def compare_sum_barplot(locus_table, interval_table, intervals, loci, names,
rows):
frame = get_r_data_by_top(locus_table, interval_table, intervals, names,
rows)
#pdb.set_trace()
frame2 = robjects.r('''agg_data <- aggregate(pi ~ interval + db, data = data, sum)''')
if len(intervals) > 1:
sort_string = '''agg_data$interval <- factor(agg_data$interval,{})'''.format(order_intervals(frame2[0]))
robjects.r(sort_string)
gg_frame = ggplot2.ggplot(robjects.r('''agg_data'''))
plot = gg_frame + \
ggplot2.aes_string(
x = 'interval',
y = 'pi',
fill='factor(db)'
) + \
ggplot2.geom_bar(**{
'position':'dodge',
'colour':'#767676',
'alpha':0.6
}
) + \
ggplot2.scale_y_continuous('net phylogenetic informativeness') + \
ggplot2.scale_x_discrete('interval (years ago)') + \
ggplot2.scale_fill_brewer("database", palette="Blues")
return plot
def plot_crawldb_status(self, data, row_filter, img_file, ratio=1.0):
if row_filter:
data = data[data['type'].isin(row_filter)]
categories = []
for value in row_filter:
if re.search('^crawldb:status:db_', value):
replacement = re.sub('^crawldb:status:db_', '', value)
categories.append(replacement)
data.replace(to_replace=value, value=replacement, inplace=True)
data['type'] = pandas.Categorical(data['type'],
ordered=True,
categories=categories.reverse())
data['size'] = data['size'].astype(float)
ratio = 0.1 + len(data['crawl'].unique()) * .03
print(data)
p = ggplot2.ggplot(data) \
+ ggplot2.aes_string(x='crawl', y='size', fill='type') \
+ ggplot2.geom_bar(stat='identity', position='stack', width=.9) \
+ ggplot2.coord_flip() \
+ ggplot2.scale_fill_brewer(palette='Pastel1', type='sequential',
guide=ggplot2.guide_legend(reverse=False)) \
+ GGPLOT2_THEME \
+ ggplot2.theme(**{'legend.position': 'bottom',
'aspect.ratio': ratio}) \
+ ggplot2.labs(title='CrawlDb Size and Status Counts\n(before crawling)',
x='', y='', fill='')
img_path = os.path.join(PLOTDIR, img_file)
p.save(img_path, height=int(7 * ratio), width=7)
return p
def plot_fetch_status(self, data, row_filter, img_file, ratio=1.0):
if row_filter:
data = data[data['type'].isin(row_filter)]
data = data[['crawl', 'percentage', 'type']]
categories = []
for value in row_filter:
if re.search('^fetcher:(?:aggr:)?', value):
replacement = re.sub('^fetcher:(?:aggr:)?', '', value)
categories.append(replacement)
data.replace(to_replace=value, value=replacement, inplace=True)
data['type'] = pandas.Categorical(data['type'],
ordered=True,
categories=categories.reverse())
ratio = 0.1 + len(data['crawl'].unique()) * .03
# print(data)
p = ggplot2.ggplot(data) \
+ ggplot2.aes_string(x='crawl', y='percentage', fill='type') \
+ ggplot2.geom_bar(stat='identity', position='stack', width=.9) \
+ ggplot2.coord_flip() \
+ ggplot2.scale_fill_brewer(palette='RdYlGn', type='sequential',
guide=ggplot2.guide_legend(reverse=True)) \
+ GGPLOT2_THEME \
+ ggplot2.theme(**{'legend.position': 'bottom',
'aspect.ratio': ratio}) \
+ ggplot2.labs(title='Percentage of Fetch Status',
x='', y='', fill='')
img_path = os.path.join(PLOTDIR, img_file)
p.save(img_path, height=int(7 * ratio), width=7)
return p
def plot(data, filename, title, ggplotter, xid="N", yid="RunTime", factorid="Step"):
df = make_dataframe(data, xid, yid, factorid)
grdevices.pdf(file=filename, width=10, height=6)
gp = ggplot2.ggplot(df)
pp = gp + \
ggplot2.aes_string(x=xid, y=yid) + \
ggplot2.aes_string(size=.5) + \
ggplotter() + \
ggplot2.aes_string(colour='factor(%s)' % factorid) + \
ggplot2.aes_string(fill='factor(%s)' % factorid) + \
ggplot2.opts(title=title) + \
ggplot2.scale_fill_brewer(palette="Set2") + \
ggplot2.scale_colour_brewer(palette="Set2")
pp.plot()
grdevices.dev_off()
def compare_mean_boxplot(locus_table, interval_table, intervals, loci, names, rows):
frame = get_r_data_by_top(locus_table, interval_table, intervals, names,
rows)
if len(intervals) > 1:
sort_string = '''data$interval <- factor(data$interval, {})'''.format(order_intervals(frame[1]))
robjects.r(sort_string)
gg_frame = ggplot2.ggplot(robjects.r('''data'''))
plot = gg_frame + ggplot2.aes_string(x = 'interval', y = 'pi') + \
ggplot2.geom_boxplot(ggplot2.aes_string(fill = 'factor(db)'), **{
'outlier.size':3,
'outlier.colour':'#767676',
'outlier.alpha':0.3,
'alpha':0.6
}
) + \
ggplot2.scale_y_continuous('mean phylogenetic informativeness') + \
ggplot2.scale_x_discrete('interval (years ago)') + \
ggplot2.scale_fill_brewer("database", palette='Blues')
return plot
def plot_stacked_bar(self, data, row_filter, img_file, ratio=1.0):
if len(row_filter) > 0:
data = data[data['type'].isin(row_filter)]
for value in row_filter:
if re.search('^fetcher:(?:aggr:)?', value):
replacement = re.sub('^fetcher:(?:aggr:)?', '', value)
data.replace(to_replace=value, value=replacement, inplace=True)
# print(data)
p = ggplot2.ggplot(data) \
+ ggplot2.aes_string(x='crawl', y='percentage', fill='type') \
+ ggplot2.geom_bar(stat='identity', position='stack', width=.9) \
+ ggplot2.coord_flip() \
+ ggplot2.scale_fill_brewer(palette='RdYlGn', type='sequential',
guide=ggplot2.guide_legend(reverse=True)) \
+ GGPLOT2_THEME \
+ ggplot2.theme(**{'legend.position': 'bottom',
'aspect.ratio': ratio}) \
+ ggplot2.labs(title='Percentage of Fetch Status',
x='', y='', fill='')
img_path = os.path.join(PLOTDIR, img_file)
p.save(img_path)
return p
def plot_fetch_status(self, data, row_filter, img_file, ratio=1.0):
if len(row_filter) > 0:
data = data[data['type'].isin(row_filter)]
for value in row_filter:
if re.search('^fetcher:(?:aggr:)?', value):
replacement = re.sub('^fetcher:(?:aggr:)?', '', value)
data.replace(to_replace=value, value=replacement, inplace=True)
# print(data)
p = ggplot2.ggplot(data) \
+ ggplot2.aes_string(x='crawl', y='percentage', fill='type') \
+ ggplot2.geom_bar(stat='identity', position='stack', width=.9) \
+ ggplot2.coord_flip() \
+ ggplot2.scale_fill_brewer(palette='RdYlGn', type='sequential',
guide=ggplot2.guide_legend(reverse=True)) \
+ GGPLOT2_THEME \
+ ggplot2.theme(**{'legend.position': 'bottom',
'aspect.ratio': ratio}) \
+ ggplot2.labs(title='Percentage of Fetch Status',
x='', y='', fill='')
img_path = os.path.join(PLOTDIR, img_file)
p.save(img_path)
return p
def plot_crawldb_status(self, data, row_filter, img_file, ratio=1.0):
if len(row_filter) > 0:
data = data[data['type'].isin(row_filter)]
for value in row_filter:
if re.search('^crawldb:status:db_', value):
replacement = re.sub('^crawldb:status:db_', '', value)
data.replace(to_replace=value, value=replacement, inplace=True)
data['size'] = data['size'].astype(float)
print(data)
p = ggplot2.ggplot(data) \
+ ggplot2.aes_string(x='crawl', y='size', fill='type') \
+ ggplot2.geom_bar(stat='identity', position='stack', width=.9) \
+ ggplot2.coord_flip() \
+ ggplot2.scale_fill_brewer(palette='Pastel1', type='sequential',
guide=ggplot2.guide_legend(reverse=False)) \
+ GGPLOT2_THEME \
+ ggplot2.theme(**{'legend.position': 'bottom',
'aspect.ratio': ratio}) \
+ ggplot2.labs(title='CrawlDb Size and Status Counts (before crawling)',
x='', y='', fill='')
img_path = os.path.join(PLOTDIR, img_file)
p.save(img_path)
return p
def rest():
df = q1_median_q3_rep_wide
pops = ["pdc", "dc-cd11b", "dc-cd8a"]
stats_l = []
for stat, (popa, popb) in product(["Q1", "median", "Q3"],
product(pops, pops)):
print(stat, popa, popb)
popa = "hsc"
popb = "pdc"
stat = "median"
mw_u, pvalue = scipy.stats.mannwhitneyu(
[0.8, 0.81, 0.79],
[0.4, 0.39, 0.41],
# df.query("Population == @popa")[stat].to_numpy(),
# df.query("Population == @popb")[stat].to_numpy(),
use_continuity=True,
alternative="two-sided",
)
pvalue
stats_l.append([stat, popa, popb, mw_u, pvalue])
stats_df = pd.DataFrame(stats_l).set_axis(
["stat", "popA", "popB", "U", "pvalue"], axis=1)
kruskal_format_means = pd.pivot(
q1_median_q3_rep_wide.query("Population in @pops"),
index="Population",
columns="Replicate",
values="mean",
)
import scikit_posthocs
stat, p_value = scipy.stats.kruskal(
*[kruskal_format_means.loc[pop].to_numpy() for pop in pops], )
dunn_res_df = scikit_posthocs.posthoc_dunn(
kruskal_format_means.to_numpy(),
p_adjust='fdr_bh',
sort=True,
)
stat, pvalue = scipy.stats.f_oneway(
*[kruskal_format_means.loc[pop].to_numpy() for pop in pops], )
import statsmodels
df = kruskal_format_means.stack().reset_index()
kruskal_format_means
res = statsmodels.stats.multicomp.pairwise_tukeyhsd(
df[0], df['Population'].to_numpy(), alpha=0.05)
res.pvalues
res.summary()
# wilcox.test(c(0.8, 0.79, 0.81), c(0.4, 0.39, 0.41), paired=F, exact=F)
plot_pops = ["pdc", "dc-cd8a", "dc-cd11b"]
results_dir = "/icgc/dkfzlsdf/analysis/hs_ontogeny/notebook-data/gNs4xcMJscaLLwlt"
point_plot_quartiles_png = results_dir + "/point-plot-quartiles.png"
q1_median_q3_rep_wide
ggplot_data = (
q1_median_q3_rep_long.query("Population in @plot_pops").sort_values(
"value",
ascending=False,
).groupby(["Population", "stat"]).apply(
lambda df: df.assign(group_order=np.arange(1, df.shape[0] + 1))))
g = (gg.ggplot(ggplot_data) + gg.aes_string(
x="Population", y="value", group="group_order", color="stat") +
gg.geom_point(position=gg.position_dodge(width=0.5), size=1) +
mh_rpy2_styling.gg_paper_theme + gg.labs(y='Methylation (%)', x=''))
a = 3
rpy2_utils.image_png2(g, (ut.cm(6), ut.cm(6)))
ut.save_and_display(
g,
png_path=point_plot_quartiles_png,
# additional_formats=tuple(),
height=ut.cm(6),
width=ut.cm(6),
)
q1_median_q3_rep_wide
g = (
gg.ggplot(
q1_median_q3_rep_wide.query("Population in @plot_pops").assign(
sample=lambda df: df["Population"].astype(str) + df[
"Replicate"].astype(str))) + gg.geom_boxplot(
gg.aes_string(
x="Population",
fill="Population",
group="sample",
lower="Q1",
upper="Q3",
middle="median",
ymin="min1",
ymax="max99",
# position=gg.position_dodge(width=0.5),
),
stat="identity",
)
# + mh_rpy2_styling.gg_paper_theme
+ gg.theme(axis_text_x=gg.element_text(angle=90, hjust=1)) +
gg.scale_fill_brewer(guide=False))
a = 3
ut.save_and_display(
g,
png_path=point_plot_quartiles_png,
additional_formats=tuple(),
height=ut.cm(6),
width=ut.cm(7),
)
# image_png2(g, (ut.cm(12), ut.cm(12)))
beta_values.loc[:, ("hsc", "1")]
# print str(a)
try:
if dsumFC.has_key(drug):
dsumFC[drug]['Fold_Change'].append(math.log10(float(val)))
dsumY[drug]['Year'].append(yr)
else:
dsumFC[drug]= {'Fold_Change': [math.log10(float(val)),]}
dsumY[drug]= {'Year': [yr,]}
except:
print "FAILURE: dsumFC="+str(dsumFC)+"\n\ndsumY="+str(dsumY)
sys.exit()
drugs = dsumFC.keys()
for x in drugs:
od = rlc.OrdDict([('Fold_Change',robjects.FloatVector(dsumFC[x]['Fold_Change'])),('Year',robjects.FactorVector(dsumY[x]['Year'])),('Drug',robjects.FactorVector(x))])
grdevices.pdf(file="drugs.pdf",width=7,height=7)
dataf = robjects.DataFrame(od)
gp3 = ggplot2.ggplot(dataf)
pp3 = gp3 + ggplot2.scale_fill_brewer(palette='BrBG',name="Year")+ ggplot2.aes_string(x='Year',y='Fold_Change',fill='factor(Year)') + ggplot2.geom_boxplot() + ggplot2.opts(title = x+" Yearly Trend")
# pp3 = gp3 + ggplot2.scale_colour_hue(h=base.c(180,270),name="Year")+ ggplot2.aes_string(x='Year',y='Fold_Change',fill='factor(Year)') + ggplot2.geom_boxplot() + ggplot2.opts(title = x+" Yearly Trend")
#+ ggplot2.scale_y_log10()
pp3.plot()
grdevices.dev_off()
f.close()
print "\nfinished\n"
