#!/usr/bin/env python
# coding: utf-8

# # NK cell annotations
# 
# In this notebook, we upload cell type annotations assigned by our domain experts. These annotations were generated using markers and by interactive investigation of our .h5ad files.
# 
# As these are derived from our .h5ad files, we'll connect them to the HISE analysis chain by chaching those files and using `hisepy.upload.upload_files()`.

# In[1]:


import hisepy
from datetime import date


# For NK cells, this was done in two steps - all NK cells, and a focused, iterative round of clustering to better identify separate a subset of CD56dim cell types.
# 
# Here, we'll combine these steps to build the link in our reproducibility chain.

# In[2]:


in_files = [
    'b2a3d1ba-312d-41ec-9e51-8ef4c33192fe', # NK cells
    '9b961a21-482c-44a6-ab08-823ed33257a4'  # CD56dim NK cells
]
hise_res = hisepy.reader.cache_files(in_files)


# In[3]:


out_files = [
    'annotations/pbmc_ref_nk_annotations_2024-03-04.csv',
    'annotations/pbmc_ref_nk-cells-dim_annotations_2024-03-04.csv'
]


# In[4]:


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


# In[5]:


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


# In[6]:


import session_info
session_info.show()


# In[ ]: