Update tutorial after data structure change

docs/tutorials/tutorial_3k_tcr.md

@@ -34,6 +34,7 @@ from matplotlib import pyplot as plt, cm as mpl_cm
 from cycler import cycler
 
 sc.set_figure_params(figsize=(4, 4))
+sc.settings.verbosity = 2  # verbosity: errors (0), warnings (1), info (2), hints (3)
 ```
 
 ```python
@@ -317,10 +318,6 @@ This appoach was initially proposed as *TCRdist* by Dash et al. (:cite:`TCRdist`
 All cells with a distance between their CDR3 sequences lower than `cutoff` will be connected in the network.
 <!-- #endraw -->
 
-```python
-sc.settings.verbosity = 4
-```
-
 ```python
 ir.pp.ir_dist(
     adata,
@@ -359,10 +356,10 @@ When extracting the CDR3 sequences of clonotype cluster `159`, we retreive five
 ```python
 adata.obs.loc[adata.obs["cc_aa_alignment"] == "159", :].groupby(
     [
-        "IR_VJ_1_cdr3",
-        "IR_VJ_2_cdr3",
-        "IR_VDJ_1_cdr3",
-        "IR_VDJ_2_cdr3",
+        "IR_VJ_1_junction_aa",
+        "IR_VJ_2_junction_aa",
+        "IR_VDJ_1_junction_aa",
+        "IR_VDJ_2_junction_aa",
         "receptor_subtype",
     ],
     observed=True,
@@ -406,8 +403,8 @@ adata.obs.loc[
     [
         "cc_aa_alignment",
         "cc_aa_alignment_same_v",
-        "IR_VJ_1_v_gene",
-        "IR_VDJ_1_v_gene",
+        "IR_VJ_1_v_call",
+        "IR_VDJ_1_v_call",
     ],
 ].sort_values("cc_aa_alignment").drop_duplicates().reset_index(drop=True)
 ```
@@ -430,7 +427,7 @@ ir.tl.clonal_expansion(adata)
 The `clonotype_size` refers to the absolute number of cells in a clonotype.
 
 ```python
-sc.pl.umap(adata, color=["clonal_expansion", "clonotype_size"])
+sc.pl.umap(adata, color=["clonal_expansion", "clone_id_size"])
 ```
 
 <!-- #raw raw_mimetype="text/restructuredtext" -->
@@ -509,7 +506,7 @@ We use `max_col` to limit the plot to the 10 most abundant V-genes.
 
 ```python
 ax = ir.pl.group_abundance(
-    adata, groupby="IR_VJ_1_v_gene", target_col="cluster", normalize=True, max_cols=10
+    adata, groupby="IR_VJ_1_v_call", target_col="cluster", normalize=True, max_cols=10
 )
 ```
 
@@ -518,13 +515,13 @@ We can pre-select groups by filtering `adata`:
 ```python
 ax = ir.pl.group_abundance(
     adata[
-        adata.obs["IR_VDJ_1_v_gene"].isin(
+        adata.obs["IR_VDJ_1_v_call"].isin(
             ["TRBV20-1", "TRBV7-2", "TRBV28", "TRBV5-1", "TRBV7-9"]
         ),
         :,
     ],
     groupby="cluster",
-    target_col="IR_VDJ_1_v_gene",
+    target_col="IR_VDJ_1_v_call",
     normalize=True,
 )
 ```
@@ -575,13 +572,13 @@ A spectratype-plot by gene usage. To pre-select specific genes, we can simply fi
 ```python
 ir.pl.spectratype(
     adata[
-        adata.obs["IR_VDJ_1_v_gene"].isin(
+        adata.obs["IR_VDJ_1_v_call"].isin(
             ["TRBV20-1", "TRBV7-2", "TRBV28", "TRBV5-1", "TRBV7-9"]
         ),
         :,
     ],
-    cdr3_col="IR_VDJ_1_cdr3",
-    color="IR_VDJ_1_v_gene",
+    cdr3_col="IR_VDJ_1_junction_aa",
+    color="IR_VDJ_1_v_call",
     normalize="sample",
     fig_kws={"dpi": 120},
 )