import matplotlib as mpl
import matplotlib.pyplot as plt
from crispy import CrispyPlot
from cancer_proteomics.notebooks import DataImport
RPATH = pkg_resources.resource_filename("cancer_proteomics", "plots/")
# ### Imports
# CMP samplesheet
cmp = DataImport.read_cmp_samplesheet()
cmp["CCLE_ID_SHORT"] = cmp["CCLE_ID"].apply(
lambda v: v.split("_")[0] if str(v).lower() != 'nan' else np.nan)
# Proteomics
prot = DataImport.read_protein_matrix(map_protein=True)
# Read proteomics BROAD (Proteins x Cell lines)
prot_broad = DataImport.read_protein_matrix_broad()
prot_broad = prot_broad.rename(columns=cmp.set_index("CCLE_ID")["model_id"])
# Read Transcriptomics
gexp = DataImport.read_gene_matrix()
# Read CRISPR
crispr = DataImport.read_crispr_matrix()
crispr = crispr.rename(columns=cmp.set_index("BROAD_ID")["model_id"])
# Read Methylation
methy = DataImport.read_methylation_matrix()
LOG = logging.getLogger("cancer_proteomics")
DPATH = pkg_resources.resource_filename("data", "/")
PPIPATH = pkg_resources.resource_filename("data", "ppi/")
TPATH = pkg_resources.resource_filename("tables", "/")
RPATH = pkg_resources.resource_filename("cancer_proteomics", "plots/")
# ### Imports
# PPI
ppi = PPI(ddir=PPIPATH).build_string_ppi(score_thres=900)
# Read samplesheet
ss = DataImport.read_samplesheet()
# Read proteomics (Proteins x Cell lines)
prot = DataImport.read_protein_matrix(map_protein=True, min_measurements=300)
# Read Transcriptomics
gexp = DataImport.read_gene_matrix()
# Read CRISPR
crispr = DataImport.read_crispr_matrix()
crispr_institute = DataImport.read_crispr_institute()[crispr.columns]
crispr_skew = crispr.apply(skew, axis=1, nan_policy="omit").astype(float)
# Read Drug-response
drespo = DataImport.read_drug_response(min_measurements=300)
dtargets = DataImport.read_drug_target()
drespo_skew = drespo.apply(skew, axis=1, nan_policy="omit").astype(float)
# ### Gene expression dimension reduction