def render(self):
# Convert first column of dataframe to list
pie_data = self.table.iloc[:, 0].to_list()
# Draw pie chart
self.fig, self.ax = plt.subplots(figsize=(12, 12), subplot_kw=dict(aspect="equal"))
cs = cm.tab20c(np.arange(20)) # set color palette with a 20 different colors
self.ax.pie(
pie_data, colors=cs, autopct='%1.2f%%', pctdistance=0.8, textprops={'color': '#333333', 'weight': 'bold'}
)
self.log.info("Pie chart '%s' rendered succesfully", self.title)
def create_plots(dict_counter, name, jaro, text_on=False, which_text=None):
distances = {}
group = {}
labels = []
for k1 in dict_counter.keys():
for k2 in dict_counter[k1].keys():
name1 = f"{k2}_{k1}"
distances[name1] = Levenshtein.jaro_winkler(
k1, k2) + 0.0000001 * np.random.random()
group[name1] = k1
labels.append(k1)
testa = list(set(group.values()))
best_word = find_first_word(testa)
testa.remove(best_word)
testb = sort_jaro_worst(best_word, testa[:])
colors = []
if len(testb) <= 20:
cm_subsection1 = linspace(0.0, 1.0, len(testb))
elif len(testb) > 20 and len(testb) <= 40:
cm_subsection1 = linspace(0.0, 1.0, 20)
cm_subsection2 = linspace(0.0, 1.0, len(testb) - 20)
else:
cm_subsection1 = linspace(0.0, 1.0, 20)
cm_subsection2 = linspace(0.0, 1.0, 20)
cm_subsection3 = linspace(0.0, 1.0, len(testb) - 40)
for i in range(len(testb)):
if i < 20:
colors.append(cm.tab20(cm_subsection1[i]))
elif i >= 20 and i < 39:
colors.append(cm.tab20b(cm_subsection2[i - 20]))
else:
colors.append(cm.tab20c(cm_subsection3[i - 40]))
colorsdict = dict(zip(testb, colors))
df = pd.Series(distances)
c = [colorsdict.get(group[label], 'k') for label in df.index]
fig, aa = plt.subplots(figsize=(11, 9))
aa.axes.get_xaxis().set_visible(False)
aa.set_xlim(-11, 0.1)
aa.set_ylim(jaro-.005, 1.005)
scatter_items = []
legend_items = []
for t, d, ca, l in zip([0 for _ in df], df, c, df.index):
scatter = aa.scatter(t, d, c=ca, alpha=0.5,
edgecolors='none')
for l, colo in zip(colorsdict.keys(), colorsdict.values()):
scatter_items += aa.plot((-100,), (-100,),
ls='none', marker='.', c=colo, label=l)
aa.legend(handles=scatter_items, title="Terms", fontsize=7,
loc='upper center', bbox_to_anchor=(0.5, -0.05), ncol=10)
aa.spines['left'].set_visible(False)
aa.spines['top'].set_visible(False)
aa.spines['bottom'].set_visible(False)
aa.yaxis.set_label_position('right')
aa.yaxis.set_ticks_position('right')
plt.tight_layout()
# We add a rectangle to make sure the labels don't move to the right
patch = patches.Rectangle((-0.1, 0), 0.2, 100, fill=False, alpha=0)
aa.add_patch(patch)
texts = []
np.random.seed(0)
for label, y in zip(df.index, df):
texts += [aa.text(-.1+np.random.random()/1000, y, label.split("_")[0],
color=colorsdict.get(group[label], 'k'), fontsize=9)]
adjust_text(texts, [0 for _ in df], df.values, ha='right', va='center', add_objects=[patch],
expand_text=(1.1, 1.25),
force_text=(0.75, 0), force_objects=(1, 0),
autoalign=False, only_move={'points': 'x', 'text': 'x', 'objects': 'x'})
plt.savefig(f'{name}_scatter.png')
print(len(list(set(group.values()))))
print(len(testb))
print(testb)
#####
from matplotlib import cm
words = ['Biological Process', 'Molecular Function', 'Cellular Component']
pie_colors = {'Set3':cm.Set3(np.arange(12)/12.),
'Set2':cm.Set2(np.arange(8)/8.),
'Set1':cm.Set1(np.arange(9)/9.),
'Pastel2':cm.Pastel2(np.arange(8)/8.),
'Pastel1':cm.Pastel1(np.arange(9)/9.),
'Dark2':cm.Dark2(np.arange(8)/8.),
'Paired':cm.Paired(np.arange(12)/12.),
'Accent':cm.Accent(np.arange(8)/8.),
'Spectral':cm.Spectral(np.arange(11)/11.),
'tab20':cm.tab20(np.arange(20)/20.),
'tab20b':cm.tab20b(np.arange(20)/20.),
'tab20c':cm.tab20c(np.arange(20)/20.)}
colors = {'#8DD3C7':pie_colors['Set3'][0:1], '#FFFFB3':pie_colors['Set3'][1:2],
'#BEBADA':pie_colors['Set3'][2:3], '#FB8072':pie_colors['Set3'][3:4],
'#80B1D3':pie_colors['Set3'][4:5], '#FDB462':pie_colors['Set3'][5:6],
'#B3DE69':pie_colors['Set3'][6:7], '#FCCDE5':pie_colors['Set3'][7:8],
'#D9D9D9':pie_colors['Set3'][8:9], '#BC80BD':pie_colors['Set3'][9:10],
'#CCEBC5':pie_colors['Set3'][10:11], '#FFED6F':pie_colors['Set3'][11:12]}
sizes = ['3x3','4x4','5x5','6x6','7x7','8x8','9x9','10x10']
escala_uno = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1]
#from __future__ import division
#import matplotlib.pyplot as plt
#from matplotlib import colors as mcolors
#colors = dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS)
# lista de colores python
def get_colour(data):
drug, dose, recover, period = data
dose = float(dose)
#print drug
if drug == 'PYY': # graded green
if dose == 300:
c = cm.tab20c(0.4)
elif dose == 7.5:
c = cm.tab20c(0.45)
elif dose == 1.5:
c = cm.tab20c(0.5)
else:
print 'ERROR with drug %s, dose %f' %(drug, dose)
elif drug == 'LiCl': # graded orange
if dose == 64:
c = cm.tab20c(0.2)
elif dose == 32:
c = cm.tab20c(0.25)
elif dose == 16:
c = cm.tab20c(0.3)
else:
print 'ERROR with drug %s, dose %f' %(drug, dose)
elif drug == 'GLP-1': # graded violet
if dose == 300:
c = cm.tab20c(0.6)
elif dose == 100:
c = cm.tab20c(0.65)
elif dose == 30:
c = cm.tab20c(0.7)
else:
print 'ERROR with drug %s, dose %f' %(drug, dose)
elif drug == 'sib':
#c = cm.tab20c(0.6) # yellow
c= 'y'
elif drug == 'Ex-4':
#c = cm.tab20c(0.8) # grey
c = 'k'
elif drug == 'Lep':
#c = cm.tab20c(0) # red
c = 'r'
elif drug == 'saline': # graded blue
if recover == 'A':
c = cm.tab20c(0.8) # grey
elif recover == 'R':
c = cm.tab20c(0) # blue
elif drug == 'vehicle':
c = cm.tab20c(0.05)
else:
print 'ERROR with drug %s' %(drug)
return c
def df_input(dfUniprot=DataFrame([])):
num_phylum = len(dfUniprot.PHYLUM.drop_duplicates())
from matplotlib import cm
set3 = [
matplotlib.colors.rgb2hex(tuple(i))
for i in cm.Set3(np.arange(12) / 12.)
]
set2 = [
matplotlib.colors.rgb2hex(tuple(i))
for i in cm.Set2(np.arange(8) / 8.)
]
set1 = [
matplotlib.colors.rgb2hex(tuple(i))
for i in cm.Set1(np.arange(9) / 9.)
]
pastel2 = [
matplotlib.colors.rgb2hex(tuple(i))
for i in cm.Pastel2(np.arange(8) / 8.)
]
pastel1 = [
matplotlib.colors.rgb2hex(tuple(i))
for i in cm.Pastel1(np.arange(9) / 9.)
]
dark2 = [
matplotlib.colors.rgb2hex(tuple(i))
for i in cm.Dark2(np.arange(8) / 8.)
]
paired = [
matplotlib.colors.rgb2hex(tuple(i))
for i in cm.Paired(np.arange(12) / 12.)
]
accent = [
matplotlib.colors.rgb2hex(tuple(i))
for i in cm.Accent(np.arange(8) / 8.)
]
spectral = [
matplotlib.colors.rgb2hex(tuple(i))
for i in cm.Spectral(np.arange(11) / 11.)
]
tab20 = [
matplotlib.colors.rgb2hex(tuple(i))
for i in cm.tab20(np.arange(20) / 20.)
]
tab20b = [
matplotlib.colors.rgb2hex(tuple(i))
for i in cm.tab20b(np.arange(20) / 20.)
]
tab20c = [
matplotlib.colors.rgb2hex(tuple(i))
for i in cm.tab20c(np.arange(20) / 20.)
]
Colors1 = set2 + set1 + dark2 + paired + accent + spectral + tab20 + tab20b + tab20c
Colors2 = accent + spectral + tab20 + tab20b + tab20c + set1 + set2 + dark2 + paired
Colors3 = dark2 + paired + accent + spectral + tab20 + tab20b + tab20c + set1 + set2
Colors4 = tab20b + tab20c + set1 + set2 + dark2 + paired + accent + spectral + tab20
Colors5 = spectral + tab20 + tab20b + tab20c + set1 + set2 + dark2 + paired + accent
pie_colors = {
'Set3': cm.Set3(np.arange(12) / 12.),
'Set2': cm.Set2(np.arange(8) / 8.),
'Set1': cm.Set1(np.arange(9) / 9.),
'Pastel2': cm.Pastel2(np.arange(8) / 8.),
'Pastel1': cm.Pastel1(np.arange(9) / 9.),
'Dark2': cm.Dark2(np.arange(8) / 8.),
'Paired': cm.Paired(np.arange(12) / 12.),
'Accent': cm.Accent(np.arange(8) / 8.),
'Spectral': cm.Spectral(np.arange(11) / 11.),
'tab20': cm.tab20(np.arange(20) / 20.),
'tab20b': cm.tab20b(np.arange(20) / 20.),
'tab20c': cm.tab20c(np.arange(20) / 20.)
}
circle_colors = {
'Colors1': Colors1[0:num_phylum],
'Colors2': Colors2[0:num_phylum],
'Colors3': Colors3[0:num_phylum],
'Colors4': Colors4[0:num_phylum],
'Colors5': Colors5[0:num_phylum]
}
def tax_colors(color_list=circle_colors['Colors1'], taxx=dfUniprot):
tax_cols = [
'Entry', 'Tax_ID', 'KINGDOM', 'PHYLUM', 'CLASS', 'ORDER', 'FAMILY',
'GENUS', 'SPECIES', 'Organism'
]
new2 = taxx[tax_cols].drop_duplicates()
#>>>>>>>>>>>>>>>>>>>>>>>>>>>
phylum0 = new2.groupby(['PHYLUM'
]).Entry.count().reset_index().sort_values(
by='Entry',
ascending=False).reset_index(drop=True)
asign_color = {}
for i, j in zip(phylum0.PHYLUM, color_list):
if i == 'NA':
asign_color[i] = 'black'
else:
asign_color[i] = j
phylum0['phy_col'] = list(asign_color.values())
# distribución de Class
phylum1 = new2.groupby(['PHYLUM', 'CLASS']).Entry.count().reset_index()
class0 = []
class0_colors = []
for i in phylum0.PHYLUM:
for j in phylum1.PHYLUM:
if i == j:
class0_colors.append(asign_color[j])
class0.append(phylum1[phylum1.PHYLUM == i].sort_values(
by='Entry', ascending=False).reset_index(drop=True))
else:
pass
class1 = pd.concat(class0).drop_duplicates()
class1['class_col'] = class0_colors
class0_colors_corregido = []
for index, row in class1.iterrows():
if row.PHYLUM == 'NA':
if row.CLASS == 'NA':
class0_colors_corregido.append(row.class_col)
else:
class0_colors_corregido.append('grey')
else:
if row.CLASS == 'NA':
class0_colors_corregido.append('black')
else:
class0_colors_corregido.append(row.class_col)
class1['class_col'] = class0_colors_corregido
class11 = class1.groupby(['CLASS'
]).Entry.sum().reset_index().sort_values(
by='Entry',
ascending=False).reset_index(drop=True)
class11 = class11.merge(class1[['CLASS',
'class_col']].drop_duplicates(),
on='CLASS',
how='left')
# distribución de Order
phylum2 = new2.groupby(['PHYLUM', 'CLASS',
'ORDER']).Entry.count().reset_index()
order0 = []
order0_colors = []
for i in phylum0.PHYLUM:
for j in phylum2.PHYLUM:
if i == j:
order0_colors.append(asign_color[j])
order0.append(phylum2[phylum2.PHYLUM == i].sort_values(
by='Entry', ascending=False).reset_index(drop=True))
else:
pass
order1 = pd.concat(order0).drop_duplicates()
order1['order_col'] = order0_colors
order0_colors_corregido = []
for index, row in order1.iterrows():
if row.PHYLUM == 'NA':
if row.ORDER == 'NA':
order0_colors_corregido.append(row.order_col)
else:
order0_colors_corregido.append('grey')
else:
if row.ORDER == 'NA':
order0_colors_corregido.append('black')
else:
order0_colors_corregido.append(row.order_col)
order1['order_col'] = order0_colors_corregido
order11 = order1.groupby(['ORDER'
]).Entry.sum().reset_index().sort_values(
by='Entry',
ascending=False).reset_index(drop=True)
order11 = order11.merge(order1[['ORDER',
'order_col']].drop_duplicates(),
on='ORDER',
how='left')
# distribución de Genus
phylum3 = new2.groupby(['PHYLUM', 'CLASS', 'ORDER',
'GENUS']).Entry.count().reset_index()
genus0 = []
genus0_colors = []
for i in phylum0.PHYLUM:
for j in phylum3.PHYLUM:
if i == j:
genus0_colors.append(asign_color[j])
genus0.append(phylum3[phylum3.PHYLUM == i].sort_values(
by='Entry', ascending=False).reset_index(drop=True))
else:
pass
genus1 = pd.concat(genus0).drop_duplicates()
genus1['genus_col'] = genus0_colors
genus0_colors_corregido = []
for index, row in genus1.iterrows():
if row.PHYLUM == 'NA':
if row.GENUS == 'NA':
genus0_colors_corregido.append(row.genus_col)
else:
genus0_colors_corregido.append('grey')
else:
if row.GENUS == 'NA':
genus0_colors_corregido.append('black')
else:
genus0_colors_corregido.append(row.genus_col)
genus1['genus_col'] = genus0_colors_corregido
genus11 = genus1.groupby(['GENUS'
]).Entry.sum().reset_index().sort_values(
by='Entry',
ascending=False).reset_index(drop=True)
genus11 = genus11.merge(genus1[['GENUS',
'genus_col']].drop_duplicates(),
on='GENUS',
how='left')
# distribución de Organism
phylum4 = new2.groupby(
['PHYLUM', 'CLASS', 'ORDER', 'GENUS',
'Organism']).Entry.count().reset_index()
org0 = []
org0_colors = []
for i in phylum0.PHYLUM:
for j in phylum4.PHYLUM:
if i == j:
org0_colors.append(asign_color[j])
org0.append(phylum4[phylum4.PHYLUM == i].sort_values(
by='Entry', ascending=False).reset_index(drop=True))
else:
pass
org1 = pd.concat(org0).drop_duplicates()
org1['org_col'] = org0_colors
org0_colors_corregido = []
for index, row in org1.iterrows():
if row.PHYLUM == 'NA':
if row.Organism == 'NA':
org0_colors_corregido.append(row.org_col)
else:
org0_colors_corregido.append('grey')
else:
if row.Organism == 'NA':
org0_colors_corregido.append('black')
else:
org0_colors_corregido.append(row.org_col)
org1['org_col'] = org0_colors_corregido
org11 = org1.groupby(['Organism'
]).Entry.sum().reset_index().sort_values(
by='Entry',
ascending=False).reset_index(drop=True)
org11 = org11.merge(org1[['Organism', 'org_col']].drop_duplicates(),
on='Organism',
how='left')
os.makedirs('tax', exist_ok=True)
return phylum0.to_csv('tax/phylum0.tsv', sep = '\t', index = None),\
class1.to_csv('tax/class1.tsv', sep = '\t', index = None),\
class11.to_csv('tax/class11.tsv', sep = '\t', index = None),\
order1.to_csv('tax/order1.tsv', sep = '\t', index = None),\
order11.to_csv('tax/order11.tsv', sep = '\t', index = None),\
genus1.to_csv('tax/genus1.tsv', sep = '\t', index = None),\
genus11.to_csv('tax/genus11.tsv', sep = '\t', index = None),\
org1.to_csv('tax/org1.tsv', sep = '\t', index = None),\
org11.to_csv('tax/org11.tsv', sep = '\t', index = None)
alfas = {
'Lineage*': [1, 1, 1, 1, 1],
'Phylum': [1, 0.3, 0.3, 0.3, 0.3],
'Class': [0.3, 1, 0.3, 0.3, 0.3],
'Order': [0.3, 0.3, 1, 0.3, 0.3],
'Genus': [0.3, 0.3, 0.3, 1, 0.3],
'Species': [0.3, 0.3, 0.3, 0.3, 1],
'Gradient1*': [1, 0.85, 0.7, 0.55, 0.4],
'Gradient2*': [0.4, 0.55, 0.7, 0.85, 1],
'Attenuate*': [0.3, 0.3, 0.3, 0.3, 0.3],
'None*': [0, 0, 0, 0, 0]
}
def circle_lineage(alphas=alfas['Phylum']):
#fig, ax = plt.subplots(111, facecolor= 'white')
#fig, ax = plt.subplot(111)
phylum0 = pd.read_csv('tax/phylum0.tsv', sep='\t').fillna('NA')
class1 = pd.read_csv('tax/class1.tsv', sep='\t').fillna('NA')
order1 = pd.read_csv('tax/order1.tsv', sep='\t').fillna('NA')
genus1 = pd.read_csv('tax/genus1.tsv', sep='\t').fillna('NA')
org1 = pd.read_csv('tax/org1.tsv', sep='\t').fillna('NA')
radio = 0.5
linaje = [phylum0, class1, order1, genus1, org1]
#colores = [list(asign_color.values()), class0_colors, order0_colors, genus0_colors, org0_colors]
colores = ['phy_col', 'class_col', 'order_col', 'genus_col', 'org_col']
pat = []
size = -.205
for i, j, k in zip(linaje, colores, alphas):
size += .205
patches, texts = plt.pie(
i.Entry,
radius=radio + size,
labels=None,
labeldistance=0.8,
rotatelabels=True,
colors=
i[j], # new_colors(valor = len(i.Entry), col = 'nipy_spectral'),
wedgeprops=dict(width=0.2, edgecolor='white', alpha=k),
textprops=dict(size=10))
pat.append(patches)
#plt.legend(pat[0], df_phylum.PHYLUM, loc=2,fontsize=13,labelspacing = 0.4,
# bbox_to_anchor=(1.05, 1),frameon=False)
plt.gca().set(aspect='equal')
plt.title('Root', fontsize=10, x=0.5, y=0.465)
plt.text(-1.8,
1.35,
'Lineage',
fontsize=15,
ha='left',
va='center',
color='black')
#plt.title('Lineage',fontsize=20, fontweight='bold', x = -0.17, y = 1)
#plt.text(1.1, 1.35, linaje_seleccionado, fontsize = 15, ha='left', va='center',
# color='black')
#>>>>>>>>>>>>>>>>>>>>>>>
#### insetplot
#ax2 = plt.axes([0.1, 0.66, 0.13, 0.14])
ax2 = plt.axes([-0.07, 1.71, 0.17, 0.18])
logo = [20, 20, 20, 20, 20, 20, 20, 20]
logo_col = [
'white', 'white', 'black', 'white', 'white', 'white', 'white',
'white'
]
logo_col1 = [
'white', 'white', 'black', 'black', 'black', 'black', 'black',
'black'
]
radio = 0.5
linaje = [logo, logo, logo, logo, logo]
colores = [logo_col1, logo_col, logo_col, logo_col, logo_col]
name_linaje = ['Phylum', 'Class', 'Order', 'Genus', 'Species']
pat = []
size = -.44
pos = -.18
for i, j, k, l in zip(linaje, colores, name_linaje, alphas):
pos += .47
size += .44
ax2.pie(i,
radius=radio + size,
labels=None,
colors=j,
wedgeprops=dict(width=0.35, edgecolor='white', alpha=l),
textprops=dict(size=10))
ax2.text(0.1,
pos,
k,
fontsize=9,
ha='left',
va='center',
fontweight='bold',
alpha=l) #color='black'
def barras_tax(df=DataFrame([]),
column=0,
dim=111,
title='',
row_num=10,
color=['#ff7f0e'],
size_x=8,
size_y=10,
ylabel_text='',
xlabel=10,
ylabel=10,
size_title=15,
size_bartxt=10,
sep=1.2):
if len(df) == 0:
print('Data frame sin datos')
else:
#plt.subplot(dim, facecolor= 'white')
barWidth = 0.9
if row_num == len(df):
ejey = list(df.iloc[0:len(df), 1])
val = max(ejey)
ejex = list(df.iloc[0:len(df), column])
colores = list(df.iloc[0:len(df), 2])
borde = list(
np.repeat('white', len(df.iloc[0:row_num, column])))
linea = list(np.repeat(0, len(df.iloc[0:row_num, column])))
if row_num < len(df):
ejey = list(df.iloc[0:row_num,
1]) + [df.iloc[row_num:len(df), 1].sum()]
val = max(ejey)
ejex = list(df.iloc[0:row_num, column]) + ['Others']
borde = list(
np.repeat('white', len(df.iloc[0:row_num,
column]))) + ['black']
colores = list(df.iloc[0:row_num, 2]) + ['linen']
linea = list(np.repeat(0, len(df.iloc[0:row_num,
column]))) + [1]
if row_num > len(df):
ejey = list(df.iloc[0:len(df), 1])
val = max(ejey)
ejex = list(df.iloc[0:len(df), column])
borde = list(
np.repeat('white', len(df.iloc[0:row_num, column])))
colores = list(df.iloc[0:len(df), 2])
linea = list(np.repeat(0, len(df.iloc[0:row_num, column])))
for i, j, k, l, m in zip(ejex, ejey, borde, colores, linea):
plt.barh(i,
j,
color=l,
align='center',
height=0.7,
linewidth=m,
alpha=1,
edgecolor=k)
plt.gca().spines['right'].set_visible(False)
plt.gca().spines['top'].set_visible(False)
plt.gca().spines['bottom'].set_position(('data', -0.6))
plt.gca().spines['left'].set_visible(False)
plt.title(title, size=size_title, loc='left')
plt.tick_params(axis="y", color="gray")
plt.yticks(size=size_y)
v1 = -50
v2 = 0
v3 = 0
for i in range(10000):
v1 += 50
v2 += 50
v3 += 10
if v1 <= max(list(ejey)) < v2:
#print(v3, v1, val, v2)
escala = v3
plt.xticks(range(0, val, escala), size=size_x) #fontweight='bold'
plt.ylabel(ylabel_text, size=ylabel)
plt.xlabel("Number of Proteins", size=xlabel)
#plt.tick_params(top = 'on', bottom = 'on', right = 'on', left = 'on')
#plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
for j, k in zip(ejey, range(0, len(ejey))):
plt.text(j + sep,
k - 0.2,
j,
size=size_bartxt,
ha='left',
color='black')
import ipywidgets as widgets
from ipywidgets import interact, interactive, fixed, interact_manual, Button, HBox, VBox, IntSlider, Label, IntRangeSlider
from ipywidgets import Checkbox, RadioButtons
from ipywidgets import Button, Layout
alfas = {
'Lineage*': [1, 1, 1, 1, 1],
'Phylum': [1, 0.3, 0.3, 0.3, 0.3],
'Class': [0.3, 1, 0.3, 0.3, 0.3],
'Order': [0.3, 0.3, 1, 0.3, 0.3],
'Genus': [0.3, 0.3, 0.3, 1, 0.3],
'Species': [0.3, 0.3, 0.3, 0.3, 1],
'Gradient1*': [1, 0.85, 0.7, 0.55, 0.4],
'Gradient2*': [0.4, 0.55, 0.7, 0.85, 1],
'Attenuate*': [0.3, 0.3, 0.3, 0.3, 0.3],
'None*': [0, 0, 0, 0, 0]
}
plotss = ['Phylum', 'Class', 'Order', 'Genus', 'Species']
posicion_subplots = []
n = 0.9
while n < 2:
n += 0.1
posicion_subplots.append(np.around(n, 1))
color_a6 = widgets.Dropdown(options=list(circle_colors.keys()),
value='Colors1',
description='Colors:',
disabled=False,
button_style='',
layout=Layout(width='20%', height='25px'))
a6 = widgets.Dropdown(options=list(alfas.keys()),
description='Chart 1:',
value='Phylum',
disabled=False,
button_style='',
layout=Layout(width='20%', height='25px'))
a61 = widgets.Dropdown(options=plotss,
description='Chart 2:',
disabled=False,
button_style='',
layout=Layout(width='20%', height='25px'))
pos_sub1 = widgets.Dropdown(options=posicion_subplots,
value=1.3,
description='xloc1:',
disabled=False,
layout=Layout(width='15%', height='25px'))
pos_sub2 = widgets.Dropdown(options=posicion_subplots,
value=1.3,
description='xloc2:',
disabled=False,
layout=Layout(width='15%', height='25px'))
b6 = widgets.Dropdown(options=list(range(0, 101)),
value=10,
description='rows1:',
disabled=False,
layout=Layout(width='15%', height='25px'))
c6 = widgets.Dropdown(options=list(range(0, 101)),
value=10,
description='rows2:',
disabled=False,
layout=Layout(width='15%', height='25px'))
z6 = widgets.ToggleButton(value=False,
description='Save Chart',
disabled=False,
button_style='',
tooltip='Description')
o6 = widgets.Dropdown(options=[0, 0.25, 0.5, 0.75] + list(range(0, 201)),
value=3,
description='sep1:',
disabled=False,
layout=Layout(width='15%', height='25px'))
o61 = widgets.Dropdown(options=[0, 0.25, 0.5, 0.75] + list(range(0, 201)),
value=3,
description='sep2:',
disabled=False,
layout=Layout(width='15%', height='25px'))
d6 = widgets.Dropdown(options=list(range(0, 51)),
value=8,
description='size_y1:',
disabled=False,
layout=Layout(width='15%', height='25px'))
d61 = widgets.Dropdown(options=list(range(0, 51)),
value=8,
description='size_y2:',
disabled=False,
layout=Layout(width='15%', height='25px'))
g6 = widgets.Dropdown(options=list(range(0, 51)),
value=8,
description='bartxt1:',
disabled=False,
layout=Layout(width='15%', height='25px'))
g61 = widgets.Dropdown(options=list(range(0, 51)),
value=8,
description='bartxt2:',
disabled=False,
layout=Layout(width='15%', height='25px'))
xxx = Button(layout=Layout(width='5%', height='25px'), disabled=True)
xxx.style.button_color = 'white'
yyy = Button(layout=Layout(width='94%', height='5px'), disabled=True)
yyy.style.button_color = 'red'
ww = widgets.HBox([color_a6, xxx, z6])
w6 = widgets.HBox([
a6,
b6,
o6,
d6,
g6,
pos_sub1,
])
w7 = widgets.HBox([
a61,
c6,
o61,
d61,
g61,
pos_sub2,
])
w8 = widgets.VBox([w6, w7, yyy])
######
def col(color_a6):
tax_colors(color_list=circle_colors[color_a6], taxx=dfUniprot)
out7 = widgets.interactive_output(col, {'color_a6': color_a6})
def box1(a6, a61, pos_sub1, pos_sub2, b6, c6, z6, o6, o61, d6, d61, g6,
g61):
yetiquetas_plot1 = {
'Lineage*': 'Phylum',
'Phylum': 'Phylum',
'Class': 'Class',
'Order': 'Order',
'Genus': 'Genus',
'Species': 'Species',
'Gradient1*': 'Phylum',
'Gradient2*': 'Phylum',
'Attenuate*': 'Phylum',
'None*': 'Phylum'
}
plots1 = {
'Lineage*': pd.read_csv('tax/phylum0.tsv', sep='\t').fillna('NA'),
'Phylum': pd.read_csv('tax/phylum0.tsv', sep='\t').fillna('NA'),
'Class': pd.read_csv('tax/class11.tsv', sep='\t').fillna('NA'),
'Order': pd.read_csv('tax/order11.tsv', sep='\t').fillna('NA'),
'Genus': pd.read_csv('tax/genus11.tsv', sep='\t').fillna('NA'),
'Species': pd.read_csv('tax/org11.tsv', sep='\t').fillna('NA'),
'Gradient1*': pd.read_csv('tax/phylum0.tsv',
sep='\t').fillna('NA'),
'Gradient2*': pd.read_csv('tax/phylum0.tsv',
sep='\t').fillna('NA'),
'Attenuate*': pd.read_csv('tax/phylum0.tsv',
sep='\t').fillna('NA'),
'None*': pd.read_csv('tax/phylum0.tsv', sep='\t').fillna('NA')
}
plots2 = {
'Phylum': pd.read_csv('tax/phylum0.tsv', sep='\t').fillna('NA'),
'Class': pd.read_csv('tax/class11.tsv', sep='\t').fillna('NA'),
'Order': pd.read_csv('tax/order11.tsv', sep='\t').fillna('NA'),
'Genus': pd.read_csv('tax/genus11.tsv', sep='\t').fillna('NA'),
'Species': pd.read_csv('tax/org11.tsv', sep='\t').fillna('NA')
}
ax3 = plt.axes([pos_sub2, .97, .3, 0.55])
##>>>>>>>>>>> grafico circular
ax = plt.axes([0, 1, 0.9, 1])
circle_lineage(alphas=alfas[a6])
##>>>>>>>>>>> grafico 1
#ax2 = plt.axes([pos_sub1, 1.51, .3, 0.55])
ax2 = plt.axes([pos_sub1, 1.63, .3, 0.4]) #>>>>>>>>>>
barras_tax(
plots1[a6],
#barras_tax(tax_colors(color_list = circle_colors['Spectral'])[0],
row_num=b6,
color=plots1[a6].iloc[0:b6, 2],
sep=o6,
size_y=d6,
size_bartxt=g6,
ylabel_text=yetiquetas_plot1[a6],
ylabel=10)
##>>>>>>>>>>> grafico 2
ax3 = plt.axes([pos_sub2, .97, .3, 0.55])
barras_tax(
plots2[a61],
#barras_tax(tax_colors(color_list = circle_colors['Spectral'])[0],
row_num=c6,
color=plots2[a61].iloc[0:b6, 2],
sep=o61,
size_y=d61,
size_bartxt=g61,
ylabel_text=yetiquetas_plot1[a61],
ylabel=10)
##>>>>>>>>>>>> save
if z6 == True:
import datetime
plt.savefig('img/Lineage' +
datetime.datetime.now().strftime('%d.%B.%Y_%I-%M%p') +
'.png',
dpi=900,
bbox_inches='tight')
else:
pass
out6 = widgets.interactive_output(
box1, {
'a6': a6,
'a61': a61,
'pos_sub1': pos_sub1,
'pos_sub2': pos_sub2,
'b6': b6,
'c6': c6,
'z6': z6,
'o6': o6,
'o61': o61,
'd6': d6,
'd61': d61,
'g6': g6,
'g61': g61
})
import warnings
warnings.filterwarnings("ignore")
return display(VBox([yyy, ww, w8, out6]))
part_dir = '../output/pipeline_snapshots/particles'
parts = pd.read_csv(part_dir+'/parts_filtered.csv')
parts = pd.read_csv('../output/pipeline_snapshots/particles/parts_filtered_10partsPerCell.csv')
parts = parts[~(parts.strain=='yQC25')]
rep_dict = {'yQC21':15, 'yQC22':14,'yQC23':12,'yQC62':10, 'yQC26':10.1,
'yQC63TL174':9, 'TL47pQC99':33, 'TL47pQC116':30 , 'TL47pQC115':32, 'TL47pQC118':31,
'TL47pQC119S2':27, 'TL47pQC1195D':28, 'TL47pQC1203K':27.1, 'TL47pQC1202H':26, 'TL47pQC1192E':29}
parts['CTDr'] = parts.strain.map(rep_dict)
parts['cid'] = parts['roi'].apply(str) + parts['mov_name']
################################################################################
################################################################################
plot_dir = '../output/pipeline_snapshots/plots/'
colors = ['b','k','r','seagreen','mediumorchid', 'g','y','cyan']
colors = cm.tab20c(np.linspace(0.1,1, len(parts.strain.unique())))
colors_strain = {s:c for s,c in zip(parts.strain.unique(), colors)}
patches = [mpatches.Patch(color=colors_strain[l], label=l) for l in colors_strain]
# filter by mov_name
filt_bymov = []
for im, df in parts.groupby('mov_name'):
filt_bymov.append(df[(df.mass_norm>df.mass_norm.mean())])
filt_bymov = pd.concat(filt_bymov, ignore_index=True)
fig, ax = plt.subplots()
filt_bymov.groupby('strain').apply(lambda x: plot_ecdf(x['mass_norm'].values, color=colors[x.name], ax=ax, label=x.name))
plt.legend()
fig, ax = plt.subplots(figsize=(10,8))
parts[(parts.mass_norm>=7)&(parts.corrwideal>=0.5)].groupby('strain').apply(lambda x: plot_ecdf(x['mass_norm'].values,
color=colors_strain[x.name], ax=ax, label=x.name, formal=True, rasterized=True))
parts[(parts.mass_norm>=7)&(parts.corrwideal>=0.5)].groupby('strain').apply(lambda x: ax.axvline(np.median(x['mass_norm'].values),
joint3D = os.path.join(inBasePath, 'pose_joints_3d', imgNr + '.npy')
joint2D = os.path.join(inBasePath, 'pose_joints_2d', imgNr + '.npy')
imgPath = os.path.join(inBasePath, 'composition', imgNr + '.png')
cam = np.load(camIn, allow_pickle=True)
joints3D = np.load(joint3D, allow_pickle=True).item()
joints2D = np.load(joint2D, allow_pickle=True).item()
img = cv2.imread(imgPath)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
sHumanNames = joints2D.keys()
############### 3d joints in world coordinate system ##########################
fig = plt.figure()
ax = fig.add_subplot(121, projection='3d')
ax2 = fig.add_subplot(122)
colors = cm.tab20c(np.linspace(0, 1, len(sHumanNames)))
# plot initial 3D positions
for i, key in enumerate(sHumanNames):
ax.scatter(joints3D[key][:, 0],
joints3D[key][:, 1],
joints3D[key][:, 2],
c=np.reshape(colors[i], (1, -1)))
ax.set_xlabel('X axis')
ax.set_ylabel('Y axis')
ax.set_zlabel('Z axis')
ax.scatter(cam[0, 3], cam[1, 3], cam[2, 3],
c='black') # show camera as black dot
ax2.imshow(img)
# label_list = ("SRC", "SOL$_{DIV}$", "SOL$_5$")
#
# plot_iterations_for_target(list, label_list)
# #TODO Full Iterations Plot
# list = ("zeroguess", "tompson_scalar", "supervised", "solverbased_i5")
# label_list = ("Zero", "PHY", "SUP", "SOL$_5$")
# colors = (cm.tab20c( 7,20), cm.tab20c(13,20), cm.tab20c(17,20), cm.Greens(0.5))
#
# plot_iterations(list, label_list)
#
#
#TODO Residual Plot
list = ("zeroguess", "solverbased_i5", "tompson_scalar", "supervised")
label_list = ("Zero", "SOL$_5$", "PHY", "SUP")
colors = (cm.tab20c(7, 20), cm.Greens(0.5), cm.tab20c(13,
20), cm.tab20c(17, 20))
plot_residuum(list, label_list, cropped=False)
#
#
# #TODO Ablation Plots
# list = ("solverbased_i1", "solverbased_i2", "solverbased_i5", "solverbased", "solverbased_i15")
# label_list = ("SOL$_1$", "SOL$_2$", "SOL$_5$", "SOL$_{10}$", "SOL$_{15}$")
# colors = (cm.Greens(0.3), cm.Greens(0.5), cm.Greens(0.7), cm.Greens(0.9), cm.Greens(1.1))
#
# plot_residuum_zoomed(list, label_list, cropped=True)
# plot_iterations(list, label_list)
#
#
# #TODO Residual Image Comparison