
plt.bar(x_list, means, width=truebarw, color=colors, ecolor='k', linewidth=lw)
# Plots error bars.
zeros = np.tile(0, len(x_list)).tolist()
x_errbar = [x - 0.5*barwidth for x in xlabel_list]
if errors == 'stderr':
plt.errorbar(x_errbar, means, yerr=[zeros,stderrs], fmt=None, ecolor='k', lw=lw,
capsize=2)
if errors == 'stdev':
plt.errorbar(x_errbar, means, yerr=[zeros,stdevs], fmt=None, ecolor='k', lw=lw, capsize=2)
# Defines the axes.
ax1 = plt.gca()
# Formats the yticks.
plt.yticks(fontproperties=fontv)
# Formats the xticks.
plt.xticks(multialignment = 'center')
#Uncomment lines below to display without top and right borders.
for loc, spine in ax1.spines.iteritems():
if loc in ['left','bottom', 'right']:
pass
elif loc in ['top']:
spine.set_color('none') # don't draw spine
else:
raise ValueError('unknown spine location: %s'%loc)
#Uncomment lines below to display ticks only where there are borders.
ax1.xaxis.set_ticks_position('bottom')
ax1.yaxis.set_ticks_position('left')
# Uncomment the line below to remove all of the plot axis lines.
#plt.setp(ax, frame_on=False)
#Uncomment the line below to remove all tick marks/labels.
#ax1.axes.xaxis.set_major_locator(matplotlib.ticker.NullLocator())
# Specifies the number of tickmarks/labels on the yaxis.
ax1.yaxis.set_major_locator(matplotlib.ticker.MaxNLocator(yaxisticks))
#Removes the tickmarks on the x-axis but leaves the labels and the spline.
for line in ax1.get_xticklines():
line.set_visible(False)
plt.ylim(ymax=YLIM)
# Labels the yaxis; labelpad is the space between the ticklabels and y-axis label.
plt.ylabel(ylabel, labelpad=lpy, fontproperties=fontv, multialignment='center')
# Labels the xaxis.
if keyfile == 'keyfile_c':
plt.xlabel('[Sucrose]', labelpad=lpx, fontproperties=fontv, multialignment='center')
if keyfile == 'keyfile_s':
plt.xlabel('Starvation time', labelpad=lpx, fontproperties=fontv, multialignment='center')
### PLOTS PER DATA ###
ax2 = ax1.twinx()
props = []
lcis = []
ucis = []
nsuccesses = []
ntotals = []
perdictprop = mp.loadpropci(perfname)
for condition in keylist:
cind = CONDPUMP.index(condition)
condition = CONDPER[cind]
prop, lci, uci, nsuccess, ntotal = perdictprop[condition]
props.append(prop*100)
lcis.append((prop-lci)*100)
ucis.append((uci-prop)*100)
nsuccesses.append(nsuccess)
ntotals.append(ntotal)
lastbarper = (3*barnum*barwidth-barwidth) # X-coordinate of last bar
x_gen1per = np.linspace(1.5*barwidth, lastbarper, barnum).tolist()
x_listper = x_gen1per
print(x_listper)
print(x_list)
colors = np.tile(PERCOLOR, barnum).tolist()
#Plots the bar plot and error bars.
x_errbar_per = [x + 0.5*barwidth for x in x_listper]
plt.bar(x_listper, props, width=truebarw, color=colors, linewidth=lw)
plt.errorbar(x_errbar_per, props, yerr=[lcis,ucis], fmt=None, ecolor='k', lw=lw, capsize=2)
for loc, spine in ax2.spines.iteritems():
if loc in ['right']:
pass
elif loc in ['left', 'top', 'bottom']:
spine.set_color('none') # don't draw spine
else:
raise ValueError('unknown spine location: %s'%loc)
# Plots the yticks and ylabel for the PER scale.
plt.yticks(fontproperties=fontv, color=PERTEXTCOLOR)
plt.ylim(ymax=100)
plt.ylabel(ylabelper, labelpad=lpy, fontproperties=fontv, multialignment='center',
rotation=-90, color=PERTEXTCOLOR)
### ADJUSTS FIGURE PROPERTIES ####
# Plots the xticks and labels for both scales.
ax1.xaxis.set_ticks(xlabel_list)
ax1.xaxis.set_ticklabels(conds, fontproperties=fontv)
xlim = x_list[-1]+2.5*barwidth
# Sets the x limits.
plt.xlim( [0, xlim] )
print(xlim)
#Adjusts the space between the plot and the edges of the figure; (0,0) is the lower lefthand
#corner of the figure.
fig1.subplots_adjust(bottom=ADJBOTTOM)
fig1.subplots_adjust(right=ADJRIGHT)
fig1.subplots_adjust(left=ADJLEFT)
# Saves the figure with the name 'figname'.
if savefig == 'yes':
plt.savefig('pump_per_'+keyfile+'.png', dpi=300)
plt.savefig('pump_per_'+keyfile+'.svg', dpi=300)
def multiplot_visc(pumpfname, keyfile, errors, savefig, figname, ylim, ylabel, figw, figh,
figdpi, yaxisticks, ymin, barwidth, lw, fontsz, lpx, lpy):
matplotlib.rc('axes', linewidth=lw)
matplotlib.rc('axes.formatter', limits = [-6, 6])
# Sets font properties.
fontv = mpl.font_manager.FontProperties()
# Uncomment line below to set the font to verdana; the default matplotlib font is very
#similar (just slightly narrower).
fontv = mpl.font_manager.FontProperties(fname='/usr/share/matplotlib/mpl-data/fonts/ttf/arial.ttf')
fontv.set_size(fontsz)
#Creates a figure of the indicated size and dpi.
figw = 1.1*figw
fig1 = plt.figure(figsize=(figw, figh), dpi=figdpi, facecolor='w', edgecolor='k')
origconds = []
conds = []
means = []
stdevs = []
stderrs = []
ns = []
# With this data, all the condition names are different.
# Loads keys in order of plotting from keyfile.
keylist = cmn.load_keys(keyfile)
### PLOTS PUMP FREQUENCY DATA ###
# Loads data from pumpfname into a dictionary and generates lists from that data.
dictmeans = mp.loadmeans(pumpfname)
for condition in keylist:
mean, stdev, sterr, n = dictmeans[condition]
means.append(mean)
stdevs.append(stdev)
stderrs.append(sterr)
conds.append(condition)
origconds.append(condition)
if keyfile == 'keyfile_v':
conds = [cond.replace('MC', '\nMC') for cond in conds]
barnum = len(keylist)
# Defines coordinates for each bar.
lastbar = (2*barnum*barwidth)-barwidth # X-coordinate of last bar
x_gen1 = np.linspace(0.5*barwidth, lastbar, barnum).tolist()
x_list = x_gen1
colors = np.tile('k', barnum).tolist()
#Coordinates where the xlabels will be listed.
truebarw = barwidth-(0.05*barwidth)
xlabel_list = [x + 0.5*truebarw for x in x_list]
# Defines limit of x axis.
xlim = x_list[-1]+1.5*barwidth
print(xlim)
#Plots the bar plot.
plt.bar(x_list, means, width=truebarw, color=colors, ecolor='k')
# Plots error bars.
zeros = np.tile(0, len(x_list)).tolist()
x_errbar = xlabel_list
if errors == 'stderr':
plt.errorbar(x_errbar, means, yerr=[zeros,stderrs], fmt=None, ecolor='k', lw=lw,
capsize=2)
if errors == 'stdev':
plt.errorbar(x_errbar, means, yerr=[zeros,stdevs], fmt=None, ecolor='k', lw=lw,
capsize=2)
# Defines the axes.
ax1 = plt.gca()
# Writes the label for 500 mM sucrose.
line = mpl.lines.Line2D([x_list[0],x_list[2]+barwidth], [-3.65, -3.65], lw=lw, color='k')
line.set_clip_on(False)
l = ax1.add_line(line)
plt.text(xlabel_list[1], -4.05, '500 mM sucrose', fontproperties=fontv,
horizontalalignment='center', verticalalignment='top',multialignment='center',
rotation=0)
# Writes the label for sucrose.
plt.text(xlabel_list[3]+barwidth, -2.15, 'sucrose', fontproperties=fontv,
horizontalalignment='center', verticalalignment='top',multialignment='center',
rotation=0)
# Formats the yticks.
plt.yticks(fontproperties=fontv)
# Plots the xticks.
plt.xticks(xlabel_list, conds, multialignment = 'center', fontproperties=fontv)
#Uncomment lines below to display without top and right borders.
for loc, spine in ax1.spines.iteritems():
if loc in ['left','bottom']:
pass
elif loc in ['top', 'right']:
spine.set_color('none') # don't draw spine
else:
raise ValueError('unknown spine location: %s'%loc)
#Uncomment lines below to display ticks only where there are borders.
ax1.xaxis.set_ticks_position('bottom')
ax1.yaxis.set_ticks_position('left')
# Uncomment the line below to remove all of the plot axis lines.
#plt.setp(ax, frame_on=False)
#Uncomment the line below to remove all tick marks/labels.
#ax1.axes.xaxis.set_major_locator(matplotlib.ticker.NullLocator())
# Specifies the number of tickmarks/labels on the yaxis.
ax1.yaxis.set_major_locator(matplotlib.ticker.MaxNLocator(yaxisticks))
#Removes the tickmarks on the x-axis but leaves the labels and the spline.
for line in ax1.get_xticklines():
line.set_visible(False)
plt.ylim(ymax = YLIM)
plt.xlim(xmax = xlim)
# Labels the yaxis; labelpad is the space between the ticklabels and y-axis label.
plt.ylabel(ylabel, labelpad=lpy, fontproperties=fontv, multialignment='center')
#plt.text(xlabel_list[1], -2, '[Sucrose]', fontproperties=fontv,
#horizontalalignment='center', verticalalignment='top',multialignment='center',rotation=0)
#Adjusts the space between the plot and the edges of the figure; (0,0) is the lower lefthand
#corner of the figure.
fig1.subplots_adjust(bottom=ADJBOTTOM)
fig1.subplots_adjust(right=ADJRIGHT)
fig1.subplots_adjust(left=ADJLEFT)
# Saves the figure with the name 'figname'.
if savefig == 'yes':
plt.savefig('pump_per_'+keyfile+'.png', dpi=300)
plt.savefig('pump_per_'+keyfile+'.svg', dpi=300)
def multiplot_deltatime(fname1max1min, fname1min1max, keyfile, errors, savefig, figname, ylim,
ylabel, figw, figh, figdpi, yaxisticks, ymin, barwidth, barnum, othercolor, fontsz, lp,
lw):
matplotlib.rc('axes', linewidth=lw)
matplotlib.rc('axes.formatter', limits = [-6, 6])
# Sets font properties.
fontv = mpl.font_manager.FontProperties()
# Uncomment line below to set the font to verdana; the default matplotlib font is very
#similar (just slightly narrower).
fontv = mpl.font_manager.FontProperties(fname='/usr/share/matplotlib/mpl-data/fonts/ttf/arial.ttf')
fontv.set_size(fontsz)
#Creates a figure of the indicated size and dpi.
figw = 1.1*figw
fig1 = plt.figure(figsize=(figw, figh), dpi=figdpi, facecolor='w', edgecolor='k')
conds_1max1min = []
means_1max1min = []
stdevs_1max1min = []
stderrs_1max1min = []
ns_1max1min = []
conds_1min1max = []
means_1min1max = []
stdevs_1min1max = []
stderrs_1min1max = []
ns_1min1max = []
# Some of the condition names are the same; load conditions from the summary file.
keylist = cmn.load_keys(keyfile)
# Loads data from fname into a dictionary and generates lists from that data.
dictmeans_1max1min = mp.loadmeans(fname1max1min)
for condition in keylist:
mean, stdev, stderr, n = dictmeans_1max1min[condition]
conds_1max1min.append(condition)
means_1max1min.append(float(mean))
stdevs_1max1min.append(float(stdev))
stderrs_1max1min.append(float(stderr))
ns_1max1min.append(float(n))
dictmeans_1min1max = mp.loadmeans(fname1min1max)
for condition in keylist:
mean, stdev, stderr, n = dictmeans_1min1max[condition]
conds_1min1max.append(condition)
means_1min1max.append(float(mean))
stdevs_1min1max.append(float(stdev))
stderrs_1min1max.append(float(stderr))
ns_1min1max.append(float(n))
assert conds_1max1min == conds_1min1max, (conds1max1min, conds_1min1max)
conds_1max1min = [cond.replace('MC', '\nMC') for cond in conds_1max1min]
conds_1min1max = [cond.replace('MC', '\nMC') for cond in conds_1min1max]
barnum = len(conds_1min1max)
#### Plots deltatime 1max1min #####
# Defines coordinates for each bar.
lastbar_1max1min = (2*barnum*barwidth) # X-coordinate of last bar
x_gen1_1max1min = np.linspace(barwidth, lastbar_1max1min, barnum).tolist()
x_list_1max1min = x_gen1_1max1min
colors_1max1min = np.tile('k', barnum).tolist()
#Coordinates where the xlabels will be listed.
truebarw = barwidth-(0.05*barwidth)
xlabel_list = [x + truebarw for x in x_list_1max1min]
# Defines limit of x axis.
xlim = x_list_1max1min[-1]+3*barwidth
#Plots the bar plot.
plt.bar(x_list_1max1min, means_1max1min, width=truebarw, color=colors_1max1min, ecolor='k',
label='Filling', lw=lw)
# Plots error bars.
zeros = np.tile(0, len(x_list_1max1min)).tolist()
x_errbar = [x + 0.5*truebarw for x in x_list_1max1min]
if errors == 'stderr':
plt.errorbar(x_errbar, means_1max1min, yerr=[zeros,stderrs_1max1min], fmt=None,
ecolor='k', lw=lw, capsize=2)
if errors == 'stdev':
plt.errorbar(x_errbar, means_1max1min, yerr=[zeros,stdevs_1max1min], fmt=None,
ecolor='k', lw=lw, capsize=2)
#### Plots deltatime 1min1max #####
# Defines coordinates for each bar.
lastbar_1min1max = lastbar_1max1min+barwidth # X-coordinate of last bar
print(lastbar_1min1max)
x_list_1min1max = [x + barwidth for x in x_list_1max1min]
colors_1min1max = np.tile(othercolor, barnum).tolist()
#Plots the bar plot.
plt.bar(x_list_1min1max, means_1min1max, width=truebarw, color=colors_1min1max, ecolor='k',
label='Emptying', lw=lw)
# Plots error bars.
zeros = np.tile(0, len(x_list_1min1max)).tolist()
x_errbar_1min1max = [x + 0.5*truebarw for x in x_list_1min1max]
if errors == 'stderr':
plt.errorbar(x_errbar_1min1max, means_1min1max, yerr=[zeros,stderrs_1min1max], fmt=None,
ecolor='k', lw=lw, capsize=2)
if errors == 'stdev':
plt.errorbar(x_errbar_1min1max, means_1min1max, yerr=[zeros,stdevs_1min1max], fmt=None,
ecolor='k', lw=lw, capsize=2)
# Defines the axes.
ax1 = plt.gca()
# Formats the yticks.
plt.yticks(fontproperties=fontv)
# Plots the xticks.
plt.xticks(xlabel_list, conds_1max1min, multialignment = 'center', fontproperties=fontv)
#Uncomment lines below to display without top and right borders.
for loc, spine in ax1.spines.iteritems():
if loc in ['left','bottom']:
pass
elif loc in ['top', 'right']:
spine.set_color('none') # don't draw spine
else:
raise ValueError('unknown spine location: %s'%loc)
#Uncomment lines below to display ticks only where there are borders.
ax1.xaxis.set_ticks_position('bottom')
ax1.yaxis.set_ticks_position('left')
## Uncomment the line below to remove all of the plot axis lines.
##plt.setp(ax, frame_on=False)
##Uncomment the line below to remove all tick marks/labels.
##ax1.axes.xaxis.set_major_locator(matplotlib.ticker.NullLocator())
# Specifies the number of tickmarks/labels on the yaxis.
ax1.yaxis.set_major_locator(matplotlib.ticker.MaxNLocator(yaxisticks))
#Removes the tickmarks on the x-axis but leaves the labels and the spline.
for line in ax1.get_xticklines():
line.set_visible(False)
plt.ylim(ymax = YLIM)
plt.xlim(xmax = xlim)
# Labels the yaxis; labelpad is the space between the ticklabels and y-axis label.
plt.ylabel(ylabel, labelpad=lp, fontproperties=fontv, multialignment='center')
# Plots legend
legend = plt.legend(loc='upper right', bbox_to_anchor = (1.2, 1.4), markerscale=0.1,
numpoints=1, labelspacing=0.2)
# Changes legend font to fontsz.
ltext = legend.get_texts()
plt.setp(ltext, fontproperties=fontv)
# Removes border around the legend.
legend.draw_frame(False)
# Writes the label for 500 mM sucrose.
line = mpl.lines.Line2D([x_list_1max1min[0],x_list_1max1min[2]+2*barwidth], [-0.183, -0.183],
lw=lw, color='k')
line.set_clip_on(False)
l = ax1.add_line(line)
plt.text(xlabel_list[1], -0.21, '500 mM sucrose', fontproperties=fontv,
horizontalalignment='center', verticalalignment='top',multialignment='center',
rotation=0)
# Writes the label for sucrose.
plt.text(xlabel_list[3]+barwidth, -0.11, 'sucrose', fontproperties=fontv,
horizontalalignment='center', verticalalignment='top',multialignment='center',
rotation=0)
#Adjusts the space between the plot and the edges of the figure; (0,0) is the lower lefthand
#corner of the figure.
fig1.subplots_adjust(bottom=ADJBOTTOM)
fig1.subplots_adjust(right=ADJRIGHT)
fig1.subplots_adjust(left=ADJLEFT)
## Saves the figure with the name 'figname'.
if savefig == 'yes':
plt.savefig('deltatime.png', dpi=FIGDPI)
plt.savefig('deltatime.svg', dpi=FIGDPI)