def plot_data_mlp_to_html(data_points, labels=[]):
# print(len(dataPoints))
data_points = cull_to_number(data_points, 250)
# print(len(dataPoints))
# print(dataPoints)
swap = swapaxes(data_points, 0, 1)
# print(swap)
timescale = []
for i, date in enumerate(swap[0]):
parsedDate = datetime.datetime.strptime(date, '%m/%d/%y %H:%M:%S')
timescale.append(parsedDate)
# timescale = [datetime.datetime.strptime(date, '%m/%d/%y %H:%M:%S') for date in swap[0]]
dates = matplotlib.dates.date2num(timescale)
fig, ax = plt.subplots(figsize=[9.28, 4.8])
plt.rcParams.update({'figure.autolayout': True})
ax.yaxis.set_major_locator(matplotlib.ticker.MultipleLocator(10))
ax.grid(which='major', axis='both', linestyle="-", alpha=0.25, linewidth=1)
ax.tick_params(labelsize='medium', width=1)
# Read labels from list
p1 = ax.plot_date(dates,
swap[1],
color='red',
linestyle='solid',
marker=None,
label=labels[0])
p2 = ax.plot_date(dates,
swap[2],
color='green',
linestyle='solid',
marker=None,
label=labels[1])
p3 = ax.plot_date(dates,
swap[3],
color='blue',
linestyle='solid',
marker=None,
label=labels[2])
s1 = ax.scatter(dates, swap[1], color='#00000000', s=50)
s2 = ax.scatter(dates, swap[2], color='#00000000', s=50)
s3 = ax.scatter(dates, swap[3], color='#00000000', s=50)
ax.legend()
# xlabels = ax.get_xticklabels()
# plt.setp(xlabels, rotation=30, horizontalalignment='right')
ax.set(ylim=[0, 100],
xlabel='Dată',
ylabel='Procent (%)',
title='Grafic umiditate plante')
# Create HTML
plugins.clear(fig)
tooltip_plugin1 = plugins.PointLabelTooltip(
s1, extract_point_position(swap[0], swap[1]))
tooltip_plugin2 = plugins.PointLabelTooltip(
s2, extract_point_position(swap[0], swap[2]))
tooltip_plugin3 = plugins.PointLabelTooltip(
s3, extract_point_position(swap[0], swap[3]))
plugins.connect(fig, tooltip_plugin1, tooltip_plugin2, tooltip_plugin3,
plugins.Zoom(), plugins.Reset())
html_str = fig_to_html(fig)
return html_str
def gen_html(self, dataset=None, channels=["FSC-A", "SSC-A"]):
if not dataset: dataset = self.dataset
data = [dataset[i].values for i in channels]
fig = plt.figure()
ax = fig.add_subplot(111)
plot = ax.scatter(data[0], data[1])
plugins.clear(fig)
plugins.connect(fig, plugins.LinkedBrush(plot),
plugins.ClickSendToBack(plot))
the_html = mpld3.fig_to_html(fig)
with open("initialfigure.html", "w") as file:
file.write(the_html)
o = bs(open("initialfigure.html"), "html.parser")
script = str(o.find_all("script")[0])
script_2 = script.replace("<script>", "").replace("</script>", "")
with open("the_figure.js", "w") as file:
file.write(script_2)
with open("the_figure.html", "w") as file:
the_html = the_html.replace(
script, "<script src='.\\the_figure.js'></script>")
file.write(the_html)
def create_plot():
fig, ax = plt.subplots()
points = ax.scatter(np.random.rand(50), np.random.rand(50),
s=500, alpha=0.3)
plugins.clear(fig)
plugins.connect(fig, plugins.Reset(), plugins.Zoom(), ClickInfo(points))
return fig
def create_plot():
fig, ax = plt.subplots()
points = ax.scatter(random_x, random_y, s=500, alpha=0.3)
plugins.clear(fig)
ax.set_title('Click info', size=14)
plugins.connect(fig, plugins.Reset(), plugins.Zoom(), ClickInfo(points))
return fig
def plot_vdot_race(data):
#CSS element for the tooltip label
css = """
div
{
font-family: Avenir, Helvetica, sans-serif;
border: 1px solid black;
padding-left: 5px;
padding-right: 5px;
text-align: center;
color: #000000;
background-color: #ffffff;
}
"""
#plt.style.use('seaborn-poster') #sets the size of the charts
plt.style.use('ggplot')
x_axis = []
y_axis = []
label = []
#print (data)
for key in data:
label.append("Race: " + str(data[key][1]) + "<br/>" + "VDOT: " +
str(data[key][3]))
y_axis.append(data[key][3])
datetime_obj = datetime.strptime(data[key][4],
'%Y-%m-%d %H:%M:%S+00:00')
x_axis.append(datetime_obj)
dates = matplotlib.dates.date2num(x_axis)
fig, ax = plt.subplots()
plt.xlabel("Race dates", fontsize=20)
plt.ylabel("VDOT Scores", fontsize=20)
plt.title("VDOT Score Over Time", fontsize=25)
line = plt.plot_date(dates,
y_axis,
linestyle='solid',
marker='.',
markersize=14)
plugins.clear(fig) # clear all plugins from the figure
tooltip = plugins.PointHTMLTooltip(line[0],
label,
hoffset=-60,
voffset=-70,
css=css)
plugins.connect(fig, plugins.Reset(), plugins.BoxZoom(), plugins.Zoom(),
tooltip)
return mpld3.fig_to_html(fig)
def make_html_plot_for_file(filename, my_title):
my_fontsize = 20
# read in csv file
if os.stat(filename).st_size:
data = np.genfromtxt(filename, delimiter=',')
else:
# empty data file
data = np.zeros([2, 2])
if len(data.shape) == 1:
return "<h2> " + my_title + " has no data </h2>"
x = data[:, 0]
fig, ax = plt.subplots(figsize=(10, 5))
for k in range(1, data.shape[1]):
y = data[:, k]
# sort data
x_sorted = [x1 for (x1, y1) in sorted(zip(x, y))]
y_sorted = [y1 for (x1, y1) in sorted(zip(x, y))]
lines = ax.plot(x_sorted,
y_sorted,
'.-',
markersize=15,
label='ion ' + str(k - 1))
plt.legend(fontsize=my_fontsize, loc='best')
plt.title(my_title, fontsize=my_fontsize)
ax.grid()
plt.xticks(fontsize=my_fontsize)
plt.yticks(fontsize=my_fontsize)
plt.xlim([np.min(x), np.max(x)])
#90% of the time that this function takes is taken up after this line
plugins.clear(fig)
plugins.connect(fig, plugins.Reset(), plugins.BoxZoom(),
plugins.Zoom(enabled=True),
plugins.MousePosition(fontsize=my_fontsize))
java_txt = mpld3.fig_to_html(fig)
plt.close()
return java_txt
def spladder_viz(options):
"""Main visualization code"""
### parse parameters from options object
options = settings.parse_args(options, identity='viz')
### create plot directory if it does not exist yet
if options.testdir != '-':
dirname = options.testdir
else:
dirname = options.outdir
if not os.path.exists(os.path.join(dirname, 'plots')):
os.mkdir(os.path.join(dirname, 'plots'))
if options.format == 'd3':
try:
import mpld3
from mpld3 import plugins
except ImportError:
sys.stderr.write("ERROR: missing package for output format d3. Package mpld3 required")
sys.exit(1)
### load gene information
gene_names = get_gene_names(options)
rows = get_plot_len(options)
gs = gridspec.GridSpec(rows, 1)
### set color maps
cmap_cov = plt.get_cmap('jet')
cmap_edg = plt.get_cmap('jet')
### plot log scale?
log_tag = ''
if options.log:
log_tag = '.log'
event_tag = ''
### did we get any labels?
if ',' in options.labels:
options.labels = options.labels.strip(',').split(',')
assert len(options.labels) == len(options.bam_fnames), "The number of given labels (%i) needs to match the number of given bam file groups (%i)" % (len(options.labels), len(options.bam_fnames))
# Collect genes to be plotted - there are three cases possible
# 1) the user provides a gene ID
# 2) all genes containing a significant event of a given type from differential testing are plotted
# 3) all genes that contain any event are plotted
### the user chose a specific gene for plotting
### create pairs of gene ids and an event_id (the latter is None by default)
if options.gene_name is not None:
gids = [[sp.where(sp.array(gene_names) == options.gene_name)[0][0], options.event_id]]
if len(gids) == 0:
sys.stderr.write('ERROR: provided gene ID %s could not be found, please check for correctness\n' % options.gene_name)
sys.exit(1)
### the plotting happens on the results of spladder test
### the user chooses to plot the top k significant events
### this requires the event type to be specified
elif options.test_result > 0:
gene_names = []
for event_type in options.event_types:
### the testing script should generate a setup file for the test
### SETUP is structured as follows:
### [gene_strains, event_strains, dmatrix0, dmatrix1, event_type, options]
labels = options.test_labels.split(':')
options.labels = labels
if options.testdir != '-':
testdir = dirname
else:
testdir = os.path.join(dirname, 'testing_%s_vs_%s' % (labels[0], labels[1]))
SETUP = pickle.load(open(os.path.join(testdir, 'test_setup_C%i_%s.pickle' % (options.confidence, event_type)), 'rb'))
### get strains to plot
idx1 = sp.where(sp.in1d(SETUP[0], SETUP[6]['conditionA']))[0]
idx2 = sp.where(sp.in1d(SETUP[0], SETUP[6]['conditionB']))[0]
### load test results
for l, line in enumerate(open(os.path.join(testdir, 'test_results_C%i_%s.tsv' % (options.confidence, event_type)), 'r')):
if l == 0:
continue
if l > options.test_result:
break
sl = line.strip().split('\t')
gene_names.append([sl[1], sl[0]])
gids = get_gene_ids(options, gene_names)
### no gene specified but result provided - plot all genes with confirmed events
### if an event_id is provided, only the associated gene will be plotted
else:
gids = get_gene_ids(options)
### iterate over genes to plot
for gid in gids:
### gather information about the gene we plot
gene = load_genes(options, idx=[gid[0]])[0]
if options.verbose:
print('plotting information for gene %s' % gene.name)
gene.from_sparse()
### event to plot is specified with the gene id list
if gid[1] is not None:
event_info = [x[::-1] for x in re.split(r'[._]', gid[1][::-1], maxsplit=1)[::-1]]
event_info[1] = int(event_info[1]) - 1
event_info = sp.array(event_info, dtype='str')[sp.newaxis, :]
event_tag = '.%s' % gid[1]
### get all confident events of the current gene
else:
event_info = get_conf_events(options, gid[0])
### go over different plotting options
axes = []
### plot result of testing
if options.test_result > 0:
fig = plt.figure(figsize = (9, 5), dpi=200)
gs = gridspec.GridSpec(2, 1, height_ratios=[4, 1])
_add_ax(fig, axes, gs)
_add_ax(fig, axes, gs)
_plot_event(options, event_info, fig, axes[1], gs, None, padding=100)
start, stop = axes[1].get_xlim()
plot_bam(options, gene, options.bam_fnames, fig, axes[0], gs, None, cmap_cov, cmap_edg, single=False, sharex=axes[1], start=int(start), stop=int(stop))
### plot custom layout
else:
### set defaults
if not options.user:
options.splicegraph = True
options.transcripts = True
if options.format == 'd3':
fig = plt.figure(figsize = (12, 2*rows), dpi=100)
else:
fig = plt.figure(figsize = (18, 3*rows), dpi=200)
xlim = None
### plot splicing graph
if options.splicegraph:
_plot_splicegraph(gene, fig, axes, gs)
xlim = axes[-1].get_xlim()
### plot annotated transcripts
if options.transcripts:
sharex = None if len(axes) == 0 else axes[0]
axes.append(fig.add_subplot(gs[len(axes), 0], sharex=sharex))
multiple(gene.exons, ax=axes[-1], x_range=xlim)
axes[-1].set_title('Annotated Transcripts')
### plot coverage information for a set of given samples
if len(options.bam_fnames) > 0:
plot_bam(options, gene, options.bam_fnames, fig, axes, gs, xlim, cmap_cov, cmap_edg)
### plot all the samples in a single plot
if len(options.bam_fnames) > 1:
plot_bam(options, gene, options.bam_fnames, fig, axes, gs, xlim, cmap_cov, cmap_edg, single=False)
### plot segment counts
if len(options.bam_fnames) == 0 or False: # add option for segment plots
if options.test_result > 0:
_plot_segments(options, gid, fig, axes, gs, [idx1, idx2])
else:
_plot_segments(options, gid, fig, axes, gs)
### plot structure of a single given event
_plot_event(options, event_info, fig, axes, gs, xlim)
### we only need to adapt the xoom for one axis object - as we share the x
zoom_x = [float(x) for x in options.zoom_x.split(',')]
xlim = axes[0].get_xlim()
xdiff = xlim[1] - xlim[0]
axes[0].set_xlim([xlim[0] + (zoom_x[0] * xdiff), xlim[0] + (zoom_x[1] * xdiff)])
for ax in axes:
vax.clean_axis(ax)
plt.tight_layout()
### save plot into file
if options.format == 'd3':
out_fname = os.path.join(dirname, 'plots', 'gene_overview_C%i_%s%s%s.html' % (options.confidence, gene.name, event_tag, log_tag))
plugins.clear(fig)
plugins.connect(fig, plugins.Zoom(enabled=True))
mpld3.save_html(fig, open(out_fname, 'w'))
else:
if options.test_result > 0:
out_fname = os.path.join(dirname, 'plots', 'gene_overview_C%i_%s%s%s.%s' % (options.confidence, gene.name, event_tag, log_tag, options.format))
else:
out_fname = os.path.join(dirname, 'plots', 'gene_overview_C%i_%s%s%s.%s' % (options.confidence, gene.name, event_tag, log_tag, options.format))
plt.savefig(out_fname, format=options.format, bbox_inches='tight')
plt.close(fig)
def plot_beats_per_mile(data):
#divide meters by this factor to get in terms of miles
convert_meters_to_miles = 1609.344
#plt.style.use('seaborn-poster') #sets the size of the charts
plt.style.use('ggplot')
#Data is dictionary mapping id to (activity_name, start_date_local, average_hr, distance, total_elevation_gain)
data_by_week = {}
for key in data:
datetime_obj = datetime.strptime(data[key][1], '%Y-%m-%d %H:%M:%S')
year_week_val = datetime_obj.isocalendar()[:-1]
if year_week_val not in data_by_week:
#Storing average_hr, distance, total_elevation_gain
data_by_week[year_week_val] = [(data[key][2], data[key][3],
data[key][4])]
else:
data_by_week[year_week_val].append(
(data[key][2], data[key][3], data[key][4]))
x_axis = []
x_label = []
y_axis = []
for key in data_by_week:
print(key)
total_hr_each_week = 0
distance_week = 0
for activity in data_by_week[key]:
total_hr_each_week += activity[0] * (activity[1] /
convert_meters_to_miles)
distance_week += activity[1] / convert_meters_to_miles
avg_hr_per_mile = total_hr_each_week / distance_week
x_label.append(str(key[0]) + "-" + str(key[1]))
x_axis.append(key[0] * 52 + key[1])
y_axis.append(avg_hr_per_mile)
total_hr_each_week = 0
distance_week = 0
fig, ax = plt.subplots()
plt.xlabel("Weeks", fontsize=20)
plt.ylabel("Average Heartrate Per Mile", fontsize=20)
plt.title("Average Heartrate Per Mile Sorted by Week", fontsize=25)
line = plt.plot(x_axis,
y_axis,
linestyle='solid',
marker='.',
markersize=14)
x = np.asarray(x_axis)
y = np.asarray(y_axis)
b, m = polyfit(x, y, 1)
#Plots regression line
plt.plot(x, b + m * x, linestyle='solid')
plugins.clear(fig) # clear all plugins from the figure
#tooltip = plugins.PointHTMLTooltip(line[0], label,
#hoffset = -60, voffset = -70, css = css)
plugins.connect(fig, plugins.Reset(), plugins.BoxZoom(), plugins.Zoom())
return mpld3.fig_to_html(fig)
def plotPower(sid, MCP='', pow=0, ss=0):
powerdata = PowerTableModel.objects.filter(SID=sid)[::-1][0]
parsdata = ParameterModel.objects.filter(SID=sid)[::-1][0]
powtab = powerdata.data
powtxt = powtab.round(2)
cols = dict(zip(['BF', 'BH', 'RFT', 'UN'], Set1_9.mpl_colors))
sub = int(parsdata.Subj)
newsubs = powtab.newsamplesize
amax = int(50)
css = """
table{border-collapse: collapse}
td{background-color: rgba(217, 222, 230,50)}
table, th, td{border: 1px solid;border-color: rgba(217, 222, 230,50);text-align: right;font-size: 12px}
"""
hover_BF = [
pd.DataFrame([
'Bonferroni', 'Sample Size: ' + str(newsubs[i]),
'Power: ' + str(powtxt['BF'][i])
]).to_html(header=False, index_names=False, index=False)
for i in range(len(powtab))
]
hover_BH = [
pd.DataFrame([
'Benjamini-Hochberg', 'Sample Size: ' + str(newsubs[i]),
'Power: ' + str(powtxt['BH'][i])
]).to_html(header=False, index_names=False, index=False)
for i in range(len(powtab))
]
hover_RFT = [
pd.DataFrame([
'Random Field Theory', 'Sample Size: ' + str(newsubs[i]),
'Power: ' + str(powtxt['RFT'][i])
]).to_html(header=False, index_names=False, index=False)
for i in range(len(powtab))
]
hover_UN = [
pd.DataFrame([
'Uncorrected', 'Sample Size: ' + str(newsubs[i]),
'Power: ' + str(powtxt['UN'][i])
]).to_html(header=False, index_names=False, index=False)
for i in range(len(powtab))
]
fig, axs = plt.subplots(1, 1, figsize=(8, 5))
fig.patch.set_facecolor('None')
lty = ['--' if all(powtab.BF == powtab.RFT) else '-']
BF = axs.plot(newsubs, powtab.BF, 'o', markersize=15, alpha=0, label="")
BH = axs.plot(newsubs, powtab.BH, 'o', markersize=15, alpha=0, label="")
RFT = axs.plot(newsubs, powtab.RFT, 'o', markersize=15, alpha=0, label="")
UN = axs.plot(newsubs, powtab.UN, 'o', markersize=15, alpha=0, label="")
plugins.clear(fig)
plugins.connect(
fig,
plugins.PointHTMLTooltip(BF[0],
hover_BF,
hoffset=0,
voffset=10,
css=css))
plugins.connect(
fig,
plugins.PointHTMLTooltip(BH[0],
hover_BH,
hoffset=0,
voffset=10,
css=css))
plugins.connect(
fig,
plugins.PointHTMLTooltip(RFT[0],
hover_RFT,
hoffset=0,
voffset=10,
css=css))
plugins.connect(
fig,
plugins.PointHTMLTooltip(UN[0],
hover_UN,
hoffset=0,
voffset=10,
css=css))
axs.plot(newsubs, powtab.BF, color=cols['BF'], lw=2, label="Bonferroni")
axs.plot(newsubs,
powtab.BH,
color=cols['BH'],
lw=2,
label="Benjamini-Hochberg")
axs.plot(newsubs,
powtab.RFT,
color=cols['RFT'],
lw=2,
linestyle=str(lty[0]),
label="Random Field Theory")
axs.plot(newsubs, powtab.UN, color=cols['UN'], lw=2, label="Uncorrected")
text = "None"
if pow != 0:
if all(powtab[MCP] < pow):
text = "To obtain a statistical power of " + str(
pow
) + " this study would require a sample size larger than 300 subjects."
else:
min = int(
np.min(
[i
for i, elem in enumerate(powtab[MCP] > pow, 1) if elem]) +
sub - 1)
axs.plot([min, min], [0, powtab[MCP][min - sub]], color=cols[MCP])
axs.plot([sub, min],
[powtab[MCP][min - sub], powtab[MCP][min - sub]],
color=cols[MCP])
text = "To obtain a statistical power of %s this study would require a sample size of %s subjects." % (
pow, min)
amax = max(min, amax)
if ss != 0:
ss_pow = powtab[MCP][ss]
axs.plot([ss, ss], [0, ss_pow], color=cols[MCP], linestyle="--")
axs.plot([sub, ss], [ss_pow, ss_pow], color=cols[MCP], linestyle="--")
xticks = [
x for x in list(np.arange((np.ceil(sub / 10.)) * 10, 100, 10))
if not x == np.round(ss / 10.) * 10
]
axs.set_xticks(xticks + [ss])
axs.set_yticks(list(np.arange(0, 1.1, 0.1)))
text = "A sample size of %s subjects with %s control comes with a power of %s." % (
ss, MCP, str(np.round(ss_pow, decimals=2)))
amax = max(ss, amax)
axs.set_ylim([0, 1])
axs.set_xlim([sub, amax])
axs.set_title("Power curves")
axs.set_xlabel("Subjects")
axs.set_ylabel("Average power")
axs.legend(loc="lower right", frameon=False, title="")
code = mpld3.fig_to_html(fig)
out = {"code": code, "text": text}
return out
def problem_view(request, problem_id):
user = request.user
problem = get_object_or_404(Problem, id=problem_id)
if problem.user_id != user and not user.is_superuser:
raise PermissionDenied
parse_error = False
a = None
b = None
c = None
a_copy = None
b_copy = None
result = None
figure = None
parse_result = parse_problem(problem.problem_text)
print parse_result
if parse_result is not None:
a, b, c, x = parse_result
if a and b and c and x:
result = simple_simplex(copy.deepcopy(a), copy.deepcopy(b),
copy.deepcopy(c))
b.pop(0)
a_copy = copy.deepcopy(a)
b_copy = copy.deepcopy(b)
if len(x) is 2:
tmp = [0, 0]
tmp[1] = 1
a.insert(0, copy.deepcopy(tmp))
tmp[0] = 1
tmp[1] = 0
a.insert(0, copy.deepcopy(tmp))
b.insert(0, 0)
b.insert(0, 0)
dots = []
for i in range(len(b)):
dots.append([])
shape_x = []
for i in xrange(0, len(b)):
for j in xrange(i + 1, len(b)):
if a[i][0] == a[j][0] and a[i][1] == a[j][1]:
continue
shape_a = np.array([[a[i][0], a[i][1]],
[a[j][0], a[j][1]]])
shape_b = np.array([b[i], b[j]])
res = np.linalg.solve(shape_a, shape_b)
dots[i].append(res)
dots[j].append(res)
# fig, ax = plt.subplots()
fig = plt.figure()
ax = fig.add_subplot(111)
for dot in dots:
ax.plot(
[float(dot[0][0]), float(dot[1][0])],
[float(dot[0][1]), float(dot[1][1])],
ls='-',
color='green',
marker='o',
lw=2)
# print([dot[0], dot[1]])
ax.set_xlabel('X axis')
ax.set_ylabel('Y axis')
ax.set_title('Plot of 2D Problem!', size=14)
plugins.clear(fig)
plugins.connect(fig, plugins.Reset(),
plugins.Zoom(enabled=True), plugins.BoxZoom())
figure = fig_to_html(fig,
d3_url=STATIC_URL + 'js/d3.min.js',
mpld3_url=STATIC_URL + 'js/mpld3.v0.2.js',
use_http=True)
else:
figure = 'Larger than 2D!'
else:
parse_error = True
return render_to_response(
'problems/view.html', {
'user': user,
'problem': problem,
'parse_error': parse_error,
'figure': figure,
'a': a_copy,
'b': b_copy,
'c': c,
'result': result,
'view_name': 'Problem - %s' % problem.id,
})
def plotPower(sid,MCP='',pow=0,ss=0):
powerdata = PowerTableModel.objects.filter(SID=sid)[::-1][0]
parsdata = ParameterModel.objects.filter(SID=sid)[::-1][0]
powtab = powerdata.data
powtxt = powtab.round(2)
cols = dict(zip(['BF','BH','RFT','UN'],Set1_9.mpl_colors))
sub = int(parsdata.Subj)
newsubs = powtab.newsamplesize
amax = int(np.min(powtab.newsamplesize)+50)
css = """
table{border-collapse: collapse}
td{background-color: rgba(217, 222, 230,50)}
table, th, td{border: 1px solid;border-color: rgba(217, 222, 230,50);text-align: right;font-size: 12px}
"""
hover_BF = [pd.DataFrame(['Bonferroni','Sample Size: '+str(newsubs[i]),'Power: '+str(powtxt['BF'][i])]).to_html(header=False,index_names=False,index=False) for i in range(len(powtab)) if 'BF' in powtab.columns]
hover_BH = [pd.DataFrame(['Benjamini-Hochberg','Sample Size: '+str(newsubs[i]),'Power: '+str(powtxt['BH'][i])]).to_html(header=False,index_names=False,index=False) for i in range(len(powtab)) if 'BH' in powtab.columns]
hover_RFT = [pd.DataFrame(['Random Field Theory','Sample Size: '+str(newsubs[i]),'Power: '+str(powtxt['RFT'][i])]).to_html(header=False,index_names=False,index=False) for i in range(len(powtab)) if 'RFT' in powtab.columns]
hover_UN = [pd.DataFrame(['Uncorrected','Sample Size: '+str(newsubs[i]),'Power: '+str(powtxt['UN'][i])]).to_html(header=False,index_names=False,index=False) for i in range(len(powtab)) if 'UN' in powtab.columns]
fig,axs=plt.subplots(1,1,figsize=(8,5))
fig.patch.set_facecolor('None')
lty = ['--' if all(powtab.BF==powtab.RFT) else '-']
BF=axs.plot(powtab.newsamplesize,powtab.BF,'o',markersize=15,alpha=0,label="") if 'BF' in powtab.columns else 'nan'
BH=axs.plot(powtab.newsamplesize,powtab.BH,'o',markersize=15,alpha=0,label="") if 'BH' in powtab.columns else 'nan'
RFT=axs.plot(powtab.newsamplesize,powtab.RFT,'o',markersize=15,alpha=0,label="") if 'RFT' in powtab.columns else 'nan'
UN=axs.plot(powtab.newsamplesize,powtab.UN,'o',markersize=15,alpha=0,label="") if 'UN' in powtab.columns else 'nan'
plugins.clear(fig)
plugins.connect(fig, plugins.PointHTMLTooltip(BF[0], hover_BF,hoffset=0,voffset=10,css=css))
plugins.connect(fig, plugins.PointHTMLTooltip(RFT[0], hover_RFT,hoffset=0,voffset=10,css=css))
plugins.connect(fig, plugins.PointHTMLTooltip(UN[0], hover_UN,hoffset=0,voffset=10,css=css))
if 'BH' in powtab.columns:
plugins.connect(fig, plugins.PointHTMLTooltip(BH[0], hover_BH,hoffset=0,voffset=10,css=css))
axs.plot(newsubs,powtab.BH,color=cols['BH'],lw=2,label="Benjamini-Hochberg")
axs.plot(newsubs,powtab.BF,color=cols['BF'],lw=2,label="Bonferroni")
axs.plot(newsubs,powtab.RFT,color=cols['RFT'],lw=2,linestyle=str(lty[0]),label="Random Field Theory")
axs.plot(newsubs,powtab.UN,color=cols['UN'],lw=2,label="Uncorrected")
text = "None"
if pow != 0:
if MCP == 'BH' and not 'BH' in powtab.columns:
text = "There is not enough power to estimate a threshold for FDR control. As such it's impossible to predict power for FDR control."
elif all(powtab[MCP]<pow):
text = "To obtain a statistical power of "+str(pow)+" this study would require a sample size larger than 600 subjects."
amax = max(powtab.newsamplesize)
else:
min = int(np.min([i for i,elem in enumerate(powtab[MCP]>pow,1) if elem])+sub-1)
axs.plot([min,min],[0,powtab[MCP][min-sub]],color=cols[MCP])
axs.plot([sub,min],[powtab[MCP][min-sub],powtab[MCP][min-sub]],color=cols[MCP])
text = "To obtain a statistical power of %s this study would require a sample size of %s subjects." %(pow,min)
amax = max(min,amax)
if ss != 0:
if MCP == 'BH' and not 'BH' in powtab.columns:
text = "There is not enough power to estimate a threshold for FDR control. As such it's impossible to predict power for FDR control."
else:
ss_pow = powtab[MCP][ss]
axs.plot([ss,ss],[0,ss_pow],color=cols[MCP],linestyle="--")
axs.plot([sub,ss],[ss_pow,ss_pow],color=cols[MCP],linestyle="--")
xticks = [x for x in list(np.arange((np.ceil(sub/10.))*10,100,10)) if not x == np.round(ss/10.)*10]
axs.set_xticks(xticks+[ss])
axs.set_yticks(list(np.arange(0,1.1,0.1)))
text = "A sample size of %s subjects with %s control comes with a power of %s." %(ss,MCP,str(np.round(ss_pow,decimals=2)))
amax = max(ss,amax)
axs.set_ylim([0,1])
axs.set_xlim([sub,amax])
axs.set_title("Power curves")
axs.set_xlabel("Subjects")
axs.set_ylabel("Average power")
axs.legend(loc="lower right",frameon=False,title="")
code = mpld3.fig_to_html(fig)
out = {
"code":code,
"text":text
}
return out
def shallow_water(ql=np.array([3.0, 5.0]), qr=np.array([3.0, -5.0]), g=1.0, time=2.0, tmax=5.0,
hmax=None, humin=None, humax=None):
# Create figure
# Create a figure
#fig, axfull = plt.subplots(2,2, figsize=(13, 12))
fig, axfull = plt.subplots(2,2, figsize=(10, 8))
fig.subplots_adjust(left=0.05, right=0.9, bottom=0.1, top=0.9,
hspace=0.3, wspace=0.15)
# Have to do this to avoid issue with mpld3
ax = axfull[0] # First row of plots
axsol = axfull[1] # Second row of plost
# Calculate dq with ql and qr and other parameters
dq = np.array([qr[0]-ql[0], qr[1]-ql[1]])
iters = 100
iter_charac = 2
# eps required for bug in mpld3 (ql[0] cannot be the same than qr[0])
eps = 0.00000001
# Calculate plotting boundaries if not specified
if hmax is None:
hmax = max(ql[0],qr[0]) + 7.0
if humax is None:
humax = 3*max(abs(ql[1]),abs(qr[1]))
if humin is None:
humin = -3*max(abs(ql[1]),abs(qr[1]))
# PLOT PHASE PLANE
xxL = np.linspace(0,ql[0],iters)
xxL2 = np.linspace(ql[0],hmax,iters)
xxR = np.linspace(0,qr[0]+eps,iters)
xxR2 = np.linspace(qr[0]+eps,hmax,iters)
#Plot midpoint
qm = -1 +0.0*ql
midpoint = ax[0].plot(qm[0],qm[1],'ok', alpha=0.9, markersize=8, markeredgewidth=1)
# Plot hugoloci initial state
yyL = hugoloci(g,xxL,ql[0],ql[1],-1)
yyL2 = hugoloci(g,xxL2,ql[0],ql[1],-1)
yyR = hugoloci(g,xxR,qr[0],qr[1],1)
yyR2 = hugoloci(g,xxR2,qr[0],qr[1],1)
hugol = ax[0].plot(xxL,yyL, '--r', linewidth=1.5)
hugol2 = ax[0].plot(xxL2,yyL2, '-r', linewidth=2, label = 'Hugoniot Loci')
hugor = ax[0].plot(xxR,yyR, '--r', linewidth=1.5, label = 'Hugoniot Loci (unphysical)')
hugor2 = ax[0].plot(xxR2,yyR2, '-r', linewidth=2)
# Plot integral curve initial state
yyL = intcurve(g,xxL,ql[0],ql[1],-1)
yyL2 = intcurve(g,xxL2,ql[0],ql[1],-1)
yyR = intcurve(g,xxR,qr[0],qr[1],1)
yyR2 = intcurve(g,xxR2,qr[0],qr[1],1)
intcl = ax[0].plot(xxL,yyL, '-b', linewidth=2, label = 'Integral Curves')
intcl2 = ax[0].plot(xxL2,yyL2, '--b', linewidth=1.5, label = 'Integral Curves (unphysical)')
intcr = ax[0].plot(xxR,yyR, '-b', linewidth=2)
intcr2 = ax[0].plot(xxR2,yyR2, '--b', linewidth=1.5)
# Plot ql and qr
points = ax[0].plot([ql[0],qr[0]], [ql[1], qr[1]], 'ok', alpha=0.7, markersize=10, markeredgewidth=1)
#data = ["q_l", "q_r"]
# Plot markers
offsetx = 0.3*hmax/10
offsety = -3*offsetx
qlmarker = ax[0].plot(ql[0]+offsetx, ql[1]+offsety, 'ok', marker=(r"$ q_l $"), markersize=15)
qmmarker = ax[0].plot(qm[0]+offsetx, qm[1]+offsety, 'ok', marker=(r"$ q_m $"), markersize=15)
qrmarker = ax[0].plot(qr[0]+offsetx, qr[1]+offsety, 'ok', marker=(r"$ q_r $"), markersize=15)
# Set axis 1 properties
ax[0].set_title("Phase plane", fontsize=18)
ax[0].axis([0,hmax,humin,humax])
ax[0].set_xlabel('h', fontsize=17)
ax[0].set_ylabel('hu', fontsize=17)
#ax[0].set_aspect('equal')
ax[0].grid(alpha=0.1,color='k', linestyle='--')
legend = ax[0].legend(loc='upper left', shadow=True, fontsize = 8)
# PLOT x-t PLANE
x_xtp = np.linspace(-10,10,iter_charac)
x_xtp2 = np.linspace(-11,11,iter_charac)
# Shock speeds lam1 and lam2
lam2 = ql[1]/ql[0] - np.sqrt(g*ql[0])
lam1 = qr[1]/qr[0] + np.sqrt(g*qr[0])
char1 = x_xtp/lam1
char2 = x_xtp/lam2
char3 = x_xtp2/lam1
char4 = x_xtp2/lam2
shock1 = ax[1].plot(x_xtp, char1, '-k', linewidth=4, label="1 or 2 shock")
shock2 = ax[1].plot(x_xtp, char2, '-k', linewidth=4)
rar1 = ax[1].plot(x_xtp2, char3, '-k', linewidth=1, label="1 or 2 rarefaction")
rar2 = ax[1].plot(x_xtp2, char4, '-k', linewidth=1)
# For other 4 rarefaction lines on each side
x_xtp3 = np.linspace(-11.1,11.1,iter_charac)
x_xtp4 = np.linspace(-11.2,11.2,iter_charac)
x_xtp5 = np.linspace(-11.3,11.3,iter_charac)
x_xtp6 = np.linspace(-11.4,11.4,iter_charac)
char1a = x_xtp3/lam1; char2a = x_xtp3/lam2
char1b = x_xtp4/lam1; char2b = x_xtp4/lam2
char1c = x_xtp5/lam1; char2c = x_xtp5/lam2
char1d = x_xtp6/lam1; char2d = x_xtp6/lam2
rar1a = ax[1].plot(x_xtp3, char1a, '-k', linewidth=1)
rar2a = ax[1].plot(x_xtp3, char2a, '-k', linewidth=1)
rar1b = ax[1].plot(x_xtp4, char1b, '-k', linewidth=1)
rar2b = ax[1].plot(x_xtp4, char2b, '-k', linewidth=1)
rar1c = ax[1].plot(x_xtp5, char1c, '-k', linewidth=1)
rar2c = ax[1].plot(x_xtp5, char2c, '-k', linewidth=1)
rar1d = ax[1].plot(x_xtp6, char1d, '-k', linewidth=1)
rar2d = ax[1].plot(x_xtp6, char2d, '-k', linewidth=1)
timedot = ax[1].plot([100000,1000000,9], [-10,-10,time], 'ok' , alpha=0.7, markersize=10)
timeline = ax[1].plot([-12,0,12], [time, time, time], '--k', linewidth = 3, label="time")
# Set axis 2 properties
ax[1].set_title("x-t plane", fontsize=18)
ax[1].set_xlabel('x', fontsize=17)
ax[1].set_ylabel('t', fontsize=17)
ax[1].axis([-10,10,-0,tmax])
ax[1].grid(alpha=0.1,color='k', linestyle='--')
legend = ax[1].legend(loc='upper center', shadow=True, fontsize = 8)
# PLOT SOLUTIONS
xsol = np.linspace(-10,10,2*iters)
hsol = 0*xsol + ql[0]
husol = 0*xsol + ql[1]
q1 = axsol[0].plot(xsol,hsol, '-k', linewidth = 4, alpha = 1.0)
q2 = axsol[1].plot(xsol,husol, '-k', linewidth = 4, alpha = 1.0)
def solplot(xsol,ql,qr,qm,g):
hl = ql[0]; hm = qm[0]; hr = qr[0]
ul = ql[1]/hl; um = qm[1]/hm; ur = qr[1]/hr
lam = np.empty(4, dtype=float)
# Set axis 3 properties
axsol[0].set_title("Depth h at time = t", fontsize=18)
axsol[0].set_xlabel('x', fontsize=17)
axsol[0].set_ylabel('h', fontsize=17)
axsol[0].axis([-10,10,0,hmax])
axsol[0].grid(alpha=0.1,color='k', linestyle='--')
# Set axis 4 properties
axsol[1].set_title("Momentum hu at time = t", fontsize=18)
axsol[1].set_xlabel('x', fontsize=17)
axsol[1].set_ylabel('hu', fontsize=17)
axsol[1].axis([-10,10,humin,humax])
axsol[1].grid(alpha=0.1,color='k', linestyle='--')
# Remove defult mpld3 plugins
plugins.clear(fig)
# Call mpld3 custom PPLane plugin to interact with plot
plugins.connect(fig, PPlaneNLPlugin(points[0],midpoint[0],
g,iters,iter_charac,time, hmax, offsetx,
hugol[0],hugor[0],intcl[0],intcr[0],
hugol2[0],hugor2[0],intcl2[0],intcr2[0],
qlmarker[0],qmmarker[0],qrmarker[0],
shock1[0],shock2[0],rar1[0],rar2[0],
rar1a[0],rar2a[0],rar1b[0],rar2b[0],
rar1c[0],rar2c[0],rar1d[0],rar2d[0],
timedot[0],timeline[0],
q1[0],q2[0]))
return fig
def spladder_viz():
"""Main visualization code"""
### parse command line parameters
options = parse_options(sys.argv)
### create plot directory if it does not exist yet
if not os.path.exists(os.path.join(options.outdir, 'plots')):
os.mkdir(os.path.join(options.outdir, 'plots'))
if options.format == 'd3':
try:
import mpld3
from mpld3 import plugins
except ImportError:
sys.stderr.write(
"ERROR: missing package for format d3. Package mpld3 required")
sys.exit(1)
### load gene information
genes = load_genes(options)
rows = get_plot_len(options)
gs = gridspec.GridSpec(rows, 1)
### get coloring
cmap_cov = plt.get_cmap('jet')
cmap_edg = plt.get_cmap('jet')
### plot log scale?
log_tag = ''
if options.log:
log_tag = '.log'
event_tag = ''
### did we get any labels?
if options.labels != '-':
options.labels = options.labels.strip(',').split(',')
assert len(options.labels) == len(
options.bams.strip(':').split(':')
), "The number of given labels (%i) needs to match the number of given bam file groups (%i)" % (
len(options.labels), len(options.bams.strip(':').split(':')))
### the user specified a gene to plot
if options.gene_name is not None:
gid = sp.where(
sp.array([x.name.split('.')[0]
for x in genes]) == options.gene_name.split('.')[0])[0]
if gid.shape[0] == 0:
sys.stderr.write(
'ERROR: provided gene ID %s could not be found, please check for correctness\n'
% options.gene_name)
sys.exit(1)
gids = [
sp.where(sp.array([x.name
for x in genes]) == options.gene_name)[0][0]
]
### no gene specified but result provided - plot all genes with confirmed events
### if an event_id is provided, only the associated gene will be plotted
else:
gids = get_gene_ids(options)
### iterate over genes to plot
for gid in gids:
if options.format == 'd3':
fig = plt.figure(figsize=(12, 2 * rows), dpi=100)
else:
fig = plt.figure(figsize=(18, 3 * rows), dpi=200)
axes = []
### gather information about the gene we plot
gene = get_gene(genes[gid])
if options.verbose:
print 'plotting information for gene %s' % gene.name
### plot splicing graph
axes.append(fig.add_subplot(gs[len(axes), 0]))
plot_graph(gene.splicegraph.vertices, gene.splicegraph.edges, axes[-1])
xlim = axes[-1].get_xlim()
axes[-1].set_title('Splicing graph for %s' % gene.name)
### plot annotated transcripts
if options.transcripts:
axes.append(fig.add_subplot(gs[len(axes), 0], sharex=axes[0]))
multiple(gene.exons, ax=axes[-1], x_range=xlim)
axes[-1].set_title('Annotated Transcripts')
### plot coverage information for a set of samples
if options.bams != '-':
samples = options.bams.strip(':').split(':')
plot_bam(options, gene, samples, fig, axes, gs, xlim, cmap_cov,
cmap_edg)
### plot all the samples in a single plot
if len(samples) > 1:
plot_bam(options,
gene,
samples,
fig,
axes,
gs,
xlim,
cmap_cov,
cmap_edg,
single=False)
### plot segment counts
(segments, edges, edge_idx) = get_seg_counts(options, gid)
axes.append(fig.add_subplot(gs[len(axes), 0], sharex=axes[0]))
if identity() == 'matlab':
cov_from_segments(gene,
segments,
edges,
edge_idx,
axes[-1],
xlim=xlim,
log=options.log,
grid=True,
order='F')
else:
cov_from_segments(gene,
segments,
edges,
edge_idx,
axes[-1],
xlim=xlim,
log=options.log,
grid=True,
order='C')
axes[-1].set_title('Segment counts')
### plot structure of a single given event
#if options.event_id is not None:
axes.append(fig.add_subplot(gs[len(axes), 0], sharex=axes[0]))
### user defined event to plot
if options.event_id is not None:
event_info = [
x[::-1] for x in re.split(
r'[._]', options.event_id[::-1], maxsplit=1)[::-1]
]
event_info[1] = int(event_info[1]) - 1
event_info = sp.array(event_info, dtype='str')[sp.newaxis, :]
event_tag = '.%s' % options.event_id
### get all significant events of the current gene
else:
event_info = get_conf_events(options, gid)
plot_event(options, event_info, axes[-1], xlim)
### we only need to adapt the xoom for one axis object - as we share the x
zoom_x = [float(x) for x in options.zoom_x.split(',')]
xlim = axes[0].get_xlim()
xdiff = xlim[1] - xlim[0]
axes[0].set_xlim(
[xlim[0] + (zoom_x[0] * xdiff), xlim[0] + (zoom_x[1] * xdiff)])
plt.tight_layout()
### save plot into file
if options.format == 'd3':
out_fname = os.path.join(
options.outdir, 'plots', 'gene_overview_C%i_%s%s%s.html' %
(options.confidence, gene.name, event_tag, log_tag))
plugins.clear(fig)
plugins.connect(fig, plugins.Zoom(enabled=True))
mpld3.save_html(fig, open(out_fname, 'w'))
else:
out_fname = os.path.join(
options.outdir, 'plots', 'gene_overview_C%i_%s%s%s.%s' %
(options.confidence, gene.name, event_tag, log_tag,
options.format))
plt.savefig(out_fname, format=options.format, bbox_inches='tight')
plt.close(fig)
def linear_phase_plane(ql=np.array([-2.0, 2.0]),qr=np.array([0.0, 3.0]),
r1=None, r2=None, lam1=None, lam2=None,
q1min=-5, q1max=5, q2min =-5, q2max=5, domain=5, time=1.0,
title1=None, title2=None):
# Create figure
# Create a figure
fig, ax = plt.subplots(1,3, figsize=(13.5, 5.0))
fig.subplots_adjust(left=0.05, right=0.9, bottom=0.2, top=0.9,
hspace=0.3, wspace=0.15)
#Create subfigure ax1 for phaseplane
#ax[0] = fig.add_subplot(2,2,1)
# Calculate dq from ql and qr
dq = np.array([qr[0]-ql[0], qr[1]-ql[1]])
# If eigenvectors not specified, default to Acoustics
rho0 = 1.0
zz = 2.0
if r1 is None:
r1 = np.array([-zz, 1])
if r2 is None:
r2 = np.array([zz, 1])
if lam1 is None:
lam1 = -zz/rho0
if lam2 is None:
lam2 = zz/rho0
# Choose left and right eigenvectors
if (lam1 < lam2):
laml = lam1; lamr = lam2
rl = r1; rr = r2
else:
laml = lam2; lamr = lam1
rl = r2; rr = r1
# Assign titles to solution plots
if title1 is None:
title1 = "q1"
if title2 is None:
title2 = "q2"
#Plot eigenlines across ql and qr
eps = 0.00000000001 # To avoid bugs in D3
ml = rl[1]/rl[0]
mr = rr[1]/rr[0] + eps
linesla = ax[0].plot([q1min,q1max],[ml*(q1min - ql[0]) + ql[1],ml*(q1max - ql[0]) + ql[1]], '-k')
lineslb = ax[0].plot([q1min-eps,q1max+eps],[mr*(q1min - ql[0]) + ql[1],mr*(q1max - ql[0]) + ql[1]], '-k')
linesra = ax[0].plot([q1min-2*eps,q1max+2*eps],[mr*(q1min - qr[0]) + qr[1],mr*(q1max - qr[0]) + qr[1]], '-k')
linesrb = ax[0].plot([q1min-3*eps,q1max+3*eps],[ml*(q1min - qr[0]) + qr[1],ml*(q1max - qr[0]) + qr[1]], '-k')
# Plot ql and qr
points = ax[0].plot([ql[0],qr[0]], [ql[1], qr[1]], 'ok', alpha=0.7, markersize=10, markeredgewidth=1)
data = ["q_l", "q_r"]
offset = 0.4*0.5*(q1max-q1min)/5.0
qlmarker = ax[0].plot(ql[0] + offset, ql[1] - offset, 'ok', marker=(r"$ q_l $"), markersize=15)
qrmarker = ax[0].plot(qr[0] + offset, qr[1] - offset, 'ok', marker=(r"$ q_r $"), markersize=15)
#Plot midpoint
det = rl[0]*rr[1] - rr[0]*rl[1]
alL = (rr[1]*dq[0] - rr[0]*dq[1])/det
qm = ql + alL*rl
midpoint = ax[0].plot(qm[0],qm[1],'ok', alpha=0.9, markersize=8, markeredgewidth=1)
qmmarker = ax[0].plot(qm[0]+offset,qm[1]-0.7*offset, 'k',marker=(r"$ q_m $"),markersize=20)
# Set axis 1 properties
ax[0].set_title("Phase Plane", fontsize=18)
ax[0].axis([q1min,q1max,q2min,q2max])
ax[0].grid(alpha=0.1,color='k', linestyle='--')
ax[0].set_xlabel(title1)
ax[0].set_ylabel(title2)
# Remove defult mpld3 plugins
plugins.clear(fig)
# Create solutionl line with six points where solution should be
ctleft = laml*time
ctright = lamr*time
domain = max(domain,abs(ctleft)+1,abs(ctright)+1) # Readjust xdomain if necessarry
xsol = np.array([-domain,ctleft,ctleft,ctright,ctright,domain])
qsol1 = 1*xsol
qsol1[0:2] = ql[0]
qsol1[2:4] = qm[0]
qsol1[4:6] = qr[0]
# Set axis 2 properties
ax[1].set_title(title1, fontsize=18)
ax[1].axis([-domain,domain,q1min,q1max])
ax[1].grid(alpha=0.1,color='k', linestyle='--')
ax[1].set_xlabel('x')
#ax[1].set_ylabel(title1)
# Plot solution
qone = ax[1].plot(xsol, qsol1, '-k', linewidth = 4, alpha = 1.0)
#Create subfigure ax2 for solution plane
#ax[2] = fig.add_subplot(2,2,2)
# Create solutionl line with six points
xsol2 = np.array([-domain,ctleft,ctleft,ctright,ctright,domain])
qsol2 = 1*xsol2
qsol2[0:2] = ql[1]
qsol2[2:4] = qm[1]
qsol2[4:6] = qr[1]
# Set axis 2 properties
ax[2].set_title(title2, fontsize=18)
ax[2].axis([-domain,domain,q2min,q2max])
ax[2].grid(alpha=0.1,color='k', linestyle='--')
ax[2].set_xlabel('x')
#ax[2].set_ylabel(title2)
# Plot solution
qtwo = ax[2].plot(xsol2, qsol2, '-k', linewidth = 4, alpha = 1.0)
# Call mpld3 custom PPLane plugin to interact with plot
plugins.connect(fig, PPlanePlugin(points[0],midpoint[0],
linesla[0],lineslb[0],linesra[0],linesrb[0],
qlmarker[0],qmmarker[0],qrmarker[0],qone[0],qtwo[0],
rl[0],rl[1],rr[0],rr[1]))
return fig
def shallow_water(ql=np.array([3.0, 5.0]), qr=np.array([3.0, -5.0]), g=1.0, time=2.0, tmax=5.0,
hmax=None, humin=None, humax=None):
# Create figure
# Create a figure
#fig, axfull = plt.subplots(2,2, figsize=(13, 12))
fig, axfull = plt.subplots(2,2, figsize=(10, 8))
fig.subplots_adjust(left=0.05, right=0.9, bottom=0.1, top=0.9,
hspace=0.3, wspace=0.15)
# Have to do this to avoid issue with mpld3
ax = axfull[0] # First row of plots
axsol = axfull[1] # Second row of plost
# Calculate dq with ql and qr and other parameters
dq = np.array([qr[0]-ql[0], qr[1]-ql[1]])
iters = 100
iter_charac = 2
# eps required for bug in mpld3 (ql[0] cannot be the same than qr[0])
eps = 0.00000001
# Calculate plotting boundaries if not specified
if hmax is None:
hmax = max(ql[0],qr[0]) + 7.0
if humax is None:
humax = 3*max(abs(ql[1]),abs(qr[1]))
if humin is None:
humin = -3*max(abs(ql[1]),abs(qr[1]))
# PLOT PHASE PLANE
xxL = np.linspace(0,ql[0],iters)
xxL2 = np.linspace(ql[0],hmax,iters)
xxR = np.linspace(0,qr[0]+eps,iters)
xxR2 = np.linspace(qr[0]+eps,hmax,iters)
#Plot midpoint
qm = -1 +0.0*ql
midpoint = ax[0].plot(qm[0],qm[1],'ok', alpha=0.9, markersize=8, markeredgewidth=1)
# Plot hugoloci initial state
yyL = hugoloci(g,xxL,ql[0],ql[1],-1)
yyL2 = hugoloci(g,xxL2,ql[0],ql[1],-1)
yyR = hugoloci(g,xxR,qr[0],qr[1],1)
yyR2 = hugoloci(g,xxR2,qr[0],qr[1],1)
hugol = ax[0].plot(xxL,yyL, '--r', linewidth=1.5)
hugol2 = ax[0].plot(xxL2,yyL2, '-r', linewidth=2, label = 'Hugoniot Loci')
hugor = ax[0].plot(xxR,yyR, '--r', linewidth=1.5, label = 'Hugoniot Loci (unphysical)')
hugor2 = ax[0].plot(xxR2,yyR2, '-r', linewidth=2)
# Plot integral curve initial state
yyL = intcurve(g,xxL,ql[0],ql[1],-1)
yyL2 = intcurve(g,xxL2,ql[0],ql[1],-1)
yyR = intcurve(g,xxR,qr[0],qr[1],1)
yyR2 = intcurve(g,xxR2,qr[0],qr[1],1)
intcl = ax[0].plot(xxL,yyL, '-b', linewidth=2, label = 'Integral Curves')
intcl2 = ax[0].plot(xxL2,yyL2, '--b', linewidth=1.5, label = 'Integral Curves (unphysical)')
intcr = ax[0].plot(xxR,yyR, '-b', linewidth=2)
intcr2 = ax[0].plot(xxR2,yyR2, '--b', linewidth=1.5)
# Plot ql and qr
points = ax[0].plot([ql[0],qr[0]], [ql[1], qr[1]], 'ok', alpha=0.7, markersize=10, markeredgewidth=1)
#data = ["q_l", "q_r"]
# Plot markers
offsetx = 0.3*hmax/10
offsety = -3*offsetx
qlmarker = ax[0].plot(ql[0]+offsetx, ql[1]+offsety, 'ok', marker=(r"$ q_l $"), markersize=15)
qmmarker = ax[0].plot(qm[0]+offsetx, qm[1]+offsety, 'ok', marker=(r"$ q_m $"), markersize=15)
qrmarker = ax[0].plot(qr[0]+offsetx, qr[1]+offsety, 'ok', marker=(r"$ q_r $"), markersize=15)
# Set axis 1 properties
ax[0].set_title("Phase plane", fontsize=18)
ax[0].axis([0,hmax,humin,humax])
ax[0].set_xlabel('h', fontsize=17)
ax[0].set_ylabel('hu', fontsize=17)
#ax[0].set_aspect('equal')
ax[0].grid(alpha=0.1,color='k', linestyle='--')
legend = ax[0].legend(loc='upper left', shadow=True, fontsize = 8)
# PLOT x-t PLANE
x_xtp = np.linspace(-10,10,iter_charac)
x_xtp2 = np.linspace(-11,11,iter_charac)
# Shock speeds lam1 and lam2
lam2 = ql[1]/ql[0] - np.sqrt(g*ql[0])
lam1 = qr[1]/qr[0] + np.sqrt(g*qr[0])
char1 = x_xtp/lam1
char2 = x_xtp/lam2
char3 = x_xtp2/lam1
char4 = x_xtp2/lam2
shock1 = ax[1].plot(x_xtp, char1, '-k', linewidth=4, label="1 or 2 shock")
shock2 = ax[1].plot(x_xtp, char2, '-k', linewidth=4)
rar1 = ax[1].plot(x_xtp2, char3, '-k', linewidth=1, label="1 or 2 rarefaction")
rar2 = ax[1].plot(x_xtp2, char4, '-k', linewidth=1)
# For other 4 rarefaction lines on each side
x_xtp3 = np.linspace(-11.1,11.1,iter_charac)
x_xtp4 = np.linspace(-11.2,11.2,iter_charac)
x_xtp5 = np.linspace(-11.3,11.3,iter_charac)
x_xtp6 = np.linspace(-11.4,11.4,iter_charac)
char1a = x_xtp3/lam1; char2a = x_xtp3/lam2
char1b = x_xtp4/lam1; char2b = x_xtp4/lam2
char1c = x_xtp5/lam1; char2c = x_xtp5/lam2
char1d = x_xtp6/lam1; char2d = x_xtp6/lam2
rar1a = ax[1].plot(x_xtp3, char1a, '-k', linewidth=1)
rar2a = ax[1].plot(x_xtp3, char2a, '-k', linewidth=1)
rar1b = ax[1].plot(x_xtp4, char1b, '-k', linewidth=1)
rar2b = ax[1].plot(x_xtp4, char2b, '-k', linewidth=1)
rar1c = ax[1].plot(x_xtp5, char1c, '-k', linewidth=1)
rar2c = ax[1].plot(x_xtp5, char2c, '-k', linewidth=1)
rar1d = ax[1].plot(x_xtp6, char1d, '-k', linewidth=1)
rar2d = ax[1].plot(x_xtp6, char2d, '-k', linewidth=1)
timedot = ax[1].plot([100000,1000000,9], [-10,-10,time], 'ok' , alpha=0.7, markersize=10)
timeline = ax[1].plot([-12,0,12], [time, time, time], '--k', linewidth = 3, label="time")
# Set axis 2 properties
ax[1].set_title("x-t plane", fontsize=18)
ax[1].set_xlabel('x', fontsize=17)
ax[1].set_ylabel('t', fontsize=17)
ax[1].axis([-10,10,-0,tmax])
ax[1].grid(alpha=0.1,color='k', linestyle='--')
legend = ax[1].legend(loc='upper center', shadow=True, fontsize = 8)
# PLOT SOLUTIONS
xsol = np.linspace(-10,10,2*iters)
hsol = 0*xsol + ql[0]
husol = 0*xsol + ql[1]
q1 = axsol[0].plot(xsol,hsol, '-k', linewidth = 4, alpha = 1.0)
q2 = axsol[1].plot(xsol,husol, '-k', linewidth = 4, alpha = 1.0)
def solplot(xsol,ql,qr,qm,g):
hl = ql[0]; hm = qm[0]; hr = qr[0]
ul = ql[1]/hl; um = qm[1]/hm; ur = qr[1]/hr
lam = np.empty(4, dtype=float)
# Set axis 3 properties
axsol[0].set_title("Depth h at time = t", fontsize=18)
axsol[0].set_xlabel('x', fontsize=17)
axsol[0].set_ylabel('h', fontsize=17)
axsol[0].axis([-10,10,0,hmax])
axsol[0].grid(alpha=0.1,color='k', linestyle='--')
# Set axis 4 properties
axsol[1].set_title("Momentum hu at time = t", fontsize=18)
axsol[1].set_xlabel('x', fontsize=17)
axsol[1].set_ylabel('hu', fontsize=17)
axsol[1].axis([-10,10,humin,humax])
axsol[1].grid(alpha=0.1,color='k', linestyle='--')
# Remove defult mpld3 plugins
plugins.clear(fig)
# Call mpld3 custom PPLane plugin to interact with plot
plugins.connect(fig, PPlaneNLPlugin(points[0],midpoint[0],
g,iters,iter_charac,time, hmax, offsetx,
hugol[0],hugor[0],intcl[0],intcr[0],
hugol2[0],hugor2[0],intcl2[0],intcr2[0],
qlmarker[0],qmmarker[0],qrmarker[0],
shock1[0],shock2[0],rar1[0],rar2[0],
rar1a[0],rar2a[0],rar1b[0],rar2b[0],
rar1c[0],rar2c[0],rar1d[0],rar2d[0],
timedot[0],timeline[0],
q1[0],q2[0]))
return fig
def spladder_viz(options):
"""Main visualization code"""
### parse parameters from options object
options = settings.parse_args(options, identity='viz')
### create plot directory if it does not exist yet
if options.testdir != '-':
dirname = options.testdir
else:
dirname = options.outdir
if not os.path.exists(os.path.join(dirname, 'plots')):
os.mkdir(os.path.join(dirname, 'plots'))
if options.format == 'd3':
try:
import mpld3
from mpld3 import plugins
except ImportError:
sys.stderr.write(
"ERROR: missing package for output format d3. Package mpld3 required"
)
sys.exit(1)
### load gene information
gene_names = get_gene_names(options)
rows = get_plot_len(options)
gs = gridspec.GridSpec(rows, 1)
### set color maps
cmap_cov = plt.get_cmap('jet')
cmap_edg = plt.get_cmap('jet')
### plot log scale?
log_tag = ''
if options.log:
log_tag = '.log'
event_tag = ''
### did we get any labels?
if ',' in options.labels:
options.labels = options.labels.strip(',').split(',')
assert len(options.labels) == len(
options.bam_fnames
), "The number of given labels (%i) needs to match the number of given bam file groups (%i)" % (
len(options.labels), len(options.bam_fnames))
# Collect genes to be plotted - there are three cases possible
# 1) the user provides a gene ID
# 2) all genes containing a significant event of a given type from differential testing are plotted
# 3) all genes that contain any event are plotted
### the user chose a specific gene for plotting
### create pairs of gene ids and an event_id (the latter is None by default)
if options.gene_name is not None:
gids = [[
sp.where(sp.array(gene_names) == options.gene_name)[0][0],
options.event_id
]]
if len(gids) == 0:
sys.stderr.write(
'ERROR: provided gene ID %s could not be found, please check for correctness\n'
% options.gene_name)
sys.exit(1)
### the plotting happens on the results of spladder test
### the user chooses to plot the top k significant events
### this requires the event type to be specified
elif options.test_result > 0:
gene_names = []
for event_type in options.event_types:
### the testing script should generate a setup file for the test
### SETUP is structured as follows:
### [gene_strains, event_strains, dmatrix0, dmatrix1, event_type, options]
labels = options.test_labels.split(':')
options.labels = labels
if options.testdir != '-':
testdir = dirname
else:
testdir = os.path.join(
dirname, 'testing_%s_vs_%s' % (labels[0], labels[1]))
SETUP = pickle.load(
open(
os.path.join(
testdir, 'test_setup_C%i_%s.pickle' %
(options.confidence, event_type)), 'rb'))
### get strains to plot
idx1 = sp.where(sp.in1d(SETUP[0], SETUP[6]['conditionA']))[0]
idx2 = sp.where(sp.in1d(SETUP[0], SETUP[6]['conditionB']))[0]
### load test results
for l, line in enumerate(
open(
os.path.join(
testdir, 'test_results_C%i_%s.tsv' %
(options.confidence, event_type)), 'r')):
if l == 0:
continue
if l > options.test_result:
break
sl = line.strip().split('\t')
gene_names.append([sl[1], sl[0]])
gids = get_gene_ids(options, gene_names)
### no gene specified but result provided - plot all genes with confirmed events
### if an event_id is provided, only the associated gene will be plotted
else:
gids = get_gene_ids(options)
### iterate over genes to plot
for gid in gids:
### gather information about the gene we plot
gene = load_genes(options, idx=[gid[0]])[0]
if options.verbose:
print('plotting information for gene %s' % gene.name)
gene.from_sparse()
### event to plot is specified with the gene id list
if gid[1] is not None:
event_info = [
x[::-1]
for x in re.split(r'[._]', gid[1][::-1], maxsplit=1)[::-1]
]
event_info[1] = int(event_info[1]) - 1
event_info = sp.array(event_info, dtype='str')[sp.newaxis, :]
event_tag = '.%s' % gid[1]
### get all confident events of the current gene
else:
event_info = get_conf_events(options, gid[0])
### go over different plotting options
axes = []
### plot result of testing
if options.test_result > 0:
fig = plt.figure(figsize=(9, 5), dpi=200)
gs = gridspec.GridSpec(2, 1, height_ratios=[4, 1])
_add_ax(fig, axes, gs)
_add_ax(fig, axes, gs)
_plot_event(options,
event_info,
fig,
axes[1],
gs,
None,
padding=100)
start, stop = axes[1].get_xlim()
plot_bam(options,
gene,
options.bam_fnames,
fig,
axes[0],
gs,
None,
cmap_cov,
cmap_edg,
single=False,
sharex=axes[1],
start=int(start),
stop=int(stop))
### plot custom layout
else:
### set defaults
if not options.user:
options.splicegraph = True
options.transcripts = True
if options.format == 'd3':
fig = plt.figure(figsize=(12, 2 * rows), dpi=100)
else:
fig = plt.figure(figsize=(18, 3 * rows), dpi=200)
xlim = None
### plot splicing graph
if options.splicegraph:
_plot_splicegraph(gene, fig, axes, gs)
xlim = axes[-1].get_xlim()
### plot annotated transcripts
if options.transcripts:
sharex = None if len(axes) == 0 else axes[0]
axes.append(fig.add_subplot(gs[len(axes), 0], sharex=sharex))
multiple(gene.exons, ax=axes[-1], x_range=xlim)
axes[-1].set_title('Annotated Transcripts')
### plot coverage information for a set of given samples
if len(options.bam_fnames) > 0:
plot_bam(options, gene, options.bam_fnames, fig, axes, gs,
xlim, cmap_cov, cmap_edg)
### plot all the samples in a single plot
if len(options.bam_fnames) > 1:
plot_bam(options,
gene,
options.bam_fnames,
fig,
axes,
gs,
xlim,
cmap_cov,
cmap_edg,
single=False)
### plot segment counts
if len(options.bam_fnames
) == 0 or False: # add option for segment plots
if options.test_result > 0:
_plot_segments(options, gid, fig, axes, gs, [idx1, idx2])
else:
_plot_segments(options, gid, fig, axes, gs)
### plot structure of a single given event
_plot_event(options, event_info, fig, axes, gs, xlim)
### we only need to adapt the xoom for one axis object - as we share the x
zoom_x = [float(x) for x in options.zoom_x.split(',')]
xlim = axes[0].get_xlim()
xdiff = xlim[1] - xlim[0]
axes[0].set_xlim(
[xlim[0] + (zoom_x[0] * xdiff), xlim[0] + (zoom_x[1] * xdiff)])
for ax in axes:
vax.clean_axis(ax)
plt.tight_layout()
### save plot into file
if options.format == 'd3':
out_fname = os.path.join(
dirname, 'plots', 'gene_overview_C%i_%s%s%s.html' %
(options.confidence, gene.name, event_tag, log_tag))
plugins.clear(fig)
plugins.connect(fig, plugins.Zoom(enabled=True))
mpld3.save_html(fig, open(out_fname, 'w'))
else:
if options.test_result > 0:
out_fname = os.path.join(
dirname, 'plots', 'gene_overview_C%i_%s%s%s.%s' %
(options.confidence, gene.name, event_tag, log_tag,
options.format))
else:
out_fname = os.path.join(
dirname, 'plots', 'gene_overview_C%i_%s%s%s.%s' %
(options.confidence, gene.name, event_tag, log_tag,
options.format))
plt.savefig(out_fname, format=options.format, bbox_inches='tight')
plt.close(fig)
def result():
try:
g.conn = sqlite3.connect('case_data.db')
g.curr = g.conn.cursor()
g.curr.execute("SELECT country_name FROM interval_tb")
country = g.curr.fetchone()[0]
g.curr.execute("SELECT duration FROM interval_tb")
period = g.curr.fetchall()
g.curr.execute("SELECT restriction FROM interval_tb")
restrictions = g.curr.fetchall()
g.curr.execute("SELECT * FROM cases_tb WHERE country_name=?",
(country, ))
country_statistics = g.curr.fetchone()
g.conn.commit()
g.conn.close()
plt.rcParams.update({'font.size': 12})
def differential(initial, time, *args):
# Holds initial conditions for the population
susceptible, exposed, infected, recovered = initial
# System of differential equations
# Represents changes in the susceptible, exposed, infected, and recovered population
dSdt = -(beta * susceptible * infected) / POPULATION_SIZE
dEdt = beta * susceptible * infected / POPULATION_SIZE - SIGMA * exposed
dIdt = SIGMA * exposed - GAMMA * infected
dRdt = GAMMA * infected
return dSdt, dEdt, dIdt, dRdt
POPULATION_SIZE = country_statistics[3]
BETA = 0.956
SIGMA = 1 / 5.2
GAMMA = 1 / 2.3
REPRODUCTION_NUMBER = BETA / GAMMA
period = list(period)
time = [0]
restrictions = list(restrictions)
contact_state = []
for i in range(len(period)):
period[i] = period[i][0]
for i in range(len(restrictions)):
restrictions[i] = restrictions[i][0]
time += list(accumulate(period))
for j in range(len(period)):
if restrictions[j] == "Lockdown":
contact_state.append(0.26)
elif restrictions[j] == "Vacation":
contact_state.append(0.46)
elif restrictions[j] == "School closure":
contact_state.append(0.8)
else:
contact_state.append(1)
susceptible_initial = POPULATION_SIZE - \
country_statistics[2] - country_statistics[1]
exposed_initial = 0
infected_initial = country_statistics[2]
recovered_initial = country_statistics[1]
infected_population = 0
current_maximum = 0
fig, ax = plt.subplots()
for graph in range(len(period)):
beta = BETA * contact_state[graph]
duration = np.linspace(time[graph], time[graph + 1],
period[graph] * 2 + 1)
solution = odeint(differential,
(susceptible_initial, exposed_initial,
infected_initial, recovered_initial),
duration,
args=(beta, SIGMA, GAMMA, POPULATION_SIZE))
susceptible_initial = solution[period[graph] * 2, 0]
exposed_initial = solution[period[graph] * 2, 1]
infected_initial = solution[period[graph] * 2, 2]
recovered_initial = solution[period[graph] * 2, 3]
current_maximum = max(solution[:, 2])
if current_maximum > infected_population:
infected_population = current_maximum
if graph == 0:
ax.plot(duration, solution[:, 0], 'r', label='Susceptible')
ax.plot(duration, solution[:, 1], 'g', label='Exposed')
ax.plot(duration, solution[:, 2], 'b', label='Infectious')
ax.plot(duration, solution[:, 3], 'y', label='Recovered')
else:
ax.plot(duration, solution[:, 0], 'r')
ax.plot(duration, solution[:, 1], 'g')
ax.plot(duration, solution[:, 2], 'b')
ax.plot(duration, solution[:, 3], 'y')
ax.set_xlim(0, time[-1])
ax.set_ylim(0, POPULATION_SIZE + POPULATION_SIZE * 0.01)
ax.legend(loc='best')
ax.set_xlabel('Time (days)')
ax.set_ylabel('Population', rotation='horizontal')
fig.set_size_inches(8, 6)
ax.yaxis.set_label_coords(-0.1, 1.02)
plugins.clear(fig)
plugins.connect(fig, plugins.Reset())
plugins.connect(fig, plugins.BoxZoom(enabled=True))
plugins.connect(fig, plugins.Zoom())
seir_graph = mpld3.fig_to_html(fig)
plt.close()
effective_reproduction_number = REPRODUCTION_NUMBER * \
susceptible_initial/POPULATION_SIZE
effective_reproduction_number = float(
"{:0.3f}".format(effective_reproduction_number))
return {
'graph': seir_graph,
'r0': REPRODUCTION_NUMBER,
'rt': effective_reproduction_number,
'susceptible_state': int(susceptible_initial),
'exposed_state': int(exposed_initial),
'infected_state': int(infected_initial),
'recovered_state': int(recovered_initial),
'max_active_cases': int(infected_population)
}
except:
return jsonify(0)
def spladder_viz(options):
"""Main visualization code"""
### parse parameters from options object
options = settings.parse_args(options, identity='viz')
### create plot directory if it does not exist yet
dirname = options.outdir if options.testdir == '-' else options.testdir
if not os.path.exists(os.path.join(dirname, 'plots')):
os.mkdir(os.path.join(dirname, 'plots'))
if options.format == 'd3':
try:
import mpld3
from mpld3 import plugins
except ImportError:
sys.stderr.write(
"ERROR: missing package for output format d3. Package mpld3 required"
)
sys.exit(1)
### load gene information
all_gene_names = get_gene_names(options.outdir, options.confidence,
options.validate_sg, options.verbose)
### set color maps
cmap_cov = plt.get_cmap('jet')
cmap_edg = plt.get_cmap('jet')
### Data Range
RangeData = namedtuple('RangeData', ['chr', 'start', 'stop'])
plots = []
test_tag = []
### if the --test option was given, populate the options object with additional track fields
### otherwise just create a single plot
if options.test is None:
plots.append(options.data_tracks)
test_tag.append('')
else:
for test_info in options.test:
_parse_test_info(test_info, options.outdir, options.confidence,
plots, test_tag)
for plot_data in plots:
if len(options.data_tracks) > 0:
plot_data.extend(options.data_tracks)
### generate all plots to be completed
for p, plot_data in enumerate(plots):
### get range information
genes, gene_names, gids = [], [], []
events, eids = [], []
coords = []
for range_info in options.range:
### genes
if range_info[0] == 'gene':
_parse_gene_info(range_info[1:], genes, gene_names, gids,
all_gene_names, options.outdir,
options.confidence, options.validate_sg,
options.verbose)
### events
elif range_info[0] == 'event':
_parse_event_info(eids, gids, range_info[1:], events,
options.outdir, options.confidence,
options.verbose)
### coordinate ranges
elif range_info[0] == 'coordinate':
coords.append(
RangeData._make([
range_info[1],
int(range_info[2]),
int(range_info[3])
]))
### check data tracks for additional range information if no --range was given
if len(genes) + len(events) + len(coords) == 0:
for range_info in plot_data:
### splicegraph / transcripts
if range_info[0] in ['splicegraph', 'transcript']:
_parse_gene_info(range_info[1:], genes, gene_names, gids,
all_gene_names, options.outdir,
options.confidence, options.validate_sg,
options.verbose)
### events
elif range_info[0] == 'event':
_parse_event_info(eids, gids, range_info[1:], events,
options.outdir, options.confidence,
options.verbose)
### gene (this is only needed internally for --test mode)
elif range_info[0] == 'gene':
_parse_gene_info(range_info[1:], genes, gene_names, gids,
all_gene_names, options.outdir,
options.confidence, options.validate_sg,
options.verbose)
### check that everything is on the same chromosome
plotchrm = np.unique([_.chr for _ in events + genes + coords])
if plotchrm.shape[0] > 1:
sys.stderr.write(
'ERROR: the provided gene/event/coordinate ranges are on different chromosomes and canot be plotted jointly\n'
)
sys.exit(1)
### identify the plotting range
plotrange, plotrange_orig = _set_plotting_range(genes, events, coords)
### parse all elements to be plotted as data tracks
data_tracks = []
for data_element in plot_data:
track_types = data_element[0].split(',')
for track_type in track_types:
data_tracks.append(dt.DataTrack(track_type, data_element[1:]))
### set up figure and layout
gs = gridspec.GridSpec(len(data_tracks), 1, hspace=0.05)
fig = plt.figure(figsize=(15, 3 * len(data_tracks)), dpi=200)
axes = []
for i, data_track in enumerate(data_tracks):
### plot splicing graph
if data_track.type == 'splicegraph':
ax = _add_ax(axes, fig, gs)
if len(data_track.event_info) == 0:
_genes = genes
_gene_names = gene_names
else:
_genes, _gene_names, _gids = [], [], []
_parse_gene_info(range_info[1:], _genes, _gene_names,
_gids, all_gene_names, options.outdir,
options.confidence, options.validate_sg,
options.verbose)
for gene in _genes:
gene.from_sparse()
plot_graph(gene.splicegraph.vertices,
gene.splicegraph.edges,
ax,
xlim=plotrange,
label=gene.name)
gene.to_sparse()
ax.set_ylabel('splicing graph')
ax.get_yaxis().set_label_coords(1.02, 0.5)
### plot annotated transcripts
if data_track.type == 'transcript':
ax = _add_ax(axes, fig, gs)
if len(data_track.event_info) == 0:
_genes = genes
_gene_names = gene_names
else:
_genes, _gene_names, _gids = [], [], []
_parse_gene_info(range_info[1:], _genes, _gene_names,
_gids, all_gene_names, options.outdir,
options.confidence, options.validate_sg,
options.verbose)
for gene in _genes:
gene.from_sparse()
multiple(gene.exons,
ax=ax,
x_range=plotrange,
labels=gene.transcripts,
grid=True)
gene.to_sparse()
ax.set_ylabel('transcripts')
ax.get_yaxis().set_label_coords(1.02, 0.5)
### plot events
if data_track.type == 'event':
ax = _add_ax(axes, fig, gs)
### no event ids given - plot the one from range
if len(data_track.event_info) == 0:
_events = events
### events are given in the track - plot those instead
else:
_eids, _events = [], []
_parse_event_info(_eids, gids, data_track.event_info,
_events, options.outdir,
options.confidence, options.verbose)
event_list = [[event.exons1, event.exons2]
for event in _events]
labels = ['_'.join([_.event_type, str(_.id)]) for _ in _events]
multiple(event_list,
ax=ax,
x_range=plotrange,
color='green',
padding=None,
grid=True,
labels=labels)
vax.clean_axis(ax, allx=True)
ax.set_ylabel('events')
ax.get_yaxis().set_label_coords(1.02, 0.5)
### plot coverage tracks
if data_track.type == 'coverage':
ax = _add_ax(axes, fig, gs)
min_sample_size = 5 ### TODO make that an option
caxes = []
labels = []
norm = plt.Normalize(0, len(data_track.bam_fnames))
for g, bam_group in enumerate(data_track.bam_fnames):
label = 'group %i' % (g + 1) if len(
data_track.group_labels
) == 0 else data_track.group_labels[g]
caxes.append(
cov_from_bam(plotchrm[0],
plotrange[0],
plotrange[1],
bam_group,
subsample=min_sample_size,
ax=ax,
intron_cnt=True,
log=options.log,
xlim=plotrange,
color_cov=cmap_cov(norm(g)),
color_intron_edge=cmap_edg(norm(g)),
grid=True,
min_intron_cnt=options.mincount,
return_legend_handle=True,
label=label))
labels.append(label)
ax.get_yaxis().set_label_coords(1.02, 0.5)
if len(caxes) > 0:
ax.legend(caxes, labels)
### plot segment counts
if data_track.type == 'segments':
ax = _add_ax(axes, fig, gs)
for g, gid in enumerate(gids):
(segments, edges, edge_idx,
strains) = get_seg_counts(gid, options.outdir,
options.confidence,
options.validate_sg)
seg_sample_idx = None
if len(data_track.strains) > 0:
seg_sample_idx = []
for track_strains in data_track.strains:
seg_sample_idx.append(
np.where(np.in1d(strains, track_strains))[0])
cov_from_segments(genes[g],
segments,
edges,
edge_idx,
ax,
xlim=plotrange,
log=options.log,
grid=True,
order='C',
sample_idx=seg_sample_idx)
ax.get_yaxis().set_label_coords(1.02, 0.5)
ax.set_ylabel('segment counts')
### set x axis ticks
for i, ax in enumerate(axes):
if i == len(axes) - 1:
ax.set_xticks(
np.around(
np.linspace(plotrange_orig[0], plotrange_orig[1], 10)))
ax.set_xlabel('chromosome ' + plotchrm[0])
else:
ax.set_xticks([])
### save plot into file
if options.format == 'd3':
out_fname = os.path.join(
dirname, 'plots', '%s%s.html' % (options.outbase, test_tag[p]))
plugins.clear(fig)
plugins.connect(fig, plugins.Zoom(enabled=True))
mpld3.save_html(fig, open(out_fname, 'w'))
else:
out_fname = os.path.join(
dirname, 'plots',
'%s%s.%s' % (options.outbase, test_tag[p], options.format))
plt.savefig(out_fname, format=options.format, bbox_inches='tight')
plt.close(fig)
def problem_view(request, problem_id):
user = request.user
problem = get_object_or_404(Problem, id=problem_id)
if problem.user_id != user and not user.is_superuser:
raise PermissionDenied
parse_error = False
a = None
b = None
c = None
a_copy = None
b_copy = None
result = None
figure = None
parse_result = parse_problem(problem.problem_text)
print parse_result
if parse_result is not None:
a, b, c, x = parse_result
if a and b and c and x:
result = simple_simplex(copy.deepcopy(a), copy.deepcopy(b), copy.deepcopy(c))
b.pop(0)
a_copy = copy.deepcopy(a)
b_copy = copy.deepcopy(b)
if len(x) is 2:
tmp = [0, 0]
tmp[1] = 1
a.insert(0, copy.deepcopy(tmp))
tmp[0] = 1
tmp[1] = 0
a.insert(0, copy.deepcopy(tmp))
b.insert(0, 0)
b.insert(0, 0)
dots = []
for i in range(len(b)):
dots.append([])
shape_x = []
for i in xrange(0, len(b)):
for j in xrange(i + 1, len(b)):
if a[i][0] == a[j][0] and a[i][1] == a[j][1]:
continue
shape_a = np.array([[a[i][0], a[i][1]], [a[j][0], a[j][1]]])
shape_b = np.array([b[i], b[j]])
res = np.linalg.solve(shape_a, shape_b)
dots[i].append(res)
dots[j].append(res)
# fig, ax = plt.subplots()
fig = plt.figure()
ax = fig.add_subplot(111)
for dot in dots:
ax.plot([float(dot[0][0]), float(dot[1][0])], [float(dot[0][1]), float(dot[1][1])], ls='-', color='green', marker='o', lw=2)
# print([dot[0], dot[1]])
ax.set_xlabel('X axis')
ax.set_ylabel('Y axis')
ax.set_title('Plot of 2D Problem!', size=14)
plugins.clear(fig)
plugins.connect(fig, plugins.Reset(), plugins.Zoom(enabled=True), plugins.BoxZoom())
figure = fig_to_html(fig, d3_url=STATIC_URL + 'js/d3.min.js', mpld3_url=STATIC_URL + 'js/mpld3.v0.2.js', use_http=True)
else:
figure = 'Larger than 2D!'
else:
parse_error = True
return render_to_response('problems/view.html', {
'user': user,
'problem': problem,
'parse_error': parse_error,
'figure': figure,
'a': a_copy,
'b': b_copy,
'c': c,
'result': result,
'view_name': 'Problem - %s' % problem.id,
})
def getHTML(self, params):
# print(params)
global current_gene
data_max = 0
fig = plt.figure(figsize=(9, 7))
gs = gridspec.GridSpec(4, 1, height_ratios=[6, 1, 1, 1])
gene = params["freq"].upper()
range_ext = int(params["range"])
searched = "False"
lines = []
if gene.startswith("CHR"):
temp = gene.split(":")
current_gene = ["Region", temp[0][3:], int(temp[1]), int(temp[2]), "+"]
else:
for item in genes:
if item[0] == gene:
current_gene = item
break
else:
temp_gene = self.search_SGD(gene)
print(temp_gene)
searched = "True"
for item in genes:
if item[0] == temp_gene[0]:
current_gene = item
break
else:
current_gene = temp_gene
print(gene)
# Todo add possibility to search SGD defaulting to SGD coordinates
print(
"*" * 40
+ "\nQuery by user:"******";\nrange extension:"
+ str(range_ext)
+ " ;Checked Boxes:"
+ str(params["check_boxes"])
+ ";SGD used:"
+ searched
+ "\n"
+ "*" * 40
)
# print(params)
splt1 = plt.subplot(gs[0, :])
splt1.set_title(
"{}-Chromosome {}:{}:{}, strand:{}".format(
current_gene[0], current_gene[1], current_gene[2], current_gene[3], current_gene[4]
),
size=20,
)
# splt1.set_xlabel('Distance from TSS (nt)')
splt1.axes.xaxis.set_ticklabels([])
splt1.set_ylabel("Normalized counts per million reads\n ", size=16, labelpad=15)
n = 0
fill_colors = [
"#921B16",
"#D6701C",
"#251F47",
"#68A691",
"#68A691",
"#447604",
"#232621",
"#0BBE30",
"#BC3908",
"#191923",
"#391923",
"#591923",
]
for item in params["check_boxes"]:
if current_gene[4] == "+":
data = data_sets[data_sets_names[int(item)]][current_gene[1]][
current_gene[2] - range_ext : current_gene[3] + range_ext
]
factor = 100.0 / len(data)
data = ndimage.interpolation.zoom(data, factor)
_ = splt1.plot(data, label=data_sets_legend[int(item)], color=fill_colors[n], alpha=0.3, lw=3)
lines.extend(_)
splt1.fill_between(range(0, len(data)), 0, data, alpha=0.2, color=fill_colors[n])
if current_gene[4] == "-":
data = data_sets[data_sets_names[int(item)]][current_gene[1]][
current_gene[2] - range_ext : current_gene[3] + range_ext
]
factor = 100.0 / len(data)
data = ndimage.interpolation.zoom(data[::-1], factor)
print(data)
_ = splt1.plot(data, label=data_sets_legend[int(item)], color=fill_colors[n], alpha=0.3, lw=3)
lines.extend(_)
splt1.fill_between(range(0, len(data)), 0, data, alpha=0.2, color=fill_colors[n])
if np.max(data) > data_max:
data_max = np.max(data)
n += 1
# plot2
if current_gene[4] == "+":
y_values1 = data_sets["genes"][current_gene[1]][
0, current_gene[2] - range_ext : current_gene[3] + range_ext
]
y_values2 = data_sets["genes"][current_gene[1]][
1, current_gene[2] - range_ext : current_gene[3] + range_ext
]
else:
y_values1 = data_sets["genes"][current_gene[1]][
0, current_gene[2] - range_ext : current_gene[3] + range_ext
][::-1]
y_values2 = data_sets["genes"][current_gene[1]][
1, current_gene[2] - range_ext : current_gene[3] + range_ext
][::-1]
# splt2.plot(y_values, 'grey', linewidth=2)
factor = 100.0 / len(y_values1)
splt2 = plt.subplot(gs[1, :])
y_values1 = ndimage.interpolation.zoom(y_values1, factor)
y_values2 = ndimage.interpolation.zoom(y_values2, factor)
x_values = range(0, len(y_values1))
splt2.plot([0, len(y_values1)], [0, 0], "black", linewidth=1.5)
splt2.fill_between(x_values, y_values1 / 4.0, y_values1, color="#FFB30F")
splt2.fill_between(x_values, y_values2 / 4.0, y_values2, color="#FD151B")
splt2.set_ylim(-1.5, 1.5)
splt2.text(1.01, 0.5, "GENES", fontsize=12, transform=splt2.transAxes, verticalalignment="center")
splt2.yaxis.set_tick_params(color="w")
splt2.set_yticks([-0.5, 0.5])
splt2.spines["left"].set_position(("outward", 10))
splt2.set_yticklabels([])
splt2.axes.xaxis.set_ticklabels([])
# plot3
if current_gene[4] == "+":
y_values1 = data_sets["CUTS"][current_gene[1]][0, current_gene[2] - range_ext : current_gene[3] + range_ext]
y_values2 = data_sets["CUTS"][current_gene[1]][1, current_gene[2] - range_ext : current_gene[3] + range_ext]
else:
y_values1 = data_sets["CUTS"][current_gene[1]][
0, current_gene[2] - range_ext : current_gene[3] + range_ext
][::-1]
y_values2 = data_sets["CUTS"][current_gene[1]][
1, current_gene[2] - range_ext : current_gene[3] + range_ext
][::-1]
# splt2.plot(y_values, 'grey', linewidth=2)
factor = 100.0 / len(y_values1)
splt3 = plt.subplot(gs[2, :])
y_values1 = ndimage.interpolation.zoom(y_values1, factor)
y_values2 = ndimage.interpolation.zoom(y_values2, factor)
x_values = range(0, len(y_values1))
splt3.plot([0, len(y_values1)], [0, 0], "black", linewidth=1.5)
splt3.fill_between(x_values, y_values1 / 4.0, y_values1, color="#028090")
splt3.fill_between(x_values, y_values2 / 4.0, y_values2, color="#6EEB83")
splt3.set_ylim(-1.5, 1.5)
splt3.text(1.01, 0.5, "CUTS", fontsize=12, transform=splt3.transAxes, verticalalignment="center")
splt3.yaxis.set_tick_params(color="w")
splt3.set_yticks([-0.5, 0.5])
splt3.spines["left"].set_position(("outward", 10))
splt3.set_yticklabels([])
splt3.axes.xaxis.set_ticklabels([])
# plot4
if current_gene[4] == "+":
y_values1 = data_sets["SUTS"][current_gene[1]][0, current_gene[2] - range_ext : current_gene[3] + range_ext]
y_values2 = data_sets["SUTS"][current_gene[1]][1, current_gene[2] - range_ext : current_gene[3] + range_ext]
else:
y_values1 = data_sets["SUTS"][current_gene[1]][
0, current_gene[2] - range_ext : current_gene[3] + range_ext
][::-1]
y_values2 = data_sets["SUTS"][current_gene[1]][
1, current_gene[2] - range_ext : current_gene[3] + range_ext
][::-1]
# splt2.plot(y_values, 'grey', linewidth=2)
factor = 100.0 / len(y_values1)
splt4 = plt.subplot(gs[3, :])
y_values1 = ndimage.interpolation.zoom(y_values1, factor)
y_values2 = ndimage.interpolation.zoom(y_values2, factor)
x_values = range(0, len(y_values1))
splt4.plot([0, len(y_values1)], [0, 0], "black", linewidth=1.5)
splt4.fill_between(x_values, y_values1 / 4.0, y_values1, color="#028090")
splt4.fill_between(x_values, y_values2 / 4.0, y_values2, color="#6EEB83")
splt4.set_ylim(-1.5, 1.5)
splt4.text(1.01, 0.5, "SUTS", fontsize=12, transform=splt4.transAxes, verticalalignment="center")
splt4.yaxis.set_tick_params(color="w")
splt4.set_yticks([-0.5, 0.5])
splt4.spines["left"].set_position(("outward", 10))
splt4.set_yticklabels([])
splt4.axes.xaxis.set_ticklabels([])
leg = splt1.legend(ncol=3)
leg.set_title("")
for legobj in leg.legendHandles:
legobj.set_linewidth(3.0)
size = (current_gene[3] + range_ext) - (current_gene[2] - range_ext)
splt1.set_ylim(0, np.max(data_max) * 1.3)
# print(str(plt.ylim()[1] - 5))
splt1.text(0.02, 0.02, "Luis Soares", fontsize=6, transform=splt1.transAxes, verticalalignment="top")
leg_line = leg.get_lines()
number_of_samples = len(leg_line)
class HighlightLines(plugins.PluginBase):
"""A plugin to highlight lines on hover"""
JAVASCRIPT = """
mpld3.register_plugin("linehighlight", LineHighlightPlugin);
LineHighlightPlugin.prototype = Object.create(mpld3.Plugin.prototype);
LineHighlightPlugin.prototype.constructor = LineHighlightPlugin;
LineHighlightPlugin.prototype.requiredProps = ["legend_ids","line_ids"];
LineHighlightPlugin.prototype.defaultProps = {alpha_bg:0.3, alpha_fg:1.0}
function LineHighlightPlugin(fig, props){
mpld3.Plugin.call(this, fig, props);
};
LineHighlightPlugin.prototype.draw = function(){
var obj=[]
for(var i=0; i<this.props.legend_ids.length; i++){
obj=obj.concat([mpld3.get_element(this.props.legend_ids[i], this.fig),
mpld3.get_element(this.props.line_ids[i], this.fig)]);
alpha_fg = this.props.alpha_fg;
alpha_bg = this.props.alpha_bg;
}
%s
}
""" % (
create_java(number_of_samples * 2)
)
def __init__(self, legend, lines, samples):
self.lines = lines
self.legend = legend
self.dict_ = {
"type": "linehighlight",
"legend_ids": [utils.get_id(line) for line in legend],
"line_ids": [utils.get_id(line) for line in lines],
"alpha_bg": lines[0].get_alpha(),
"alpha_fg": 1.0,
}
class TopToolbar(plugins.PluginBase):
"""Plugin for moving toolbar to top of figure"""
JAVASCRIPT = """
mpld3.register_plugin("toptoolbar", TopToolbar);
TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);
TopToolbar.prototype.constructor = TopToolbar;
function TopToolbar(fig, props){
mpld3.Plugin.call(this, fig, props);
};
TopToolbar.prototype.draw = function(){
// the toolbar svg doesn't exist
// yet, so first draw it
this.fig.toolbar.draw();
// then change the y position to be
// at the top of the figure
this.fig.toolbar.toolbar.attr("y", 2);
// then remove the draw function,
// so that it is not called again
this.fig.toolbar.draw = function() {}
}
"""
def __init__(self):
self.dict_ = {"type": "toptoolbar"}
plugins.clear(fig)
plugins.connect(
fig, TopToolbar(), HighlightLines(leg_line, lines, number_of_samples), plugins.Reset(), plugins.BoxZoom()
)
file = cStringIO.StringIO()
mpld3.save_html(fig, file)
return file.getvalue()
def render(self, filename="output.html"):
if self.html:
# write matplotlib/d3 plots to html file
import matplotlib.pyplot as plt, mpld3
import matplotlib.axes
from mpld3 import plugins
from jinja2 import Environment, FileSystemLoader
else:
# show plots in separate matplotlib windows
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.axes
figures = list()
for group in self.datasets:
fig, axes = plt.subplots(len(group["dataset"]), sharex=True, sharey=True)
# scale unified scaling figures to same ranges and add some margin
if group["unified_scaling"]:
ymin = 0
ymax = 0
for i in range(len(group["dataset"])):
ymin = np.min((np.min(group["dataset"][i]["data"]), ymin)) * 1.05
ymax = np.max((np.max(group["dataset"][i]["data"]), ymax)) * 1.05
# plot each group of data
for d_i in range(len(group["dataset"])):
d = group["dataset"][d_i]
if not issubclass(type(axes), matplotlib.axes.SubplotBase):
ax = axes[d_i]
else:
ax = axes
axes = [axes]
ax.set_title(d["title"])
if group["unified_scaling"]:
ax.set_ylim([ymin, ymax])
for data_i in range(0, len(d["data"])):
if len(d["data"][data_i].shape) > 1:
# data matrices
for i in range(0, d["data"][data_i].shape[1]):
l = group["labels"][i] if data_i == 0 else ""
if i < 6 and "contains_base" in group and group["contains_base"]:
ls = "--"
else:
ls = "-"
ax.plot(
d["time"],
d["data"][data_i][:, i],
label=l,
color=colors[i],
alpha=1 - (data_i / 2.0),
linestyle=ls,
)
else:
# data vector
ax.plot(
d["time"],
d["data"][data_i],
label=group["labels"][d_i],
color=colors[0],
alpha=1 - (data_i / 2.0),
)
ax.grid(b=True, which="both", color="0.4")
if "y_label" in group:
ax.set_ylabel(group["y_label"])
ax.set_xlabel("Time (s)")
plt.setp([a.get_xticklabels() for a in axes[:-1]], visible=False)
# plt.setp([a.get_yticklabels() for a in axes], fontsize=8)
if self.html:
# TODO: show legend properly (see mpld3 bug #274)
handles, labels = ax.get_legend_handles_labels()
# leg = fig.legend(handles, labels, loc='upper right', fancybox=True, fontsize=10, title='')
leg = axes[0].legend(
handles, labels, loc="upper right", fancybox=True, fontsize=10, title="", prop={"size": 7}
)
else:
if idf.opt["plotPerJoint"]:
font_size = 30
else:
font_size = 12
matplotlib.rcParams.update({"font.size": font_size})
matplotlib.rcParams.update({"axes.labelsize": font_size - 2})
matplotlib.rcParams.update({"axes.linewidth": font_size / 15.0})
matplotlib.rcParams.update({"axes.titlesize": font_size})
matplotlib.rcParams.update({"legend.fontsize": font_size})
matplotlib.rcParams.update({"xtick.labelsize": font_size - 2})
matplotlib.rcParams.update({"ytick.labelsize": font_size - 2})
matplotlib.rcParams.update({"lines.linewidth": font_size / 15.0})
matplotlib.rcParams.update({"patch.linewidth": font_size / 15.0})
matplotlib.rcParams.update({"grid.linewidth": font_size / 20.0})
handles, labels = ax.get_legend_handles_labels()
leg = plt.figlegend(
handles,
labels,
loc="upper right",
fancybox=True,
fontsize=10,
title="",
prop={"size": font_size - 4},
)
leg.draggable()
fig.subplots_adjust(hspace=2)
fig.set_tight_layout(True)
if self.html:
plugins.clear(fig)
plugins.connect(
fig,
plugins.Reset(),
plugins.BoxZoom(),
plugins.Zoom(enabled=False),
plugins.MousePosition(fontsize=14),
)
figures.append(mpld3.fig_to_html(fig))
else:
plt.show()
if self.html:
path = os.path.dirname(os.path.abspath(__file__))
template_environment = Environment(
autoescape=False, loader=FileSystemLoader(os.path.join(path, "../output")), trim_blocks=False
)
context = {"figures": figures}
outfile = os.path.join(path, "..", "output", "output.html")
with open(outfile, "w") as f:
html = template_environment.get_template("templates/index.html").render(context)
f.write(html)
print("Saved output at file://{}".format(outfile))
def linear_phase_plane(ql=np.array([-2.0, 2.0]),qr=np.array([0.0, 3.0]),
r1=None, r2=None, lam1=None, lam2=None,
q1min=-5, q1max=5, q2min =-5, q2max=5, domain=5, time=1.0,
title1=None, title2=None):
# Create figure
# Create a figure
fig, ax = plt.subplots(1,3, figsize=(13.5, 5.0))
fig.subplots_adjust(left=0.05, right=0.9, bottom=0.2, top=0.9,
hspace=0.3, wspace=0.15)
#Create subfigure ax1 for phaseplane
#ax[0] = fig.add_subplot(2,2,1)
# Calculate dq from ql and qr
dq = np.array([qr[0]-ql[0], qr[1]-ql[1]])
# If eigenvectors not specified, default to Acoustics
rho0 = 1.0
zz = 2.0
if r1 is None:
r1 = np.array([-zz, 1])
if r2 is None:
r2 = np.array([zz, 1])
if lam1 is None:
lam1 = -zz/rho0
if lam2 is None:
lam2 = zz/rho0
# Choose left and right eigenvectors
if (lam1 < lam2):
laml = lam1; lamr = lam2
rl = r1; rr = r2
else:
laml = lam2; lamr = lam1
rl = r2; rr = r1
# Assign titles to solution plots
if title1 is None:
title1 = "q1"
if title2 is None:
title2 = "q2"
#Plot eigenlines across ql and qr
eps = 0.00000000001 # To avoid bugs in D3
ml = rl[1]/rl[0]
mr = rr[1]/rr[0] + eps
linesla = ax[0].plot([q1min,q1max],[ml*(q1min - ql[0]) + ql[1],ml*(q1max - ql[0]) + ql[1]], '-k')
lineslb = ax[0].plot([q1min-eps,q1max+eps],[mr*(q1min - ql[0]) + ql[1],mr*(q1max - ql[0]) + ql[1]], '-k')
linesra = ax[0].plot([q1min-2*eps,q1max+2*eps],[mr*(q1min - qr[0]) + qr[1],mr*(q1max - qr[0]) + qr[1]], '-k')
linesrb = ax[0].plot([q1min-3*eps,q1max+3*eps],[ml*(q1min - qr[0]) + qr[1],ml*(q1max - qr[0]) + qr[1]], '-k')
# Plot ql and qr
points = ax[0].plot([ql[0],qr[0]], [ql[1], qr[1]], 'ok', alpha=0.7, markersize=10, markeredgewidth=1)
data = ["q_l", "q_r"]
offset = 0.4*0.5*(q1max-q1min)/5.0
qlmarker = ax[0].plot(ql[0] + offset, ql[1] - offset, 'ok', marker=(r"$ q_l $"), markersize=15)
qrmarker = ax[0].plot(qr[0] + offset, qr[1] - offset, 'ok', marker=(r"$ q_r $"), markersize=15)
#Plot midpoint
det = rl[0]*rr[1] - rr[0]*rl[1]
alL = (rr[1]*dq[0] - rr[0]*dq[1])/det
qm = ql + alL*rl
midpoint = ax[0].plot(qm[0],qm[1],'ok', alpha=0.9, markersize=8, markeredgewidth=1)
qmmarker = ax[0].plot(qm[0]+offset,qm[1]-0.7*offset, 'k',marker=(r"$ q_m $"),markersize=20)
# Set axis 1 properties
ax[0].set_title("Phase Plane", fontsize=18)
ax[0].axis([q1min,q1max,q2min,q2max])
ax[0].grid(alpha=0.1,color='k', linestyle='--')
ax[0].set_xlabel(title1)
ax[0].set_ylabel(title2)
# Remove defult mpld3 plugins
plugins.clear(fig)
# Create solutionl line with six points where solution should be
ctleft = laml*time
ctright = lamr*time
domain = max(domain,abs(ctleft)+1,abs(ctright)+1) # Readjust xdomain if necessarry
xsol = np.array([-domain,ctleft,ctleft,ctright,ctright,domain])
qsol1 = 1*xsol
qsol1[0:2] = ql[0]
qsol1[2:4] = qm[0]
qsol1[4:6] = qr[0]
# Set axis 2 properties
ax[1].set_title(title1, fontsize=18)
ax[1].axis([-domain,domain,q1min,q1max])
ax[1].grid(alpha=0.1,color='k', linestyle='--')
ax[1].set_xlabel('x')
#ax[1].set_ylabel(title1)
# Plot solution
qone = ax[1].plot(xsol, qsol1, '-k', linewidth = 4, alpha = 1.0)
#Create subfigure ax2 for solution plane
#ax[2] = fig.add_subplot(2,2,2)
# Create solutionl line with six points
xsol2 = np.array([-domain,ctleft,ctleft,ctright,ctright,domain])
qsol2 = 1*xsol2
qsol2[0:2] = ql[1]
qsol2[2:4] = qm[1]
qsol2[4:6] = qr[1]
# Set axis 2 properties
ax[2].set_title(title2, fontsize=18)
ax[2].axis([-domain,domain,q2min,q2max])
ax[2].grid(alpha=0.1,color='k', linestyle='--')
ax[2].set_xlabel('x')
#ax[2].set_ylabel(title2)
# Plot solution
qtwo = ax[2].plot(xsol2, qsol2, '-k', linewidth = 4, alpha = 1.0)
# Call mpld3 custom PPLane plugin to interact with plot
plugins.connect(fig, PPlanePlugin(points[0],midpoint[0],
linesla[0],lineslb[0],linesra[0],linesrb[0],
qlmarker[0],qmmarker[0],qrmarker[0],qone[0],qtwo[0],
rl[0],rl[1],rr[0],rr[1]))
return fig
def spladder_viz():
"""Main visualization code"""
### parse command line parameters
options = parse_options(sys.argv)
### create plot directory if it does not exist yet
if not os.path.exists(os.path.join(options.outdir, 'plots')):
os.mkdir(os.path.join(options.outdir, 'plots'))
if options.format == 'd3':
try:
import mpld3
from mpld3 import plugins
from modules.d3test import ClickInfo
except ImportError:
sys.stderr.write("ERROR: missing package for format d3. Package mpld3 required")
sys.exit(1)
### load gene information
genes = load_genes(options)
rows = get_plot_len(options)
gs = gridspec.GridSpec(rows, 1)
### get coloring
cmap_cov = plt.get_cmap('jet')
cmap_edg = plt.get_cmap('jet')
### plot log scale?
log_tag = ''
if options.log:
log_tag = '.log'
event_tag = ''
### did we get any labels?
if options.labels != '-':
options.labels = options.labels.strip(',').split(',')
assert len(options.labels) == len(options.bams.strip(':').split(':')), "The number of given labels (%i) needs to match the number of given bam file groups (%i)" % (len(options.labels), len(options.bams.strip(':').split(':')))
### the user specified a gene to plot
if options.gene_name is not None:
if isinstance(genes[0].name, sp.ndarray):
gids = sp.where(sp.array([x.name[0] for x in genes]) == options.gene_name)[0]
if gids.shape[0] == 0:
gids = sp.where(sp.array([x.name[0].startswith(options.gene_name) for x in genes]))[0]
else:
gids = sp.where(sp.array([x.name for x in genes]) == options.gene_name)[0]
if gids.shape[0] == 0:
gids = sp.where(sp.array([x.name.startswith(options.gene_name) for x in genes]))[0]
if gids.shape[0] == 0:
sys.stderr.write('ERROR: provided gene ID %s could not be found, please check for correctness\n' % options.gene_name)
sys.exit(1)
### no gene specified but result provided - plot all genes with confirmed events
else:
gids = get_gene_ids(options)
### iterate over genes to plot
for gid in gids:
if options.format == 'd3':
fig = plt.figure(figsize = (12, 2*rows), dpi=100)
else:
fig = plt.figure(figsize = (18, 3*rows), dpi=200)
axes = []
### gather information about the gene we plot
gene = get_gene(genes[gid])
if options.verbose:
print 'plotting information for gene %s' % gene.name
### plot splicing graph
axes.append(fig.add_subplot(gs[len(axes), 0]))
plot_graph(gene.splicegraph.vertices, gene.splicegraph.edges, axes[-1])
xlim = axes[-1].get_xlim()
axes[-1].set_title('Splicing graph for %s' % gene.name)
### plot annotated transcripts
if options.transcripts:
axes.append(fig.add_subplot(gs[len(axes), 0], sharex=axes[0]))
multiple(gene.exons, ax=axes[-1], x_range=xlim)
axes[-1].set_title('Annotated Transcripts')
### plot coverage information for a set of samples
if options.bams != '-':
samples = options.bams.strip(':').split(':')
plot_bam(options, gene, samples, fig, axes, gs, xlim, cmap_cov, cmap_edg)
### plot all the samples in a single plot
if len(samples) > 1:
plot_bam(options, gene, samples, fig, axes, gs, xlim, cmap_cov, cmap_edg, single=False)
### plot segment counts
(segments, edges, edge_idx) = get_seg_counts(options, gid)
axes.append(fig.add_subplot(gs[len(axes), 0], sharex=axes[0]))
if identity() == 'matlab':
cov_from_segments(gene, segments, edges, edge_idx, axes[-1], xlim=xlim, log=options.log, grid=True, order='F')
else:
cov_from_segments(gene, segments, edges, edge_idx, axes[-1], xlim=xlim, log=options.log, grid=True, order='C')
axes[-1].set_title('Segment counts')
### plot structure of a single given event
#if options.event_id is not None:
axes.append(fig.add_subplot(gs[len(axes), 0], sharex=axes[0]))
### user defined event to plot
if options.event_id is not None:
event_info = [x[::-1] for x in re.split(r'[._]', options.event_id[::-1], maxsplit=1)[::-1]]
event_info[1] = int(event_info[1]) - 1
event_info = sp.array(event_info, dtype='str')[sp.newaxis, :]
event_tag = '.%s' % options.event_id
### get all significant events of the current gene
else:
event_info = get_conf_events(options, gid)
plot_event(options, event_info, axes[-1], xlim)
plt.tight_layout()
### save plot into file
if options.format == 'd3':
out_fname = os.path.join(options.outdir, 'plots', 'gene_overview_%s%s%s.html' % (gene.name, event_tag, log_tag))
plugins.clear(fig)
plugins.connect(fig, plugins.Zoom(enabled=True), ClickInfo(sp.ones((10000,), dtype='int')))
mpld3.save_html(fig, open(out_fname, 'w'))
else:
out_fname = os.path.join(options.outdir, 'plots', 'gene_overview_%s%s%s.%s' % (gene.name, event_tag, log_tag, options.format))
plt.savefig(out_fname, format=options.format, bbox_inches='tight')
plt.close(fig)
def plotData(NQuery, input_table, FigureStrBase, html_dir=None, png_dir=None,
xvariable='SurfaceDensity', yvariable='VelocityDispersion',
zvariable='Radius',
xMin=None, xMax=None, yMin=None, yMax=None, zMin=None, zMax=None,
interactive=False, show_log=True, min_marker_width=3,
max_marker_width=0.05):
"""
This is where documentation needs to be added
Parameters
----------
NQuery
FigureStrBase : str
The start of the output filename, e.g. for "my_file.png" it would be
my_file
xMin
xMax
yMin
yMax
zMin
zMax
min_marker_width : int or float, optional
Sets the pixel width of the smallest marker to be plotted. If <1,
it is interpreted to be a fraction of the total pixels along the
shortest axis.
max_marker_width : int or float, optional
Sets the pixel width of the smallest marker to be plotted. If <1,
it is interpreted to be a fraction of the total pixels along the
shortest axis.
"""
if len(input_table) == 0:
raise ValueError("The input table is empty.")
figure = matplotlib.figure.Figure()
if interactive:
from matplotlib import pyplot
from matplotlib import _pylab_helpers
backend = getattr(matplotlib.backends, 'backend_{0}'.format(matplotlib.rcParams['backend']).lower())
canvas = backend.FigureCanvas(figure)
figmanager = backend.FigureManager(canvas, 1)
figmanager.canvas.figure.number = 1
_pylab_helpers.Gcf.set_active(figmanager)
else:
figure = matplotlib.figure.Figure()
canvas = FigureCanvasAgg(figure)
ax = figure.gca()
d = input_table
Author = d['Names']
Run = d['IDs']
x_ax = d[xvariable]
y_ax = d[yvariable]
z_ax = d[zvariable]
# Check if limits are given
if xMin is None:
xMin = x_ax.min()
if xMax is None:
xMax = x_ax.max()
if yMin is None:
yMin = y_ax.min()
if yMax is None:
yMax = y_ax.max()
if zMin is None:
zMin = z_ax.min()
if zMax is None:
zMax = z_ax.max()
if d['IsSimulated'].dtype == 'bool':
IsSim = d['IsSimulated']
else:
IsSim = d['IsSimulated'] == 'True'
if show_log:
if not label_dict_html[xvariable].startswith('log'):
label_dict_html[xvariable] = 'log ' + label_dict_html[xvariable]
label_dict_html[yvariable] = 'log ' + label_dict_html[yvariable]
if not label_dict_png[xvariable].startswith('log'):
label_dict_png[xvariable] = 'log ' + label_dict_png[xvariable]
label_dict_png[yvariable] = 'log ' + label_dict_png[yvariable]
# Select points within the limits
Use_x_ax = (x_ax > xMin) & (x_ax < xMax)
Use_y_ax = (y_ax > yMin) & (y_ax < yMax)
Use_z_ax = (z_ax > zMin) & (z_ax < zMax)
# intersects the three subsets defined above
Use = Use_x_ax & Use_y_ax & Use_z_ax
nptstoplot = np.count_nonzero(Use)
if nptstoplot == 0:
log.debug("Use: {0}".format(Use))
log.debug("Use_x_ax: {0}".format(Use_x_ax))
log.debug("xmin: {0} xmax: {1}".format(xMin, xMax))
log.debug("x_ax: {0}".format(x_ax))
log.debug("Use_y_ax: {0}".format(Use_y_ax))
log.debug("ymin: {0} ymax: {1}".format(yMin, yMax))
log.debug("y_ax: {0}".format(y_ax))
log.debug("Use_z_ax: {0}".format(Use_z_ax))
log.debug("zmin: {0} zmax: {1}".format(zMin, zMax))
log.debug("z_ax: {0}".format(z_ax))
return None,None
else:
log.debug("Found {0} points to plot".format(nptstoplot))
UniqueAuthor = list(set(Author[Use]))
NUniqueAuthor = len(UniqueAuthor)
colors = list(matplotlib.cm.jet(np.linspace(0, 1, NUniqueAuthor)))
random.seed(12)
random.shuffle(colors)
# NOTE this does NOT work with mpld3
# ax.loglog()
# Set marker sizes based on a minimum and maximum pixel size, then scale
# the rest between.
bbox = \
ax.get_window_extent().transformed(figure.dpi_scale_trans.inverted())
min_axis_size = min(bbox.width, bbox.height) * figure.dpi
if max_marker_width < 1:
max_marker_width *= min_axis_size
if min_marker_width < 1:
min_marker_width *= min_axis_size
marker_conversion = max_marker_width / \
(np.log10(z_ax[Use].max())-np.log10(z_ax[Use].min()))
marker_widths = (marker_conversion *
(np.log10(np.array(z_ax))-np.log10(z_ax[Use].min())) +
min_marker_width)
marker_sizes = marker_widths**2
scatters = []
markers = ['o', 's']
for iAu, color in zip(UniqueAuthor, colors):
ObsPlot = ((Author == iAu) & (~IsSim)) & Use
SimPlot = ((Author == iAu) & (IsSim)) & Use
if show_log:
plot_x = np.log10(x_ax)
plot_y = np.log10(y_ax)
if any(ObsPlot):
# Change to logs on next commit
scatter = \
ax.scatter(plot_x[ObsPlot], plot_y[ObsPlot], marker=markers[0],
s=marker_sizes[ObsPlot],
color=color, alpha=0.5, edgecolors='k',
label=iAu)
scatters.append(scatter)
labels = []
for row in d[ObsPlot]:
colnames = ['<div>{title}</div>'.format(title=col)
for col in row.colnames]
values = ['<div>{title}</div>'.format(title=str(val))
for val in row]
label = ""
for col, val in zip(colnames, values):
label += col+" "+val+" \n "
labels.append(label)
tooltip = plugins.PointHTMLTooltip(scatter, labels,
voffset=10, hoffset=10)
plugins.connect(figure, tooltip)
if any(SimPlot):
# Change to logs on next commit
scatter = \
ax.scatter(plot_x[SimPlot], plot_y[SimPlot], marker=markers[1],
s=marker_sizes[SimPlot],
color=color, alpha=0.5, edgecolors='k',
label=iAu)
scatters.append(scatter)
labels = []
for row in d[SimPlot]:
colnames = ['<div>{title}</div>'.format(title=col)
for col in row.colnames]
values = ['<div>{title}</div>'.format(title=str(val))
for val in row]
label = ""
for col, val in zip(colnames, values):
label += col+" "+val+" \n "
labels.append(label)
tooltip = plugins.PointHTMLTooltip(scatter, labels,
voffset=10, hoffset=10, css=css)
plugins.connect(figure, tooltip)
ax.set_xlabel(label_dict_html[xvariable], fontsize=16)
ax.set_ylabel(label_dict_html[yvariable], fontsize=16)
# Set plot limits. Needed for conversion of pixel units to plot units.
# Pad the maximum marker width on.
inv = ax.transData.inverted()
pad_x, pad_y = inv.transform((marker_widths.max(), marker_widths.max())) - \
inv.transform((0.0, 0.0))
if show_log:
ax.set_xlim(np.log10(xMin.value)-pad_x, np.log10(xMax.value)+pad_x)
ax.set_ylim(np.log10(yMin.value)-pad_y, np.log10(yMax.value)+pad_y)
else:
ax.set_xlim(xMin.value - pad_x, xMax.value + pad_x)
ax.set_ylim(yMin.value - pad_y, yMax.value + pad_y)
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
# ax.legend(UniqueAuthor, loc='center left', bbox_to_anchor=(1.0, 0.5),
# prop={'size':12}, markerscale = .7, scatterpoints = 1)
if hasattr(mpld3.plugins, 'InteractiveLegendPlugin'):
plugins.connect(figure,
plugins.InteractiveLegendPlugin(scatters,
UniqueAuthor,
alpha_unsel=0.0,
alpha_over=0.5))
# Adding fake points to show the size
# Try floor and ceil. Pick the one closest to the max/min.
max_z = round_to_pow_10(z_ax[Use].max())
min_z = round_to_pow_10(z_ax[Use].min())
mid_z = round_to_pow_10((max_z + min_z) / 2., log=False)
if mid_z == max_z:
fake_z_marker_width = np.array([max_z])
elif mid_z == max_z or mid_z == min_z:
fake_z_marker_width = np.array([max_z, min_z])
else:
fake_z_marker_width = np.array([max_z, mid_z, min_z])
fake_marker_sizes = (marker_conversion *
(fake_z_marker_width-np.log10(z_ax[Use].min())) +
min_marker_width)**2
# Set the axis fraction to plot the points at. Adjust if the largest
# will overlap with the next.
sep_ax_frac = 0.05
if np.sqrt(fake_marker_sizes[0])/float(min_axis_size) > 0.05:
sep_ax_frac = np.sqrt(fake_marker_sizes[0])/float(min_axis_size) \
+ 0.005
xfake = [0.1] * fake_z_marker_width.shape[0]
yfake = [0.95 - sep_ax_frac*x for x in range(fake_z_marker_width.shape[0])]
# xfake = [xax_limits[0] + xax_limits[0]*2.,
# xax_limits[0] + xax_limits[0]*2.,
# xax_limits[0] + xax_limits[0]*2.]
# yfake = [yax_limits[1] - yax_limits[1]*0.01,
# yax_limits[1] - yax_limits[1]*0.3,
# yax_limits[1] - yax_limits[1]*0.6]
ax.scatter(np.array(xfake), np.array(yfake), marker='+',
s=fake_marker_sizes,
transform=ax.transAxes,
facecolors='g')
for xf, yf, rad in zip(xfake, yfake, fake_z_marker_width):
ax.text(xf + 0.05, yf-0.01, str(10**rad) + ' ' + str(zMin.unit),
transform=ax.transAxes)
# Saving the plots
if html_dir is None:
html_dir = ""
if png_dir is None:
png_dir = ""
html_file = os.path.join(html_dir, FigureStrBase+NQuery+'.html')
png_file = os.path.join(png_dir, FigureStrBase+NQuery+".png")
html = mpld3.fig_to_html(figure)
with open(html_file, 'w') as f:
f.write(html)
if interactive:
# from matplotlib import pyplot as plt
# plt.ion()
# plt.show()
mpld3.show()
# Clear out the plugins
plugins.clear(figure)
# Use latex labels for the mpl outputted plot
ax.set_xlabel(label_dict_png[xvariable], fontsize=16)
ax.set_ylabel(label_dict_png[yvariable], fontsize=16)
# Shrink current axis by 20%
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
# Put a legend to the right of the current axis
legend = ax.legend(loc='center left', bbox_to_anchor=(1, 0.5),
handlelength=4)
legend.draw_frame(False)
figure.savefig(png_file, bbox_inches='tight', dpi=150)
# figure.savefig(FigureStrBase+NQuery+'.pdf',bbox_inches='tight',dpi=150)
return html_file, png_file
def render(self, idf, filename='output.html'):
progress_inst = helpers.Progress(idf.opt)
self.progress = progress_inst.progress
if idf.opt['outputFilename']:
filename = idf.opt['outputFilename']
if idf.opt['outputAs'] == 'html':
# write matplotlib/d3 plots to html file
import matplotlib
import matplotlib.pyplot as plt, mpld3
import matplotlib.axes
from mpld3 import plugins
from jinja2 import Environment, FileSystemLoader
elif idf.opt['outputAs'] in ['pdf', 'interactive', 'tikz']:
# show plots in separate matplotlib windows
import matplotlib
if idf.opt['outputAs'] == 'pdf':
from matplotlib.backends.backend_pdf import PdfPages
pp = PdfPages(filename)
import matplotlib.pyplot as plt
import matplotlib.axes
else:
print("No proper output method given. Not plotting.")
return
font_size = 10
if idf.opt['outputAs'] in ['pdf', 'tikz']:
if idf.opt['plotPerJoint']:
font_size = 30
else:
font_size = 12
matplotlib.rcParams.update({'font.size': font_size})
matplotlib.rcParams.update({'axes.labelsize': font_size - 5})
matplotlib.rcParams.update({'axes.linewidth': font_size / 15.})
matplotlib.rcParams.update({'axes.titlesize': font_size - 2})
matplotlib.rcParams.update({'legend.fontsize': font_size - 2})
matplotlib.rcParams.update({'xtick.labelsize': font_size - 5})
matplotlib.rcParams.update({'ytick.labelsize': font_size - 5})
matplotlib.rcParams.update({'lines.linewidth': font_size / 15.})
matplotlib.rcParams.update({'patch.linewidth': font_size / 15.})
matplotlib.rcParams.update({'grid.linewidth': font_size / 20.})
# skip some samples so graphs don't get too large/detailed TODO: change skip so that some
# maximum number of points is plotted (determined by screen etc.)
skip = 5
#create figures and plots
figures = list()
for ds in self.progress(range(len(self.datasets))):
group = self.datasets[ds]
fig, axes = plt.subplots(len(group['dataset']),
sharex=True,
sharey=True)
# scale unified scaling figures to same ranges and add some margin
if group['unified_scaling']:
ymin = 0
ymax = 0
for i in range(len(group['dataset'])):
ymin = np.min(
(np.min(group['dataset'][i]['data']), ymin)) * 1.05
ymax = np.max(
(np.max(group['dataset'][i]['data']), ymax)) * 1.05
#plot each group of data
for d_i in range(len(group['dataset'])):
d = group['dataset'][d_i]
if not issubclass(type(axes), matplotlib.axes.SubplotBase):
ax = axes[d_i]
else:
ax = axes
axes = [axes]
if idf.opt['outputAs'] != 'tikz':
ax.set_title(d['title'])
if group['unified_scaling']:
ax.set_ylim([ymin, ymax])
for data_i in range(0, len(d['data'])):
if len(d['data'][data_i].shape) > 1:
#data matrices
for i in range(0, d['data'][data_i].shape[1]):
l = group['labels'][i] if data_i == 0 else ''
if i < 6 and 'contains_base' in group and group[
'contains_base']:
ls = 'dashed'
else:
ls = '-'
dashes = () # type: Tuple
if idf.opt['plotErrors']:
if idf.opt['plotPrioriTorques']:
n = 3
else:
n = 2
if i == n:
ls = 'dashed'
dashes = (3, 0.5)
ax.plot(d['time'][::skip],
d['data'][data_i][::skip, i],
label=l,
color=colors[i],
alpha=1 - (data_i / 2.0),
linestyle=ls,
dashes=dashes)
else:
#data vector
ax.plot(d['time'][::skip],
d['data'][data_i][::skip],
label=group['labels'][d_i],
color=colors[0],
alpha=1 - (data_i / 2.0))
ax.grid(which='both', linestyle="dotted", alpha=0.8)
if 'y_label' in group:
ax.set_ylabel(group['y_label'])
if idf.opt['outputAs'] != 'tikz':
ax.set_xlabel("Time (s)")
plt.setp([a.get_xticklabels() for a in axes[:-1]], visible=False)
#plt.setp([a.get_yticklabels() for a in axes], fontsize=8)
if idf.opt['plotLegend']:
handles, labels = ax.get_legend_handles_labels()
if idf.opt['outputAs'] == 'html':
#TODO: show legend properly (see mpld3 bug #274)
#leg = fig.legend(handles, labels, loc='upper right', fancybox=True, fontsize=10, title='')
leg = axes[0].legend(handles,
labels,
loc='upper right',
fancybox=True,
fontsize=10,
title='',
prop={'size': 8})
else:
leg = plt.figlegend(handles,
labels,
loc='upper right',
fancybox=True,
fontsize=font_size,
title='',
prop={'size': font_size - 3})
leg.draggable()
fig.subplots_adjust(hspace=2)
fig.set_tight_layout(True)
if idf.opt['outputAs'] == 'html':
plugins.clear(fig)
plugins.connect(fig, plugins.Reset(), plugins.BoxZoom(),
plugins.Zoom(enabled=False),
plugins.MousePosition(fontsize=14, fmt=".5g"))
figures.append(mpld3.fig_to_html(fig))
elif idf.opt['outputAs'] == 'interactive':
plt.show(block=False)
elif idf.opt['outputAs'] == 'pdf':
pp.savefig(plt.gcf())
elif idf.opt['outputAs'] == 'tikz':
from matplotlib2tikz import save as tikz_save
tikz_save('{}_{}_{}.tex'.format(
filename, group['dataset'][0]['title'].replace('_', '-'),
ds // idf.model.num_dofs),
figureheight='\\figureheight',
figurewidth='\\figurewidth',
show_info=False)
if idf.opt['outputAs'] == 'html':
path = os.path.dirname(os.path.abspath(__file__))
template_environment = Environment(
autoescape=False,
loader=FileSystemLoader(os.path.join(path, '../output')),
trim_blocks=False)
context = {'figures': figures, 'text': self.text}
outfile = os.path.join(path, '..', 'output', filename)
import codecs
with codecs.open(outfile, 'w', 'utf-8') as f:
html = template_environment.get_template(
"templates/index.html").render(context)
f.write(html)
print("Saved output at file://{}".format(outfile))
elif idf.opt['outputAs'] == 'interactive':
#keep non-blocking plot windows open
plt.show()
elif idf.opt['outputAs'] == 'pdf':
pp.close()
