fix: update jump-portrait dependency and fix image loading issues

Add note on casting of load_data_csv casting after cpg changes.

explanations/quirks_details.md

@@ -11,3 +11,4 @@ source_7 where the compounds were assayed at 0.625 uM (the goal being to assay s
 compounds at a low concentration in addition to the higher concentration used for most of data
 production). The positive control compounds in compound, ORF and CRISPR plates were assayed at 5
 uM. JUMP-Target-1-Compound and JUMP-Target-2-Compound plates were also assayed at 5 uM
+- Due to some plates having letters and numbers and others only numbers, be careful when loading multiple `load_data_csv`s. We treat all columns and strings to avoid any potential casting issue.

pyproject.toml

@@ -20,7 +20,7 @@ seaborn = "^0.13.2"
 biopython = "^1.84"
 broad-babel = ">=0.1.26"
 copairs = "^0.4.1"
-jump-portrait = "^0.0.26"
+jump-portrait = "^0.0.27"
 
 [tool.poetry.group.dev.dependencies]
 ipdb = "^0.13.13"

scripts/4_display_perturbation_images.py

@@ -21,6 +21,8 @@
 import polars as pl
 from jump_portrait.fetch import get_item_location_info, get_jump_image
 from matplotlib import pyplot as plt
+
+#
 # %% [markdown]
 # First, we need to get location information telling us where all images corresponding to a specific perturbation can be found. We will use the "get_item_location" function from jump_portrait for this.
 # Here we retrieve image locations for the "RAB30" gene:
@@ -36,6 +38,8 @@
 
 print(cmpd_info_byinchi.shape)
 print(cmpd_info_byjcp.shape)
+
+
 # %% [markdown]
 # There are 34 sites corresponding to this compound.
 # We've written a function to display all channels for a specific image. Note that this is just one possible way to display images - we've included the function here so that you can modify it to suit your own needs.
@@ -45,7 +49,7 @@ def display_site(
     batch: str,
     plate: str,
     well: str,
-    site: str,
+    site: int,
     label: str,
     int_percentile: float,
 ) -> None:
@@ -78,11 +82,11 @@ def display_site(
     counter = 0
 
     channel_rgb = {
-        "AGP": "#FF7F00", # Orange
-        "DNA": "#0000FF", # Blue
-        "ER": "#00FF00", # Green
-        "Mito": "#FF0000", # Red
-        "RNA": "#FFFF00", # Yellow
+        "AGP": "#FF7F00",  # Orange
+        "DNA": "#0000FF",  # Blue
+        "ER": "#00FF00",  # Green
+        "Mito": "#FF0000",  # Red
+        "RNA": "#FFFF00",  # Yellow
     }
 
     for ax, (channel, rgb) in zip(axes, channel_rgb.items()):
@@ -124,10 +128,18 @@ def display_site(
 
     # show plot
     plt.tight_layout()
+
+
 # %% [markdown]
 # We can get the required location parameters from the location info that we retrieved earlier. Here we get parameters for the first site in the JCP compound results:
 # %%
-source, batch, plate, well, site, *rest = cmpd_info_byjcp.row(0)
+(
+    source,
+    batch,
+    plate,
+    well,
+    site,
+) = cmpd_info_byjcp.select(pl.col(f"Metadata_{x}" for x in ("Source", "Batch", "Plate", "Well", "Site"))).row(0)
 # %% [markdown]
 # Next, we define the label and make the plot:
 # %%
@@ -145,9 +157,9 @@ def display_site(
 # %% [markdown]
 # Here, we plot one of the RAB30 ORF images:
 # %%
-source, batch, plate, well, site, *rest = gene_info.filter(
+source, batch, plate, well, site = gene_info.filter(
     pl.col("Metadata_PlateType") == "ORF"
-).row(0)
+).select(pl.col(f"Metadata_{x}" for x in ("Source", "Batch", "Plate", "Well", "Site"))).row(0)
 display_site(
     source,
     batch,
@@ -160,9 +172,9 @@ def display_site(
 # %% [markdown]
 # And for CRISPR:
 # %%
-source, batch, plate, well, site, *rest = gene_info.filter(
+source, batch, plate, well, site = gene_info.filter(
     pl.col("Metadata_PlateType") == "CRISPR"
-).row(0)
+).select(pl.col(f"Metadata_{x}" for x in ("Source", "Batch", "Plate", "Well", "Site"))).row(0)
 display_site(
     source,
     batch,