# Imports
from glycowork.motif.analysis import get_heatmap
import pandas as pd

# Example with dataframe with alphanumerical index without special character
df_no_special = pd.DataFrame({
    'Gal(b1-3)GalNAc': [0.242959, 0.208267, 0.223529, 0.245893, 0.297072],
    'GalOS(b1-3)GalNAc': [0.007172, 0.012267, 0.017346, 0.004030, 0.006302],
    'Gal(b1-3)[Fuc(a1-?)]GalNAc': [0.140820, 0.183320, 0.182716, 0.202931, 0.160700],
    'Fuc(a1-2)Gal(b1-3)GalNAc': [1.811925, 1.782675, 1.249882, 1.189128, 1.221432],
    'Fuc(a1-?)[HexNAc(?1-?)]GalNAc': [0.098392, 0.079560, 0.063288, 0.047380, 0.051726]
}, index=['A1', 'B1', 'C1', 'D1', 'E1'])

print("Heatmap with alphanumeric index:")
get_heatmap(df_no_special, motifs=True)

# Example with dataframe with index that contains '-'
df_with_special = pd.DataFrame({
    'Gal(b1-3)GalNAc': [0.242959, 0.208267, 0.223529, 0.245893, 0.297072],
    'GalOS(b1-3)GalNAc': [0.007172, 0.012267, 0.017346, 0.004030, 0.006302],
    'Gal(b1-3)[Fuc(a1-?)]GalNAc': [0.140820, 0.183320, 0.182716, 0.202931, 0.160700],
    'Fuc(a1-2)Gal(b1-3)GalNAc': [1.811925, 1.782675, 1.249882, 1.189128, 1.221432],
    'Fuc(a1-?)[HexNAc(?1-?)]GalNAc': [0.098392, 0.079560, 0.063288, 0.047380, 0.051726]
}, index=['A-1', 'B-1', 'C-1', 'D-1', 'E-1'])

print("Heatmap with special character index:")
get_heatmap(df_with_special, motifs=True)

import pandas as pd
from glycowork.motif.analysis import get_differential_expression

data = {
    'Glycan': ['Gal(b1-3)GalNAc', 'GalOS(b1-3)GalNAc', 'Gal(b1-3)[Fuc(a1-?)]GalNAc'],
    'Sample1': [1.1, 0.2, 0.3],
    'Sample2': [1.2, 0.1, 0.2],
    'Sample3': [0.1, 1.8, 1.9],
    'Sample4': [0.2, 1.1, 1.2]
}
differential_glycomics_df = pd.DataFrame(data)

group1 = ['Sample1', 'Sample2']
group2 = ['Sample3', 'Sample4']

get_differential_expression(df = differential_glycomics_df,
                            group1 = group1,
                            group2 = group2,
                            motifs = True,
                            feature_set = ['exhaustive'],
                            paired = False,
                            min_samples = 0.1)

import pandas as pd
from glycowork.motif.analysis import get_differential_expression
from glycowork.motif.analysis import get_volcano
from glycowork.motif.draw import annotate_figure
from glycowork.motif.draw import GlycoDraw

data = {
    'Glycan': ['Gal(b1-3)GalNAc', 'GalOS(b1-3)GalNAc', 'Gal(b1-3)[Fuc(a1-?)]GalNAc', 'GlcNAc(b1-2)Man(a1-3)Man', 'Man(a1-6)[Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAc', 'Neu5Ac(a2-6)Gal(b1-4)GlcNAc(b1-2)Man', 'Neu5Ac(a2-3)Gal(b1-3)GalNAc'],
    'Sample1': [1.1, 0.2, 0.3, 0.5, 0.7, 1.0, 0.6],
    'Sample2': [1.2, 0.1, 0.2, 0.4, 0.8, 0.9, 0.5],
    'Sample3': [0.1, 1.8, 1.9, 0.3, 0.6, 0.8, 1.2],
    'Sample4': [0.2, 1.1, 1.2, 0.2, 0.5, 0.7, 1.1],
    'Sample5': [1.3, 0.3, 0.4, 0.6, 0.9, 1.1, 0.7],
    'Sample6': [1.4, 0.4, 0.5, 0.7, 1.0, 1.2, 0.8],
    'Sample7': [0.3, 1.9, 2.0, 0.4, 0.7, 0.9, 1.3],
    'Sample8': [0.4, 1.2, 1.3, 0.3, 0.6, 0.8, 1.2]
}
differential_glycomics_df = pd.DataFrame(data)

# Define the groups
group1 = ['Sample1', 'Sample2', 'Sample5', 'Sample6']
group2 = ['Sample3', 'Sample4', 'Sample7', 'Sample8']

differential_expression = get_differential_expression(df = differential_glycomics_df,
                            group1 = group1,
                            group2 = group2,
                            motifs = True,
                            feature_set = ['terminal1', 'terminal2', 'terminal3'],
                            paired = False,
                            min_samples = 0.1)

print(differential_expression)

# Differential glycomics volcano plot
volcano = get_volcano(differential_expression,
                      y_thresh = 0.05,
                      annotate_volcano = True,
                      filepath = './volcano.svg')

# Setup
from glycowork.motif.analysis import get_heatmap
import pandas as pd
data = {
    'Glycan': ['Gal(b1-3)GalNAc', 'GalOS(b1-3)GalNAc', 'Gal(b1-3)[Fuc(a1-?)]GalNAc', 'GlcNAc(b1-2)Man(a1-3)Man', 'Man(a1-6)[Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAc', 'Neu5Ac(a2-6)Gal(b1-4)GlcNAc(b1-2)Man', 'Neu5Ac(a2-3)Gal(b1-3)GalNAc'],
    'Sample1': [1.1, 0.2, 0.3, 0.5, 0.7, 1.0, 0.6],
    'Sample2': [1.2, 0.1, 0.2, 0.4, 0.8, 0.9, 0.5],
    'Sample3': [0.1, 1.8, 1.9, 0.3, 0.6, 0.8, 1.2],
    'Sample4': [0.2, 1.1, 1.2, 0.2, 0.5, 0.7, 1.1],
    'Sample5': [1.3, 0.3, 0.4, 0.6, 0.9, 1.1, 0.7],
    'Sample6': [1.4, 0.4, 0.5, 0.7, 1.0, 1.2, 0.8],
    'Sample7': [0.3, 1.9, 2.0, 0.4, 0.7, 0.9, 1.3],
    'Sample8': [0.4, 1.2, 1.3, 0.3, 0.6, 0.8, 1.2]
}
data = pd.DataFrame(data)

# This works
get_heatmap(data,
           motifs = True,
           feature_set=[
               'terminal1',
               #'terminal2',
               #'terminal3'
                       ])

# This works
get_heatmap(data,
           motifs = True,
           feature_set=[
               #'terminal1',
               'terminal2',
               #'terminal3'
                       ])

# This fails
get_heatmap(data,
           motifs = True,
           feature_set=[
               'terminal1',
               'terminal2',
               #'terminal3'
                       ])

# This works
get_heatmap(data,
           motifs = True,
           feature_set=[
               'terminal1',
               #'terminal2',
               'terminal3'
                       ])

# This works
get_heatmap(data,
           motifs = True,
           feature_set=[
               #'terminal1',
               'terminal2',
               'terminal3'
                       ])

# This fails
get_heatmap(data,
           motifs = True,
           feature_set=[
               'terminal1',
               'terminal2',
               'terminal3'
                       ])

import pandas as pd
from glycowork.motif.analysis import get_pca

# Sample data
data = {
    'Glycan': ['Gal(b1-3)GalNAc', 'GalOS(b1-3)GalNAc', 'Gal(b1-3)[Fuc(a1-?)]GalNAc', 'Fuc(a1-2)Gal(b1-3)GalNAc', 'Fuc(a1-?)[HexNAc(?1-?)]GalNAc'],
    'Sample1': [0.5, 0.3, 0.2, 0.7, 0.9],
    'Sample2': [0.4, 0.2, 0.3, 0.6, 0.8],
    'Sample3': [0.3, 0.1, 0.4, 0.5, 0.7]
}

# Create a DataFrame
df = pd.DataFrame(data)

# Perform PCA
get_pca(df, groups=None, motifs=True, feature_set=['known', 'exhaustive'], pc_x=1, pc_y=2, color=None, shape=None, filepath='', custom_motifs=[], transform=None, rarity_filter=0.05)

# Sample data
data = {
    'Glycan': ['Gal(b1-3)GalNAc', 'GalOS(b1-3)GalNAc', 'Gal(b1-3)[Fuc(a1-?)]GalNAc', 'Fuc(a1-2)Gal(b1-3)GalNAc', 'Fuc(a1-?)[HexNAc(?1-?)]GalNAc'],
    'Sample1': [0.5, 0.3, 0.2, 0.7, 0.9],
    'Sample2': [0.4, 0.2, 0.3, 0.6, 0.8],
    'Sample3': [0.3, 0.1, 0.4, 0.5, 0.7]
}

# Create a DataFrame
df = pd.DataFrame(data)

# Sample groups data
groups_data = {
    'id': ['Sample1', 'Sample2', 'Sample3'],
    'Treatment': ['Control', 'Treatment', 'Control']
}

# Create groups DataFrame
groups_df = pd.DataFrame(groups_data)

# Perform PCA
get_pca(df, groups=groups_df, motifs=True, feature_set=['known', 'exhaustive'], pc_x=1, pc_y=2, color='Treatment', shape=None, filepath='', custom_motifs=[], transform=None, rarity_filter=0.05)

# Sample data
data = {
    'Glycan': ['Gal(b1-3)GalNAc', 'GalOS(b1-3)GalNAc', 'Gal(b1-3)[Fuc(a1-?)]GalNAc', 'Fuc(a1-2)Gal(b1-3)GalNAc', 'Fuc(a1-?)[HexNAc(?1-?)]GalNAc'],
    'Sample1': [0.5, 0.3, 0.2, 0.7, 0.9],
    'Sample2': [0.4, 0.2, 0.3, 0.6, 0.8],
    'Sample3': [0.3, 0.1, 0.4, 0.5, 0.7]
}

# Create a DataFrame
df = pd.DataFrame(data)

# Create groups DataFrame
groups_df = pd.DataFrame(groups_data)

# Perform PCA
get_pca(df, groups = ['Control', 'Treatment', 'Control'], motifs=True, feature_set=['known', 'exhaustive'], pc_x=1, pc_y=2, shape=None, filepath='', custom_motifs=[], transform=None, rarity_filter=0.05)

# Setup
from glycowork.motif.analysis import get_heatmap
import pandas as pd
data = {
    'Glycan': ['Gal(b1-3)GalNAc', 'GalOS(b1-3)GalNAc', 'Gal(b1-3)[Fuc(a1-?)]GalNAc', 'GlcNAc(b1-2)Man(a1-3)Man', 'Man(a1-6)[Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAc', 'Neu5Ac(a2-6)Gal(b1-4)GlcNAc(b1-2)Man', 'Neu5Ac(a2-3)Gal(b1-3)GalNAc'],
    'Sample1': [1.1, 0.2, 0.3, 0.5, 0.7, 1.0, 0.6],
    'Sample2': [1.2, 0.1, 0.2, 0.4, 0.8, 0.9, 0.5],
    'Sample3': [0.1, 1.8, 1.9, 0.3, 0.6, 0.8, 1.2],
    'Sample4': [0.2, 1.1, 1.2, 0.2, 0.5, 0.7, 1.1],
    'Sample5': [1.3, 0.3, 0.4, 0.6, 0.9, 1.1, 0.7],
    'Sample6': [1.4, 0.4, 0.5, 0.7, 1.0, 1.2, 0.8],
    'Sample7': [0.3, 1.9, 2.0, 0.4, 0.7, 0.9, 1.3],
    'Sample8': [0.4, 1.2, 1.3, 0.3, 0.6, 0.8, 1.2]
}
data = pd.DataFrame(data)

# This works
get_heatmap(data,
           motifs = True,
           feature_set=['terminal1'])

# This fails
get_heatmap(data,
           motifs = True,
           feature_set=['terminal2'])

# This works
get_heatmap(data,
           motifs = True,
           feature_set=['terminal3'])

# This fails
get_heatmap(data,
           motifs = True,
           feature_set=['terminal1','terminal2','terminal3'])

import pandas as pd
from glycowork.motif.analysis import get_differential_expression
from glycowork.motif.analysis import get_volcano
from glycowork.motif.draw import annotate_figure
from glycowork.motif.draw import GlycoDraw

data = {
    'Glycan': ['Gal(b1-3)GalNAc', 'GalOS(b1-3)GalNAc', 'Gal(b1-3)[Fuc(a1-?)]GalNAc', 'GlcNAc(b1-2)Man(a1-3)Man', 'Man(a1-6)[Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAc', 'Neu5Ac(a2-6)Gal(b1-4)GlcNAc(b1-2)Man', 'Neu5Ac(a2-3)Gal(b1-3)GalNAc'],
    'Sample1': [1.1, 0.2, 0.3, 0.5, 0.7, 1.0, 0.6],
    'Sample2': [1.2, 0.1, 0.2, 0.4, 0.8, 0.9, 0.5],
    'Sample3': [0.1, 1.8, 1.9, 0.3, 0.6, 0.8, 1.2],
    'Sample4': [0.2, 1.1, 1.2, 0.2, 0.5, 0.7, 1.1],
    'Sample5': [1.3, 0.3, 0.4, 0.6, 0.9, 1.1, 0.7],
    'Sample6': [1.4, 0.4, 0.5, 0.7, 1.0, 1.2, 0.8],
    'Sample7': [0.3, 1.9, 2.0, 0.4, 0.7, 0.9, 1.3],
    'Sample8': [0.4, 1.2, 1.3, 0.3, 0.6, 0.8, 1.2]
}
differential_glycomics_df = pd.DataFrame(data)

# Define the groups
group1 = ['Sample1', 'Sample2', 'Sample5', 'Sample6']
group2 = ['Sample3', 'Sample4', 'Sample7', 'Sample8']

differential_expression = get_differential_expression(df = differential_glycomics_df,
                            group1 = group1,
                            group2 = group2,
                            motifs = True,
                            feature_set = ['terminal1'],
                            paired = False,
                            min_samples = 0.1)

print(differential_expression)

construct_network(["Gal(b1)GalNAc"])

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[135], line 2
      1 # Top most expressed glycans
----> 2 construct_network(["Gal(b1)GalNAc"])

File ~\anaconda3\Lib\site-packages\glycowork\network\biosynthesis.py:663, in construct_network(glycans, allowed_ptms, edge_type, permitted_roots, abundances)
    661 network.remove_edges_from(to_remove)
    662 # Create edge and node labels
--> 663 nx.set_edge_attributes(network, {el: find_diff(el[0], el[1], graph_dic) for el in network.edges()}, 'diffs')
    664 virtual_labels = {k: (1 if k in virtual_nodes else 0) for k in network.nodes()}
    665 nx.set_node_attributes(network, virtual_labels, 'virtual')

File ~\anaconda3\Lib\site-packages\glycowork\network\biosynthesis.py:663, in <dictcomp>(.0)
    661 network.remove_edges_from(to_remove)
    662 # Create edge and node labels
--> 663 nx.set_edge_attributes(network, {el: find_diff(el[0], el[1], graph_dic) for el in network.edges()}, 'diffs')
    664 virtual_labels = {k: (1 if k in virtual_nodes else 0) for k in network.nodes()}
    665 nx.set_node_attributes(network, virtual_labels, 'virtual')

File ~\anaconda3\Lib\site-packages\glycowork\network\biosynthesis.py:180, in find_diff(glycan_a, glycan_b, graph_dic)
    178 matched = subgraph_isomorphism(graphs[0], graphs[1], return_matches = True)
    179 if not isinstance(matched, bool) and matched[0]:
--> 180   return graph_to_string(graphs[0].subgraph([k for k in graphs[0].nodes() if k not in matched[1][0]]))
    181 else:
    182   return 'disregard'

File ~\anaconda3\Lib\site-packages\glycowork\motif\graph.py:450, in graph_to_string(graph)
    448   return parts[:parts.rfind('{')] + parts[parts.rfind('{')+1:]
    449 else:
--> 450   return graph_to_string_int(graph)

File ~\anaconda3\Lib\site-packages\glycowork\motif\graph.py:426, in graph_to_string_int(graph)
    424 if ')(' in nodes and ((nodes.index(')(') < nodes.index('(')) or (nodes[:nodes.index(')(')].count(')') == nodes[:nodes.index(')(')].count('('))):
    425   nodes = nodes.replace(')(', '(', 1)
--> 426 return canonicalize_iupac(nodes.strip('()'))

File ~\anaconda3\Lib\site-packages\glycowork\motif\processing.py:824, in canonicalize_iupac(glycan)
    822   return
    823 elif ((glycan[-1].isdigit() and bool(re.search("[A-Z]", glycan))) or (glycan[-2].isdigit() and glycan[-1] == ']') or glycan.endswith('B') or glycan.endswith("LacDiNAc")) and 'e' not in glycan and '-' not in glycan:
--> 824   glycan = oxford_to_iupac(glycan)
    825 # Canonicalize usage of monosaccharides and linkages
    826 replace_dic = {'Nac': 'NAc', 'AC': 'Ac', 'Nc': 'NAc', 'NeuAc': 'Neu5Ac', 'NeuNAc': 'Neu5Ac', 'NeuGc': 'Neu5Gc',
    827                '\u03B1': 'a', '\u03B2': 'b', 'N(Gc)': 'NGc', 'GL': 'Gl', 'GaN': 'GalN', '(9Ac)': '9Ac',
    828                'KDN': 'Kdn', 'OSO3': 'S', '-O-Su-': 'S', '(S)': 'S', 'SO3-': 'S', 'SO3(-)': 'S', 'H2PO3': 'P', '(P)': 'P',
    829                '–': '-', ' ': '', ',': '-', 'α': 'a', 'β': 'b', 'ß': 'b', '.': '', '((': '(', '))': ')', '→': '-',
    830                'Glcp': 'Glc', 'Galp': 'Gal', 'Manp': 'Man', 'Fucp': 'Fuc', 'Neup': 'Neu', 'a?': 'a1',
    831                '5Ac4Ac': '4Ac5Ac', '(-)': '(?1-?)'}

File ~\anaconda3\Lib\site-packages\glycowork\motif\processing.py:715, in oxford_to_iupac(oxford)
    710 oxford_wo_branches = bracket_removal(oxford)
    711 branches = {"A": int(oxford_wo_branches[oxford_wo_branches.index("A")+1]) if "A" in oxford_wo_branches and oxford_wo_branches[oxford_wo_branches.index("A")+1] != "c" else 0,
    712             "G": int(oxford_wo_branches[oxford_wo_branches.index("G")+1]) if "G" in oxford_wo_branches and oxford_wo_branches[oxford_wo_branches.index("G")+1] != "a" else 0,
    713             "S": int(oxford_wo_branches[oxford_wo_branches.index("S")+1]) if "S" in oxford_wo_branches and oxford_wo_branches[oxford_wo_branches.index("S")+1] != "g" else 0}
    714 extras = {"Sg": int(oxford_wo_branches[oxford_wo_branches.index("Sg")+2]) if "Sg" in oxford_wo_branches else 0,
--> 715           "Ga": int(oxford_wo_branches[oxford_wo_branches.index("Ga")+2]) if "Ga" in oxford_wo_branches else 0,
    716           "Lac": int(oxford_wo_branches[oxford_wo_branches.index("Lac")+3]) if "Lac" in oxford_wo_branches and oxford_wo_branches[oxford_wo_branches.index("Lac")+3] != "D" else 0,
    717           "LacDiNAc": 1 if "LacDiN" in oxford_wo_branches else 0}
    718 specified_linkages = {'Neu5Ac(a2-?)': oxford[oxford.index("S")+2:] if branches['S'] else []}
    719 specified_linkages = {k: [int(n) for n in v[:v.index(']')].split(',')] for k, v in specified_linkages.items() if v}

ValueError: invalid literal for int() with base 10: 'l'

construct_network(["Gal(b1-?)GalNAc"])

from glycowork.motif.annotate import annotate_dataset

annotate_dataset(glycans = ['Gal(b1-3)GalNAc', 
                            'GalOS(b1-3)GalNAc', 
                            'Gal(b1-3)[Fuc(a1-?)]GalNAc', 
                            'GlcNAc(b1-2)Man(a1-3)Man', 
                            'Man(a1-6)[Man(a1-3)]Man(b1-4)GlcNAc(b1-4)GlcNAc', 
                            'Neu5Ac(a2-6)Gal(b1-4)GlcNAc(b1-2)Man', 
                            'Neu5Ac(a2-3)Gal(b1-3)GalNAc'],
                feature_set = ['exhaustive'])

ModuleNotFoundError                       Traceback (most recent call last)
Input In [15], in <cell line: 1>()
----> 1 from glycowork.motif import analysis

File /opt/homebrew/Caskroom/miniforge/base/envs/alive/lib/python3.8/site-packages/glycowork/motif/analysis.py:12, in <module>
      9 from sklearn.manifold import TSNE
     11 from glycowork.glycan_data.loader import lib, glycan_emb, df_species
---> 12 from glycowork.motif.annotate import annotate_dataset, link_find
     13 from glycowork.motif.graph import subgraph_isomorphism
     16 def get_pvals_motifs(df, glycan_col_name = 'glycan', label_col_name = 'target',
     17                      libr = None, thresh = 1.645, sorting = True,
     18                      feature_set = ['exhaustive'], extra = 'termini',
     19                      wildcard_list = [], multiple_samples = False,
     20                      motifs = None, estimate_speedup = False):

File /opt/homebrew/Caskroom/miniforge/base/envs/alive/lib/python3.8/site-packages/glycowork/motif/annotate.py:7, in <module>
      4 import re
      6 from glycowork.glycan_data.loader import lib, linkages, motif_list, find_nth, unwrap
----> 7 from glycowork.motif.graph import subgraph_isomorphism, generate_graph_features, glycan_to_nxGraph, try_string_conversion, compare_glycans
      8 from glycowork.motif.processing import small_motif_find
     11 def convert_to_counts_glycoletter(glycan, libr = None):

File /opt/homebrew/Caskroom/miniforge/base/envs/alive/lib/python3.8/site-packages/glycowork/motif/graph.py:7, in <module>
      5 import numpy as np
      6 import pandas as pd
----> 7 from scipy.sparse.linalg.eigen.arpack import eigsh
      9 def character_to_label(character, libr = None):
     10   """tokenizes character by indexing passed library\n
     11   | Arguments:
     12   | :-
   (...)
     17   | Returns index of character in library
     18   """

ModuleNotFoundError: No module named 'scipy.sparse.linalg.eigen.arpack'; 'scipy.sparse.linalg.eigen' is not a package

anyio=3.5.0=py38h10201cd_0
appnope=0.1.2=py38h10201cd_2
argon2-cffi=21.3.0=pyhd8ed1ab_0
argon2-cffi-bindings=21.2.0=py38hea4295b_1
asttokens=2.0.5=pyhd8ed1ab_0
attrs=21.4.0=pyhd8ed1ab_0
babel=2.9.1=pyh44b312d_0
backcall=0.2.0=pyh9f0ad1d_0
backports=1.0=py_2
backports.functools_lru_cache=1.6.4=pyhd8ed1ab_0
beautifulsoup4=4.10.0=pyha770c72_0
bleach=4.1.0=pyhd8ed1ab_0
brotli=1.0.9=h3422bc3_6
brotli-bin=1.0.9=h3422bc3_6
brotlipy=0.7.0=py38hea4295b_1003
c-ares=1.18.1=h3422bc3_0
ca-certificates=2021.10.8=h4653dfc_0
cachecontrol=0.12.11=pypi_0
cached-property=1.5.2=hd8ed1ab_1
cached_property=1.5.2=pyha770c72_1
certifi=2021.10.8=py38h10201cd_2
cffi=1.15.0=py38hc67bbb8_0
charset-normalizer=2.0.12=pyhd8ed1ab_0
click=8.1.2=pypi_0
cryptography=36.0.1=py38h10d4710_0
cycler=0.11.0=pyhd8ed1ab_0
cython=0.29.28=pypi_0
debugpy=1.5.1=py38h6f2b01f_0
decorator=5.1.1=pyhd8ed1ab_0
defusedxml=0.7.1=pyhd8ed1ab_0
emperor=1.0.3=pypi_0
entrypoints=0.4=pyhd8ed1ab_0
et-xmlfile=1.1.0=pypi_0
executing=0.8.3=pyhd8ed1ab_0
flit-core=3.7.1=pyhd8ed1ab_0
fonttools=4.30.0=py38h33210d7_0
freetype=2.10.4=h17b34a0_1
future=0.18.2=pypi_0
giflib=5.2.1=h27ca646_2
glycowork=0.4.0=pypi_0
h5py=3.6.0=nompi_py38hacf61ce_100
hdf5=1.12.1=nompi_hf9525e8_104
hdmedians=0.14.2=pypi_0
icu=69.1=hbdafb3b_0
idna=3.3=pyhd8ed1ab_0
importlib-metadata=4.11.3=py38h10201cd_0
importlib_resources=5.4.0=pyhd8ed1ab_0
ipykernel=6.9.2=py38h2cb4d76_0
ipython=8.1.1=py38h10201cd_0
ipython_genutils=0.2.0=py_1
ipywidgets=7.6.5=pyhd8ed1ab_0
jbig=2.1=h3422bc3_2003
jedi=0.18.1=py38h10201cd_0
jinja2=3.0.3=pyhd8ed1ab_0
joblib=1.1.0=pyhd8ed1ab_0
jpeg=9e=h3422bc3_0
json5=0.9.5=pyh9f0ad1d_0
jsonschema=4.4.0=pyhd8ed1ab_0
jupyter=1.0.0=py38h10201cd_7
jupyter_client=7.1.2=pyhd8ed1ab_0
jupyter_console=6.4.3=pyhd8ed1ab_0
jupyter_core=4.9.2=py38h10201cd_0
jupyter_server=1.15.3=pyhd8ed1ab_0
jupyterlab=3.3.2=pyhd8ed1ab_0
jupyterlab_pygments=0.1.2=pyh9f0ad1d_0
jupyterlab_server=2.10.3=pyhd8ed1ab_0
jupyterlab_widgets=1.0.2=pyhd8ed1ab_0
kiwisolver=1.3.2=py38h1670459_1
krb5=1.19.3=hf9b2bbe_0
lcms2=2.12=had6a04f_0
lerc=3.0=hbdafb3b_0
libblas=3.9.0=13_osxarm64_openblas
libbrotlicommon=1.0.9=h3422bc3_6
libbrotlidec=1.0.9=h3422bc3_6
libbrotlienc=1.0.9=h3422bc3_6
libcblas=3.9.0=13_osxarm64_openblas
libcurl=7.82.0=hb0e6552_0
libcxx=13.0.1=h6a5c8ee_0
libdeflate=1.10=h3422bc3_0
libedit=3.1.20191231=hc8eb9b7_2
libev=4.33=h642e427_1
libffi=3.4.2=h3422bc3_5
libgfortran=5.0.0.dev0=11_0_1_hf114ba7_23
libgfortran5=11.0.1.dev0=hf114ba7_23
liblapack=3.9.0=13_osxarm64_openblas
libnghttp2=1.47.0=he723fca_0
libopenblas=0.3.18=openmp_h5dd58f0_0
libpng=1.6.37=hf7e6567_2
libsodium=1.0.18=h27ca646_1
libssh2=1.10.0=hb80f160_2
libtiff=4.3.0=h77dc3b6_3
libuv=1.43.0=h3422bc3_0
libwebp=1.2.2=h0d20362_0
libwebp-base=1.2.2=h3422bc3_1
libxcb=1.13=h9b22ae9_1004
libzlib=1.2.11=hee7b306_1013
llvm-openmp=13.0.1=h455960f_1
lockfile=0.12.2=pypi_0
lz4-c=1.9.3=hbdafb3b_1
markupsafe=2.1.0=py38h33210d7_1
matplotlib-base=3.5.1=py38hb140015_0
matplotlib-inline=0.1.3=pyhd8ed1ab_0
mistune=0.8.4=py38hea4295b_1005
mpld3=0.5.7=pypi_0
msgpack=1.0.3=pypi_0
munkres=1.1.4=pyh9f0ad1d_0
natsort=8.1.0=pypi_0
nbclassic=0.3.6=pyhd8ed1ab_0
nbclient=0.5.13=pyhd8ed1ab_0
nbconvert=6.4.4=py38h10201cd_0
nbformat=5.2.0=pyhd8ed1ab_0
ncurses=6.3=hc470f4d_0
nest-asyncio=1.5.4=pyhd8ed1ab_0
networkx=2.8=pyhd8ed1ab_0
nodejs=17.4.0=habd0e26_0
notebook=6.4.9=pyha770c72_0
notebook-shim=0.1.0=pyhd8ed1ab_0
numpy=1.22.3=py38hf29d37f_0
openjpeg=2.4.0=h062765e_1
openpyxl=3.0.9=pypi_0
openssl=1.1.1n=h90dfc92_0
packaging=21.3=pyhd8ed1ab_0
pandas=1.4.1=py38h3777fb4_0
pandocfilters=1.5.0=pyhd8ed1ab_0
parso=0.8.3=pyhd8ed1ab_0
patsy=0.5.2=pyhd8ed1ab_0
pexpect=4.8.0=pyh9f0ad1d_2
pickleshare=0.7.5=py_1003
pillow=9.0.1=py38h7ff1586_2
pip=22.0.4=pyhd8ed1ab_0
prometheus_client=0.13.1=pyhd8ed1ab_0
prompt-toolkit=3.0.27=pyha770c72_0
prompt_toolkit=3.0.27=hd8ed1ab_0
psutil=5.9.0=py38hea4295b_0
pthread-stubs=0.4=h27ca646_1001
ptitprince=0.2.5=pypi_0
ptyprocess=0.7.0=pyhd3deb0d_0
pure_eval=0.2.2=pyhd8ed1ab_0
pycparser=2.21=pyhd8ed1ab_0
pygments=2.11.2=pyhd8ed1ab_0
pyhamcrest=2.0.3=pypi_0
pyopenssl=22.0.0=pyhd8ed1ab_0
pyparsing=3.0.7=pyhd8ed1ab_0
pyrsistent=0.18.1=py38hea4295b_0
pysocks=1.7.1=py38h10201cd_4
python=3.8.12=hab31e5c_3_cpython
python-dateutil=2.8.2=pyhd8ed1ab_0
python-graphviz=0.19.1=pypi_0
python_abi=3.8=2_cp38
pytz=2021.3=pyhd8ed1ab_0
pyzmq=22.3.0=py38h51b17a6_1
readline=8.1=hedafd6a_0
regex=2022.3.15=pypi_0
requests=2.27.1=pyhd8ed1ab_0
scikit-bio=0.5.7=pypi_0
scikit-learn=1.0.2=py38h2cd4032_0
scipy=1.8.0=py38hd0c9ec0_1
seaborn=0.11.2=hd8ed1ab_0
seaborn-base=0.11.2=pyhd8ed1ab_0
send2trash=1.8.0=pyhd8ed1ab_0
setuptools=60.9.3=py38h10201cd_0
six=1.16.0=pyh6c4a22f_0
sklearn=0.0=pypi_0
sniffio=1.2.0=py38h10201cd_2
soupsieve=2.3.1=pyhd8ed1ab_0
sqlite=3.37.0=h72a2b83_0
stack_data=0.2.0=pyhd8ed1ab_0
statsmodels=0.13.2=py38h691f20f_0
terminado=0.13.3=py38h10201cd_0
testpath=0.6.0=pyhd8ed1ab_0
threadpoolctl=3.1.0=pyh8a188c0_0
tk=8.6.12=he1e0b03_0
torch=1.11.0=pypi_0
tornado=6.1=py38hea4295b_2
traitlets=5.1.1=pyhd8ed1ab_0
typing-extensions=4.2.0=pypi_0
unicodedata2=14.0.0=py38hea4295b_0
urllib3=1.26.8=pyhd8ed1ab_1
wcwidth=0.2.5=pyh9f0ad1d_2
webencodings=0.5.1=py_1
websocket-client=1.3.1=pyhd8ed1ab_0
wheel=0.37.1=pyhd8ed1ab_0
widgetsnbextension=3.5.2=py38h10201cd_1
xgboost=1.6.0=pypi_0
xlrd=2.0.1=pypi_0
xorg-libxau=1.0.9=h27ca646_0
xorg-libxdmcp=1.1.3=h27ca646_0
xz=5.2.5=h642e427_1
zeromq=4.3.4=hbdafb3b_1
zipp=3.7.0=pyhd8ed1ab_1
zlib=1.2.11=hee7b306_1013
zstd=1.5.2=h861e0a7_0

from glycowork.ml.models import SweetNet, init_weights, trained_SweetNet
/usr/share/miniconda/lib/python3.8/site-packages/glycowork/ml/models.py:15: in <module>
    trained_SweetNet = torch.load(data_path)
/usr/share/miniconda/lib/python3.8/site-packages/torch/serialization.py:712: in load
    return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)
/usr/share/miniconda/lib/python3.8/site-packages/torch/serialization.py:1046: in _load
    result = unpickler.load()
/usr/share/miniconda/lib/python3.8/site-packages/torch/serialization.py:1016: in persistent_load
    load_tensor(dtype, nbytes, key, _maybe_decode_ascii(location))
/usr/share/miniconda/lib/python3.8/site-packages/torch/serialization.py:1001: in load_tensor
    wrap_storage=restore_location(storage, location),
/usr/share/miniconda/lib/python3.8/site-packages/torch/serialization.py:1[76](https://github.com/ilsenatorov/rindti/runs/6470382314?check_suite_focus=true#step:5:76): in default_restore_location
    result = fn(storage, location)
/usr/share/miniconda/lib/python3.8/site-packages/torch/serialization.py:152: in _cuda_deserialize
    device = validate_cuda_device(location)
/usr/share/miniconda/lib/python3.8/site-packages/torch/serialization.py:136: in validate_cuda_device
    raise RuntimeError('Attempting to deserialize object on a CUDA '
E   RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU.
Error: Process completed with exit code 4.