Assign B cell annotations¶

To assemble our annotations, we'll read our clustered B cell data and assign our expert annotations to those clusters. We'll then inspect the annotations in our UMAP projections, and output final labels for these cells.

For B cells, we have two groups of cells to label - Most of the B cells were assigned labels at one resolution, and the non-effector memory B cells were assigned labels after additional, iterative clustering. So, we'll load both of these sets, remove the memory cells from the rest of the B cells, assign identities based on clusters in each, and finally concatenate all of the cell barcodes.

In [1]:

import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
warnings.simplefilter(action='ignore', category=RuntimeWarning)

from datetime import date
import hisepy
import os
import pandas as pd
import scanpy as sc

Helper function¶

This function makes it easy to pull csv files stored in HISE as a pandas data.frame

In [2]:

def read_csv_uuid(csv_uuid):
    csv_path = '/home/jupyter/cache/{u}'.format(u = csv_uuid)
    if not os.path.isdir(csv_path):
        hise_res = hisepy.reader.cache_files([csv_uuid])
    csv_filename = os.listdir(csv_path)[0]
    csv_file = '{p}/{f}'.format(p = csv_path, f = csv_filename)
    df = pd.read_csv(csv_file, index_col = 0)
    return df

Read subclustering results from HISE¶

In [3]:

cell_class = 'b-cells'

In [4]:

h5ad_uuid = '99f83994-26ee-49af-a882-c1f2558daed2'
h5ad_path = '/home/jupyter/cache/{u}'.format(u = h5ad_uuid)

In [5]:

if not os.path.isdir(h5ad_path):
    hise_res = hisepy.reader.cache_files([h5ad_uuid])

In [6]:

h5ad_filename = os.listdir(h5ad_path)[0]
h5ad_file = '{p}/{f}'.format(p = h5ad_path, f = h5ad_filename)

In [7]:

adata = sc.read_h5ad(h5ad_file)

In [8]:

adata.shape

Out[8]:

(177994, 1209)

Read memory cell subclustering results from HISE¶

In [9]:

mem_uuid = '51838754-b378-4f13-b447-82511bcd0a66'
mem_path = '/home/jupyter/cache/{u}'.format(u = mem_uuid)

In [10]:

if not os.path.isdir(mem_path):
    hise_res = hisepy.reader.cache_files([mem_path])

In [11]:

mem_filename = os.listdir(mem_path)[0]
mem_file = '{p}/{f}'.format(p = mem_path, f = mem_filename)

In [12]:

mem_adata = sc.read_h5ad(mem_file)

In [13]:

mem_adata

Out[13]:

AnnData object with n_obs × n_vars = 47886 × 2110
    obs: 'barcodes', 'batch_id', 'cell_name', 'cell_uuid', 'chip_id', 'hto_barcode', 'hto_category', 'n_genes', 'n_mito_umis', 'n_reads', 'n_umis', 'original_barcodes', 'pbmc_sample_id', 'pool_id', 'well_id', 'sample.sampleKitGuid', 'cohort.cohortGuid', 'subject.subjectGuid', 'subject.biologicalSex', 'subject.race', 'subject.ethnicity', 'subject.birthYear', 'sample.visitName', 'sample.drawDate', 'file.id', 'subject.cmv', 'subject.bmi', 'celltypist.low', 'seurat.l1', 'seurat.l1.score', 'seurat.l2', 'seurat.l2.score', 'seurat.l2.5', 'seurat.l2.5.score', 'seurat.l3', 'seurat.l3.score', 'predicted_doublet', 'doublet_score', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'pct_counts_in_top_50_genes', 'pct_counts_in_top_100_genes', 'pct_counts_in_top_200_genes', 'pct_counts_in_top_500_genes', 'total_counts_mito', 'log1p_total_counts_mito', 'pct_counts_mito', 'leiden', 'leiden_resolution_1', 'leiden_resolution_1.5', 'leiden_resolution_2', 'ms_leiden_2', 'umap_1', 'umap_2', 'ms_leiden_2.5'
    var: 'mito', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'celltypist.low_colors', 'hvg', 'leiden', 'leiden_colors', 'leiden_resolution_2_colors', 'log1p', 'ms_leiden_2_colors', 'neighbors', 'pca', 'seurat.l2.5_colors', 'umap'
    obsm: 'X_pca', 'X_pca_harmony', 'X_umap'
    varm: 'PCs'
    obsp: 'connectivities', 'distances'

Subset non-memory cells¶

In [14]:

drop_lgl = [not x for x in adata.obs['barcodes'].isin(mem_adata.obs['barcodes'])]

In [15]:

nonmem_adata = adata[drop_lgl].copy()

In [16]:

nonmem_adata.shape

Out[16]:

(130108, 1209)

Read non-memory annotations¶

In [17]:

anno_uuid = 'aed5e5f4-1166-48c1-ad84-c92aee8edcf8'

In [18]:

anno = read_csv_uuid(anno_uuid)

downloading fileID: aed5e5f4-1166-48c1-ad84-c92aee8edcf8
Files have been successfully downloaded!

Assign non-memory labels¶

In [19]:

join_col = 'ms_leiden_2'

In [20]:

anno[join_col] = anno[join_col].astype('string').astype('category')

In [21]:

obs = nonmem_adata.obs

In [22]:

sum(obs[join_col].isin(anno[join_col]))

Out[22]:

In [23]:

nonmem_anno = obs.merge(anno, how = 'left', on = join_col)

In [24]:

nonmem_anno.head()

Out[24]:

	barcodes	batch_id	cell_name	cell_uuid	chip_id	hto_barcode	hto_category	n_genes	n_mito_umis	n_reads	...	log1p_total_counts_mito	pct_counts_mito	leiden	leiden_resolution_1	leiden_resolution_1.5	leiden_resolution_2	ms_leiden_2	AIFI_L3	AIFI_L1	AIFI_L2
0	cf72a14a48b611ea8957bafe6d70929e	B001	bestial_fireless_rottweiler	cf72a14a48b611ea8957bafe6d70929e	B001-P1C1	TGATGGCCTATTGGG	singlet	904	58	10269	...	4.077537	1.968103	7	7	14	18	21	T_B doublet	T_B doublet	T_B doublet
1	cf73500e48b611ea8957bafe6d70929e	B001	stressed_wishful_flea	cf73500e48b611ea8957bafe6d70929e	B001-P1C1	TGATGGCCTATTGGG	singlet	1085	71	12422	...	4.276666	1.978267	7	1	1	0	0	Core naive B cell	B cell	Naive B cell
2	cf73694a48b611ea8957bafe6d70929e	B001	silicone_nonsolar_snowgeese	cf73694a48b611ea8957bafe6d70929e	B001-P1C1	TGATGGCCTATTGGG	singlet	1152	198	11355	...	5.293305	5.899881	10	5	9	11	11	CD27+ effector B cell	B cell	Effector B cell
3	cf73fbee48b611ea8957bafe6d70929e	B001	luxuriant_overrated_gypsymoth	cf73fbee48b611ea8957bafe6d70929e	B001-P1C1	TGATGGCCTATTGGG	singlet	1769	245	18105	...	5.505332	4.552211	7	4	3	6	1	Transitional B cell	B cell	Transitional B cell
4	cf74ae2c48b611ea8957bafe6d70929e	B001	real_bighearted_kitty	cf74ae2c48b611ea8957bafe6d70929e	B001-P1C1	TGATGGCCTATTGGG	singlet	692	83	5468	...	4.430817	4.976019	7	1	1	0	0	Core naive B cell	B cell	Naive B cell

5 rows × 57 columns

Read memory annotations¶

In [25]:

mem_anno_uuid = 'afc4fe7f-6426-41ce-a10c-0dcdce422dd1'
mem_anno = read_csv_uuid(mem_anno_uuid)

downloading fileID: afc4fe7f-6426-41ce-a10c-0dcdce422dd1
Files have been successfully downloaded!

Assign memory labels¶

In [26]:

join_col = 'ms_leiden_2.5'

In [27]:

mem_anno[join_col] = mem_anno[join_col].astype('string').astype('category')

In [28]:

obs = mem_adata.obs

In [29]:

sum(obs[join_col].isin(mem_anno[join_col]))

Out[29]:

In [30]:

mem_anno = obs.merge(mem_anno, how = 'left', on = join_col)

In [31]:

mem_anno.head()

Out[31]:

	barcodes	batch_id	cell_name	cell_uuid	chip_id	hto_barcode	hto_category	n_genes	n_mito_umis	n_reads	...	leiden_resolution_1	leiden_resolution_1.5	leiden_resolution_2	ms_leiden_2	umap_1	umap_2	ms_leiden_2.5	AIFI_L3	AIFI_L1	AIFI_L2
0	cf720af048b611ea8957bafe6d70929e	B001	patterny_untalented_halcyon	cf720af048b611ea8957bafe6d70929e	B001-P1C1	TGATGGCCTATTGGG	singlet	928	66	7856	...	2	4	2	2	NaN	NaN	8	Core memory B cell	B cell	Memory B cell
1	cf78473048b611ea8957bafe6d70929e	B001	burly_backward_tuatara	cf78473048b611ea8957bafe6d70929e	B001-P1C1	TGATGGCCTATTGGG	singlet	1372	338	17586	...	3	6	13	13	NaN	NaN	5	Core memory B cell	B cell	Memory B cell
2	cf79279a48b611ea8957bafe6d70929e	B001	illadvised_applicable_annelid	cf79279a48b611ea8957bafe6d70929e	B001-P1C1	TGATGGCCTATTGGG	singlet	757	207	8651	...	3	7	10	13	NaN	NaN	8	Core memory B cell	B cell	Memory B cell
3	cf80e44e48b611ea8957bafe6d70929e	B001	frousy_vapourific_oropendola	cf80e44e48b611ea8957bafe6d70929e	B001-P1C1	TGATGGCCTATTGGG	singlet	776	70	5600	...	2	4	2	2	NaN	NaN	8	Core memory B cell	B cell	Memory B cell
4	cf81a58c48b611ea8957bafe6d70929e	B001	minor_clerkish_hartebeest	cf81a58c48b611ea8957bafe6d70929e	B001-P1C1	TGATGGCCTATTGGG	singlet	1244	151	10561	...	2	5	4	2	NaN	NaN	9	Early memory B cell	B cell	Memory B cell

5 rows × 60 columns

Concatenate annotations¶

In [32]:

anno = pd.concat([nonmem_anno, mem_anno], axis = 0)

In [33]:

anno = anno[['barcodes', 'AIFI_L1', 'AIFI_L2', 'AIFI_L3']]

Add to AnnData to preview assignments¶

In [34]:

anno = anno.set_index('barcodes')

In [35]:

obs = adata.obs
obs = obs.merge(anno, how = 'left', left_index = True, right_index = True)

In [36]:

adata.obs = obs

In [37]:

adata.obs.head()

Out[37]:

	barcodes	batch_id	cell_name	cell_uuid	chip_id	hto_barcode	hto_category	n_genes	n_mito_umis	n_reads	...	log1p_total_counts_mito	pct_counts_mito	leiden	leiden_resolution_1	leiden_resolution_1.5	leiden_resolution_2	ms_leiden_2	AIFI_L1	AIFI_L2	AIFI_L3
barcodes
cf720af048b611ea8957bafe6d70929e	cf720af048b611ea8957bafe6d70929e	B001	patterny_untalented_halcyon	cf720af048b611ea8957bafe6d70929e	B001-P1C1	TGATGGCCTATTGGG	singlet	928	66	7856	...	4.204693	2.798982	10	2	4	2	2	B cell	Memory B cell	Core memory B cell
cf72a14a48b611ea8957bafe6d70929e	cf72a14a48b611ea8957bafe6d70929e	B001	bestial_fireless_rottweiler	cf72a14a48b611ea8957bafe6d70929e	B001-P1C1	TGATGGCCTATTGGG	singlet	904	58	10269	...	4.077537	1.968103	7	7	14	18	21	T_B doublet	T_B doublet	T_B doublet
cf73500e48b611ea8957bafe6d70929e	cf73500e48b611ea8957bafe6d70929e	B001	stressed_wishful_flea	cf73500e48b611ea8957bafe6d70929e	B001-P1C1	TGATGGCCTATTGGG	singlet	1085	71	12422	...	4.276666	1.978267	7	1	1	0	0	B cell	Naive B cell	Core naive B cell
cf73694a48b611ea8957bafe6d70929e	cf73694a48b611ea8957bafe6d70929e	B001	silicone_nonsolar_snowgeese	cf73694a48b611ea8957bafe6d70929e	B001-P1C1	TGATGGCCTATTGGG	singlet	1152	198	11355	...	5.293305	5.899881	10	5	9	11	11	B cell	Effector B cell	CD27+ effector B cell
cf73fbee48b611ea8957bafe6d70929e	cf73fbee48b611ea8957bafe6d70929e	B001	luxuriant_overrated_gypsymoth	cf73fbee48b611ea8957bafe6d70929e	B001-P1C1	TGATGGCCTATTGGG	singlet	1769	245	18105	...	5.505332	4.552211	7	4	3	6	1	B cell	Transitional B cell	Transitional B cell

5 rows × 57 columns

In [38]:

sc.pl.umap(adata, color = ['AIFI_L1', 'AIFI_L2', 'AIFI_L3'], ncols = 1)

/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:394: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:394: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:394: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(

In [39]:

sc.pl.umap(adata, 
           color = ['leiden_resolution_1',
                    'leiden_resolution_1.5',
                    'leiden_resolution_2',
                    'ms_leiden_2'],
           ncols = 1)

/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:394: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:394: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:394: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(
/opt/conda/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:394: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(

Output final annotations¶

In [40]:

obs = adata.obs
obs = obs.reset_index(drop = True)

In [41]:

umap_mat = adata.obsm['X_umap']
umap_df = pd.DataFrame(umap_mat, columns = ['umap_1', 'umap_2'])
obs['umap_1'] = umap_df['umap_1']
obs['umap_2'] = umap_df['umap_2']

In [42]:

obs.head()

Out[42]:

	barcodes	batch_id	cell_name	cell_uuid	chip_id	hto_barcode	hto_category	n_genes	n_mito_umis	n_reads	...	leiden	leiden_resolution_1	leiden_resolution_1.5	leiden_resolution_2	ms_leiden_2	AIFI_L1	AIFI_L2	AIFI_L3	umap_1	umap_2
0	cf720af048b611ea8957bafe6d70929e	B001	patterny_untalented_halcyon	cf720af048b611ea8957bafe6d70929e	B001-P1C1	TGATGGCCTATTGGG	singlet	928	66	7856	...	10	2	4	2	2	B cell	Memory B cell	Core memory B cell	1.259842	-1.832500
1	cf72a14a48b611ea8957bafe6d70929e	B001	bestial_fireless_rottweiler	cf72a14a48b611ea8957bafe6d70929e	B001-P1C1	TGATGGCCTATTGGG	singlet	904	58	10269	...	7	7	14	18	21	T_B doublet	T_B doublet	T_B doublet	1.463564	11.663149
2	cf73500e48b611ea8957bafe6d70929e	B001	stressed_wishful_flea	cf73500e48b611ea8957bafe6d70929e	B001-P1C1	TGATGGCCTATTGGG	singlet	1085	71	12422	...	7	1	1	0	0	B cell	Naive B cell	Core naive B cell	-2.856547	-0.907700
3	cf73694a48b611ea8957bafe6d70929e	B001	silicone_nonsolar_snowgeese	cf73694a48b611ea8957bafe6d70929e	B001-P1C1	TGATGGCCTATTGGG	singlet	1152	198	11355	...	10	5	9	11	11	B cell	Effector B cell	CD27+ effector B cell	3.092754	-1.697405
4	cf73fbee48b611ea8957bafe6d70929e	B001	luxuriant_overrated_gypsymoth	cf73fbee48b611ea8957bafe6d70929e	B001-P1C1	TGATGGCCTATTGGG	singlet	1769	245	18105	...	7	4	3	6	1	B cell	Transitional B cell	Transitional B cell	-3.169807	2.543742

5 rows × 59 columns

In [43]:

out_dir = 'output'
if not os.path.isdir(out_dir):
    os.makedirs(out_dir)

In [44]:

obs_out_csv = '{p}/ref_pbmc_{c}_labeled_meta_umap_{d}.csv'.format(p = out_dir, c = cell_class, d = date.today())
obs.to_csv(obs_out_csv, index = False)

In [45]:

obs_out_parquet = '{p}/ref_pbmc_{c}_labeled_meta_umap_{d}.parquet'.format(p = out_dir, c = cell_class, d = date.today())
obs.to_parquet(obs_out_parquet, index = False)

In [46]:

bc_anno = obs[['barcodes', 'AIFI_L1', 'AIFI_L2', 'AIFI_L3']]

In [47]:

label_out_csv = '{p}/ref_pbmc_{c}_barcode_labels_{d}.csv'.format(p = out_dir, c = cell_class, d = date.today())
bc_anno.to_csv(label_out_csv, index = False)

In [48]:

label_out_parquet = '{p}/ref_pbmc_{c}_barcode_labels_{d}.parquet'.format(p = out_dir, c = cell_class, d = date.today())
bc_anno.to_parquet(label_out_parquet, index = False)

Upload annotations to HISE¶

Finally, we'll use hisepy.upload.upload_files() to send a copy of our output to HISE to use for downstream analysis steps.

In [50]:

study_space_uuid = '64097865-486d-43b3-8f94-74994e0a72e0'
title = 'B cell barcode annotations {d}'.format(d = date.today())

In [51]:

in_files = [h5ad_uuid, mem_uuid, anno_uuid, mem_anno_uuid]

In [52]:

in_files

Out[52]:

['99f83994-26ee-49af-a882-c1f2558daed2',
 '51838754-b378-4f13-b447-82511bcd0a66',
 'aed5e5f4-1166-48c1-ad84-c92aee8edcf8',
 'afc4fe7f-6426-41ce-a10c-0dcdce422dd1']

In [53]:

out_files = [obs_out_csv, obs_out_parquet,
             label_out_csv, label_out_parquet]

In [54]:

out_files

Out[54]:

['output/ref_pbmc_b-cells_labeled_meta_umap_2024-03-09.csv',
 'output/ref_pbmc_b-cells_labeled_meta_umap_2024-03-09.parquet',
 'output/ref_pbmc_b-cells_barcode_labels_2024-03-09.csv',
 'output/ref_pbmc_b-cells_barcode_labels_2024-03-09.parquet']

In [55]:

hisepy.upload.upload_files(
    files = out_files,
    study_space_id = study_space_uuid,
    title = title,
    input_file_ids = in_files
)

output/ref_pbmc_b-cells_labeled_meta_umap_2024-03-09.csv
output/ref_pbmc_b-cells_labeled_meta_umap_2024-03-09.parquet
output/ref_pbmc_b-cells_barcode_labels_2024-03-09.csv
output/ref_pbmc_b-cells_barcode_labels_2024-03-09.parquet
Cannot determine the current notebook.
1) /home/jupyter/scRNA-Reference-IH-A/05-Assembly/17-Python_assign_B_cells.ipynb
2) /home/jupyter/scRNA-Reference-IH-A/04-Annotation/12-Python_B_cell_annotations.ipynb
3) /home/jupyter/scRNA-Reference-IH-A/visualizations/04-Python_other_markers.ipynb
Please select (1-3)

you are trying to upload file_ids... ['output/ref_pbmc_b-cells_labeled_meta_umap_2024-03-09.csv', 'output/ref_pbmc_b-cells_labeled_meta_umap_2024-03-09.parquet', 'output/ref_pbmc_b-cells_barcode_labels_2024-03-09.csv', 'output/ref_pbmc_b-cells_barcode_labels_2024-03-09.parquet']. Do you truly want to proceed?

Out[55]:

{'trace_id': '55a58595-c889-403d-aa20-cd4abfd16e22',
 'files': ['output/ref_pbmc_b-cells_labeled_meta_umap_2024-03-09.csv',
  'output/ref_pbmc_b-cells_labeled_meta_umap_2024-03-09.parquet',
  'output/ref_pbmc_b-cells_barcode_labels_2024-03-09.csv',
  'output/ref_pbmc_b-cells_barcode_labels_2024-03-09.parquet']}

In [56]:

import session_info
session_info.show()

Out[56]:

Click to view session information

-----
anndata             0.10.3
hisepy              0.3.0
pandas              2.1.4
scanpy              1.9.6
session_info        1.0.0
-----

Click to view modules imported as dependencies

PIL                         10.0.1
anyio                       NA
arrow                       1.3.0
asttokens                   NA
attr                        23.2.0
attrs                       23.2.0
babel                       2.14.0
beatrix_jupyterlab          NA
brotli                      NA
cachetools                  5.3.1
certifi                     2024.02.02
cffi                        1.16.0
charset_normalizer          3.3.2
cloudpickle                 2.2.1
colorama                    0.4.6
comm                        0.1.4
cryptography                41.0.7
cycler                      0.10.0
cython_runtime              NA
dateutil                    2.8.2
db_dtypes                   1.1.1
debugpy                     1.8.0
decorator                   5.1.1
defusedxml                  0.7.1
deprecated                  1.2.14
exceptiongroup              1.2.0
executing                   2.0.1
fastjsonschema              NA
fqdn                        NA
google                      NA
greenlet                    2.0.2
grpc                        1.58.0
grpc_status                 NA
h5py                        3.10.0
idna                        3.6
igraph                      0.10.8
importlib_metadata          NA
ipykernel                   6.28.0
ipython_genutils            0.2.0
ipywidgets                  8.1.1
isoduration                 NA
jedi                        0.19.1
jinja2                      3.1.2
joblib                      1.3.2
json5                       NA
jsonpointer                 2.4
jsonschema                  4.20.0
jsonschema_specifications   NA
jupyter_events              0.9.0
jupyter_server              2.12.1
jupyterlab_server           2.25.2
jwt                         2.8.0
kiwisolver                  1.4.5
leidenalg                   0.10.1
llvmlite                    0.41.0
lz4                         4.3.2
markupsafe                  2.1.3
matplotlib                  3.8.0
matplotlib_inline           0.1.6
mpl_toolkits                NA
mpmath                      1.3.0
natsort                     8.4.0
nbformat                    5.9.2
numba                       0.58.0
numpy                       1.24.0
opentelemetry               NA
overrides                   NA
packaging                   23.2
parso                       0.8.3
pexpect                     4.8.0
pickleshare                 0.7.5
pkg_resources               NA
platformdirs                4.1.0
plotly                      5.18.0
prettytable                 3.9.0
prometheus_client           NA
prompt_toolkit              3.0.42
proto                       NA
psutil                      NA
ptyprocess                  0.7.0
pure_eval                   0.2.2
pyarrow                     13.0.0
pydev_ipython               NA
pydevconsole                NA
pydevd                      2.9.5
pydevd_file_utils           NA
pydevd_plugins              NA
pydevd_tracing              NA
pygments                    2.17.2
pynvml                      NA
pyparsing                   3.1.1
pyreadr                     0.5.0
pythonjsonlogger            NA
pytz                        2023.3.post1
referencing                 NA
requests                    2.31.0
rfc3339_validator           0.1.4
rfc3986_validator           0.1.1
rpds                        NA
scipy                       1.11.4
send2trash                  NA
shapely                     1.8.5.post1
six                         1.16.0
sklearn                     1.3.2
sniffio                     1.3.0
socks                       1.7.1
sql                         NA
sqlalchemy                  2.0.21
sqlparse                    0.4.4
stack_data                  0.6.2
sympy                       1.12
termcolor                   NA
texttable                   1.7.0
threadpoolctl               3.2.0
torch                       2.1.2+cu121
torchgen                    NA
tornado                     6.3.3
tqdm                        4.66.1
traitlets                   5.9.0
typing_extensions           NA
uri_template                NA
urllib3                     1.26.18
wcwidth                     0.2.12
webcolors                   1.13
websocket                   1.7.0
wrapt                       1.15.0
xarray                      2023.12.0
yaml                        6.0.1
zipp                        NA
zmq                         25.1.2
zoneinfo                    NA
zstandard                   0.22.0

-----
IPython             8.19.0
jupyter_client      8.6.0
jupyter_core        5.6.1
jupyterlab          4.1.2
notebook            6.5.4
-----
Python 3.10.13 | packaged by conda-forge | (main, Dec 23 2023, 15:36:39) [GCC 12.3.0]
Linux-5.15.0-1052-gcp-x86_64-with-glibc2.31
-----
Session information updated at 2024-03-09 05:02

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