diff --git a/pills/README.md b/pills/README.md
new file mode 100644
index 0000000..253a6ff
--- /dev/null
+++ b/pills/README.md
@@ -0,0 +1,3 @@
+# Readme
+
+This library contains basic examples that show how to interact and make use of the JUMP dataset.
diff --git a/pills/poetry.lock b/pills/poetry.lock
new file mode 100644
index 0000000..f1dd634
--- /dev/null
+++ b/pills/poetry.lock
@@ -0,0 +1,3796 @@
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+
+[[package]]
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+
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+testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
+
+[metadata]
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diff --git a/pills/pyproject.toml b/pills/pyproject.toml
new file mode 100644
index 0000000..609480e
--- /dev/null
+++ b/pills/pyproject.toml
@@ -0,0 +1,26 @@
+[tool.poetry]
+name = "pills"
+version = "0.0.0"
+description = "Examples that show how to interact with JUMP data"
+authors = ["Alan Munoz"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.9"
+boto3 = ">=1.33.1"
+matplotlib = "^3.8.2"
+polars = "^0.19.19"
+pyarrow = "^14.0.1"
+s3fs = "^2023.12.1"
+seaborn = "^0.13.2"
+
+[tool.poetry.group.dev.dependencies]
+jupyter = "^1.0.0"
+jupytext = "^1.15.0"
+pytest = "^7.4.1"
+ruff-lsp = "^0.0.48"
+ruff = "<0.2.0"
+
+[tool.jupytext.formats]
+"notebooks/" = ".qmd"
+"scripts/" = "py:percent"
diff --git a/pills/scripts/basic.py b/pills/scripts/basic.py
new file mode 100644
index 0000000..69ecbd7
--- /dev/null
+++ b/pills/scripts/basic.py
@@ -0,0 +1,65 @@
+#!/usr/bin/env jupyter
+# ---
+# jupyter:
+#   jupytext:
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.16.1
+#   kernelspec:
+#     display_name: Python 3
+#     language: python
+#     name: python3
+# ---
+# %% Overview [markdown]
+# # Basic JUMP data access
+# This is a tutorial on how to access
+# We will use polars to fetch the data frame lazily, with the help of s3fs and pyarrow.
+# We prefer lazy loading because the data can be too big to be handled in memory.
+# %% Imports
+import polars as pl
+from pyarrow.dataset import dataset
+from s3fs import S3FileSystem
+
+# %% [markdown]
+# The shapes of the available datasets are:
+# - crispr: Knock-out genetic perturbations.
+# - orf: Overexpression genetic perturbations.
+# - compounds: Chemical genetic perturbations.
+#
+# The aws paths of the dataframes are shown below:
+# %% Paths
+prefix = (
+    "s3://cellpainting-gallery/cpg0016-jump-integrated/source_all/workspace/profiles"
+)
+filepaths = dict(
+    crispr=f"{prefix}/chandrasekaran_2024_0000000/crispr/wellpos_cellcount_mad_outlier_nan_featselect_harmony.parquet",
+    orf=f"{prefix}/chandrasekaran_2024_0000000/orf/wellpos_cellcount_mad_outlier_nan_featselect_harmony.parquet",
+    compound=f"{prefix}/arevalo_2023_e834481/compound/mad_int_featselect_harmony.parquet/",
+)
+
+
+# %% [markdown]
+# We use a S3FileSystem to avoid the need of credentials.
+# %%
+def lazy_load(path: str) -> pl.DataFrame:
+    fs = S3FileSystem(anon=True)
+    myds = dataset(path, filesystem=fs)
+    df = pl.scan_pyarrow_dataset(myds)
+    return df
+
+
+# %% [markdown]
+# We will lazy load the data to visualise its columns
+# %%
+print("Width, or number of columns.")
+for name, path in filepaths.items():
+    data = lazy_load(path)
+    metadata_cols = [col for col in data.columns if col.startswith("Metadata")]
+    print(f"{name}: {data.width}, containing {len(metadata_cols)} metadata columns")
+
+# %% [markdown]
+# Let us now focus on the crispr dataset
+# %%
+data = lazy_load(filepaths["crispr"])
diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 0000000..d43bdf9
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,3398 @@
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+
+[[package]]
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+version = "3.17.0"
+description = "Backport of pathlib-compatible object wrapper for zip files"
+optional = false
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+    {file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"},
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+[package.extras]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
+testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
+
+[metadata]
+lock-version = "2.0"
+python-versions = ">=3.9,<3.11"
+content-hash = "5441151ed40090dcf1e2dc8ae8f14b5320f420bfbef3c2bae4c64fe6789a7c30"
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..42315f9
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,36 @@
+[tool.poetry]
+name = "jump_stories"
+version = "0.1.0"
+description = ""
+authors = ["Alan"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=3.9,<3.11"
+PyYAML = "5.3.1"
+# plotly = "4.14.3"
+# dvc-s3 = "^2.23.0"
+jupyter = "^1.0.0"
+scikit-learn = "^1.3.0"
+scipy = ">=1.8.1"
+matplotlib = "^3.7.3"
+seaborn = "^0.12.2"
+broad-babel = "^0.1.9"
+copairs = {git = "https://github.com/cytomining/copairs.git", rev = "v0.4.0-alpha"}
+numpy = "*"
+fastcluster = "^1.2.6"
+pandas = "*"
+pyarrow = "^14.0.1"
+joblib = "^1.3.2"
+adbc-driver-sqlite = "^0.8.0"
+
+
+[tool.poetry.group.dev.dependencies]
+jupytext = "^1.15.2"
+ipdb = "^0.13.13"
+pyflakes = "^3.1.0"
+ruff-lsp = "^0.0.48"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
diff --git a/workspace/analysis/CD44_HAS2/1_correlations.py b/workspace/analysis/CD44_HAS2/1_correlations.py
new file mode 100644
index 0000000..f651b96
--- /dev/null
+++ b/workspace/analysis/CD44_HAS2/1_correlations.py
@@ -0,0 +1,88 @@
+#!/usr/bin/env jupyter
+# ---
+# jupyter:
+#   jupytext:
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.15.2
+#   kernelspec:
+#     display_name: Python 3
+#     language: python
+#     name: python3
+# ---
+# %% [Markdown]
+"""
+Find correlations between CD44 and HAS2. Question inspired by Prof. Chonghui Chen from Baylor College of Medicine during ASCB2 2023.
+"""
+
+from itertools import product
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+from broad_babel.query import run_query
+from copairs.compute import pairwise_cosine
+
+from utils import get_figs_dir, load_path
+
+figs_dir = get_figs_dir()
+df = load_path("../../profiles/harmonized_no_sphering_profiles.parquet")
+
+genes_of_interest = ("CD44", "HAS2")
+cd44_crispr = run_query(query="CD44", input_column="standard_key", output_column="*")
+
+gene_matches = {pert: dict() for pert in genes_of_interest}
+for gene in genes_of_interest:
+    results = list(
+        map(
+            lambda x: x,
+            run_query(query=gene, input_column="standard_key", output_column="*"),
+        )
+    )
+    for pert_type in ("crispr", "orf"):
+        result_array = [x[1] for x in results if x[0] == pert_type]
+        if len(result_array):
+            gene_matches[gene][pert_type] = result_array[0]
+
+# %% Calculate the correlations between has2 and cd44 based on CRISPR data
+
+
+feat_cols = [x for x in df.columns if not x.startswith("Metadata")]
+translator_crispr_jump_gene = {v.get("crispr"): k for k, v in gene_matches.items()}
+subset = df.loc[df["Metadata_JCP2022"].isin(translator_crispr_jump_gene)]
+subset.set_index(
+    subset["Metadata_JCP2022"].map(translator_crispr_jump_gene),
+    inplace=True,
+)
+
+data_subset_no_features = subset[feat_cols]
+n_samples = len(subset)
+all_pair_ixs = np.array(list(product(range(n_samples), range(n_samples))))
+cosine_distance = pairwise_cosine(
+    data_subset_no_features.values, all_pair_ixs, batch_size=min(200, n_samples)
+).reshape((n_samples, n_samples))
+cosine_df = (
+    pd.DataFrame(
+        data=cosine_distance,
+        index=subset.index,
+        columns=subset.index,
+    )
+    .sort_index(axis=0)
+    .sort_index(axis=1)
+)
+sns.heatmap(
+    cosine_df,
+    robust=True,
+)
+plt.tight_layout()
+plt.savefig(
+    figs_dir / f"heatmap_cosdist_harmony_{'_'.join(genes_of_interest).lower() }.png",
+    dpi=300,
+)
+plt.close()
+
+# %% TODO Once acquired, use ORF data to have a look at these profiles.
diff --git a/workspace/analysis/CD44_HAS2/utils.py b/workspace/analysis/CD44_HAS2/utils.py
new file mode 100644
index 0000000..e76d40d
--- /dev/null
+++ b/workspace/analysis/CD44_HAS2/utils.py
@@ -0,0 +1,14 @@
+#!/usr/bin/env jupyter
+
+import pandas as pd
+from pathlib import Path
+
+
+def load_path(path: str or Path):
+    return pd.read_parquet(Path(path))
+
+
+def get_figs_dir():
+    figs_dir = Path("../../figs")
+    figs_dir.mkdir(exist_ok=True)
+    return figs_dir
diff --git a/workspace/analysis/MYT1_RNF41/1_correlations.py b/workspace/analysis/MYT1_RNF41/1_correlations.py
new file mode 100644
index 0000000..8ca9742
--- /dev/null
+++ b/workspace/analysis/MYT1_RNF41/1_correlations.py
@@ -0,0 +1,86 @@
+#!/usr/bin/env jupyter
+#!/usr/bin/env jupyter
+# ---
+# jupyter:
+#   jupytext:
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.15.2
+#   kernelspec:
+#     display_name: Python 3
+#     language: python
+#     name: python3
+# ---
+from itertools import product
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+from broad_babel.query import run_query
+from copairs.compute import pairwise_cosine
+
+dataset_filepath = (
+    Path("..") / ".." / "profiles" / "harmonized_no_sphering_profiles.parquet"
+)
+figs_dir = Path("../figs")
+figs_dir.mkdir(exist_ok=True)
+
+df = pd.read_parquet(dataset_filepath)
+
+genes_of_interest = ("RNF41", "MYT1")
+# cd44_crispr = run_query(query="CD44", input_column="standard_key", output_column="*")
+
+gene_matches = {pert: dict() for pert in genes_of_interest}
+for gene in genes_of_interest:
+    results = list(
+        map(
+            lambda x: x,
+            run_query(query=gene, input_column="standard_key", output_column="*"),
+        )
+    )
+    for pert_type in ("crispr", "orf"):
+        result_array = [x[1] for x in results if x[0] == pert_type]
+        if len(result_array):
+            gene_matches[gene][pert_type] = result_array[0]
+
+
+# %% Calculate the correlations of myt1 and rnf41
+# They were reported to interact by collaborators, and Ardigen found them to correlate
+
+feat_cols = [x for x in df.columns if not x.startswith("Metadata")]
+translator_crispr_jump_gene = {v.get("crispr"): k for k, v in gene_matches.items()}
+subset = df.loc[df["Metadata_JCP2022"].isin(translator_crispr_jump_gene)]
+subset.set_index(
+    subset["Metadata_JCP2022"].map(translator_crispr_jump_gene),
+    inplace=True,
+)
+
+data_subset_no_features = subset[feat_cols]
+n_samples = len(subset)
+all_pair_ixs = np.array(list(product(range(n_samples), range(n_samples))))
+cosine_distance = pairwise_cosine(
+    data_subset_no_features.values, all_pair_ixs, batch_size=min(200, n_samples)
+).reshape((n_samples, n_samples))
+cosine_df = (
+    pd.DataFrame(
+        data=cosine_distance,
+        index=subset.index,
+        columns=subset.index,
+    )
+    .sort_index(axis=0)
+    .sort_index(axis=1)
+)
+sns.heatmap(
+    cosine_df,
+    robust=True,
+)
+plt.tight_layout()
+plt.savefig(
+    figs_dir / f"heatmap_cosdist_harmony_{'_'.join(genes_of_interest).lower() }.png",
+    dpi=300,
+)
+plt.close()
diff --git a/workspace/analysis/RAB40B/compare_features.py b/workspace/analysis/RAB40B/compare_features.py
new file mode 100644
index 0000000..77d45a7
--- /dev/null
+++ b/workspace/analysis/RAB40B/compare_features.py
@@ -0,0 +1,72 @@
+#!/usr/bin/env jupyter
+
+# Linked issue: https://github.com/broadinstitute/2023_12_JUMP_data_only_vignettes/issues/4 apply_scaler,
+# Compare the four genes of interest that have been shown as anticorrelated:
+# - Cluster A:
+#  - RAB40B
+#  - RAB40C
+# - Cluster B:
+#  - INSYN1
+#  - PIK3R3
+
+from pathlib import Path
+
+import pandas as pd  # TODO stick to polars
+import polars as pl
+import scipy.stats as stats
+from statsmodels.stats.multitest import multipletests
+
+fpath = Path(
+    "/dgx1nas1/storage/data/shared/morphmap_profiles/orf/full_profiles_cc_adj_mean_corr.parquet"
+)
+genes_of_interest = ("RAB40B", "RAB40C", "INSYN1", "PIK3R3")
+
+
+df = pl.read_parquet(fpath)
+sub_df = df.filter(pl.col("Metadata_Symbol").is_in(genes_of_interest))
+groups = dict(
+    (x, f"{'RAB40B/C' if x.startswith('RAB') else 'INS/PIK'}")
+    for x in genes_of_interest
+)
+
+clusters = sub_df.with_columns(
+    pl.col("Metadata_Symbol").replace(groups).alias("Metadata_Cluster")
+)
+
+
+medians = clusters.group_by("Metadata_Cluster").median()
+diff = medians.select(pl.all().exclude("^Metadata.*$").diff())
+feature_diff = diff.filter(~pl.all_horizontal(pl.all().is_null())).melt()
+feature_diff.sort(by="value").write_csv("feature_diffs.csv")
+
+
+cluster_a = clusters.filter(pl.col("Metadata_Cluster") == "RAB40B/C").select(
+    pl.all().exclude("^Metadata.*$")
+)
+cluster_b = clusters.filter(pl.col("Metadata_Cluster") == "INS/PIK").select(
+    pl.all().exclude("^Metadata.*$")
+)
+results = []
+for feature in cluster_a.columns:
+    cluster_A = cluster_a.get_column(feature)
+    cluster_B = cluster_b.get_column(feature)
+
+    # Use t-test or Mann-Whitney U test based on data distribution
+    t_stat, p_val = stats.ttest_ind(cluster_A, cluster_B, equal_var=False)
+
+    results.append({"Feature": feature, "T-Statistic": t_stat, "P-Value": p_val})
+
+
+results_df = pd.DataFrame(results)
+
+# drop rows with NaN
+results_df = results_df.dropna()
+
+# Apply multiple testing correction
+
+corrected_pvals = multipletests(results_df["P-Value"], method="fdr_bh")[1]
+results_df["Corrected P-Value"] = corrected_pvals
+
+# Filter significant features
+significant_features = results_df[results_df["Corrected P-Value"] < 0.05]
+significant_features.to_csv("significant_features.csv")
diff --git a/workspace/analysis/RAB40B/feature_diffs.csv b/workspace/analysis/RAB40B/feature_diffs.csv
new file mode 100644
index 0000000..f76c12c
--- /dev/null
+++ b/workspace/analysis/RAB40B/feature_diffs.csv
@@ -0,0 +1,4764 @@
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diff --git a/workspace/analysis/RAB40B/significant_features.csv b/workspace/analysis/RAB40B/significant_features.csv
new file mode 100644
index 0000000..c30e8a6
--- /dev/null
+++ b/workspace/analysis/RAB40B/significant_features.csv
@@ -0,0 +1,1041 @@
+,Feature,T-Statistic,P-Value,Corrected P-Value
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diff --git a/workspace/analysis/distances/gen_cosine_crispr_db_torch.py b/workspace/analysis/distances/gen_cosine_crispr_db_torch.py
new file mode 100644
index 0000000..f49b603
--- /dev/null
+++ b/workspace/analysis/distances/gen_cosine_crispr_db_torch.py
@@ -0,0 +1,225 @@
+#!/usr/bin/env jupyter
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+#     language: python
+#     name: python3
+# ---
+
+"""
+Calculate cosine similarity of the CRISPR profiles using GPU,
+then wrangle information and produce an explorable data frame.
+
+The GPU section is based off this function
+https://github.com/UKPLab/sentence-transformers/blob/master/sentence_transformers/util.py#L31
+"""
+from itertools import cycle
+from pathlib import Path
+
+import numpy as np
+import polars as pl
+import torch
+from broad_babel.query import run_query
+from torch import Tensor, device
+
+from utils import batch_processing, parallel
+
+device = device("cuda" if torch.cuda.is_available() else "cpu")
+
+assert device.type == "cuda", "Not running on GPUs"
+
+
+def get_first_last_n(tensor: Tensor, n: int):
+    return tensor[:, [range(n), range(-n - 1, -1)]].reshape(-1, 2 * n)
+
+
+@batch_processing
+def jcp_to_standard(x):
+    return run_query(query=x, input_column="JCP2022", output_column="standard_key")[0][
+        0
+    ]
+
+
+@batch_processing
+def jcp_to_ncbi(x):
+    return run_query(query=x, input_column="JCP2022", output_column="NCBI_Gene_ID")[0][
+        0
+    ]
+
+
+def cos_sim(a: Tensor, b: Tensor) -> Tensor:
+    """
+    Computes the cosine similarity cos_sim(a[i], b[j]) for all i and j.
+
+    :return: Matrix with res[i][j]  = cos_sim(a[i], b[j])
+    """
+    a_norm = torch.nn.functional.normalize(a, p=2, dim=1)
+    b_norm = torch.nn.functional.normalize(b, p=2, dim=1)
+    return torch.mm(a_norm, b_norm.transpose(0, 1))
+
+
+filename = "harmonized_no_sphering_profiles"
+dir_path = Path("../../profiles/")
+profiles_path = dir_path / f"{filename}.parquet"
+checkpoint_path = Path("jcp_df.parquet")
+
+# %% Set names
+jcp_col = "Metadata_JCP2022"  # Name of columns in input data frames
+url_col = "Metadata_image"  # Must start with "Metadata"
+match_jcp_col = "Match"
+match_url_col = "Match Example"
+std_outname = "Gene/Compound"
+ext_links_col = "Match resources"
+ncbi_formatter = '{{"href": "https://www.ncbi.nlm.nih.gov/gene/{}", "label":"NCBI"}}'
+url_template = (
+    '"https://phenaid.ardigen.com/static-jumpcpexplorer/' 'images/{}_{{}}.jpg"'
+)
+img_formatter = '{{"img_src": {}, "href": {}, "width": 200}}'
+n_vals_used = 25
+
+# %% Load Metadata
+df = pl.read_parquet(profiles_path)
+
+# %% add build url from individual wells
+df = df.with_columns(
+    pl.concat_str(
+        pl.col("Metadata_Source"),
+        pl.col("Metadata_Plate"),
+        pl.col("Metadata_Well"),
+        separator="/",
+    )
+    .map_elements(lambda x: url_template.format(x))
+    .alias(url_col)
+)
+grouped = df.group_by("Metadata_JCP2022")
+med = grouped.median()
+meta = grouped.agg(pl.col("^Metadata_.*$").map_elements(cycle))
+
+urls = grouped.agg(pl.col(url_col).map_elements(cycle))
+
+for srs in meta.iter_columns():
+    med.replace_column(med.columns.index(srs.name), srs)
+
+vals = Tensor(med.select(pl.all().exclude("^Metadata.*$")).to_numpy())
+
+# Calculate cosine similarty
+cosine_sim = cos_sim(vals, vals)
+
+# Sort by correlation
+_sorted = cosine_sim.sort(axis=1)
+
+indices, values = [
+    get_first_last_n(x, n_vals_used) for x in (_sorted.indices, _sorted.values)
+]
+## Build a dataframe containing matches
+# Minimise repetition
+jcp_ids = urls.select(pl.col(jcp_col)).to_series().to_numpy().astype("<U15")
+moving_idx = np.repeat(cycle(range(9)), len(vals))
+urls_vals = urls.get_column(url_col).to_numpy()
+
+jcp_df = pl.DataFrame(
+    {
+        "JCP2022": np.repeat(jcp_ids, n_vals_used * 2),
+        match_jcp_col: jcp_ids[indices.flatten()].astype("<U15"),
+        "Similarity": values.flatten().numpy(),
+        url_col: [
+            img_formatter.format(img_src, img_src)
+            for x in urls.get_column(url_col).to_numpy()
+            for j in range(n_vals_used * 2)
+            if (img_src := next(x).format(j % 9))
+        ],
+        match_url_col: [
+            img_formatter.format(img_src, img_src)
+            for url, idx in zip(
+                urls_vals[indices.flatten()], moving_idx[indices.flatten()]
+            )
+            if (img_src := next(url).format(next(idx)))
+        ],
+    }
+)
+
+jcp_df.write_parquet(checkpoint_path, compression="zstd")
+
+# %% And then proceed to add gene names (TODO incorporate NCBI ids once they are in babel)
+uniq_jcp = jcp_df.select(pl.col("JCP2022")).to_series().unique().to_list()
+mapper_values = parallel(uniq_jcp, jcp_to_standard)
+mapper = {jcp: std for jcp, std in zip(uniq_jcp, mapper_values)}
+
+ncbi_mapper_values = parallel(uniq_jcp, jcp_to_ncbi)
+ncbi_mapper = {
+    jcp: ncbi_formatter.format(idx) for jcp, idx in zip(uniq_jcp, ncbi_mapper_values)
+}
+
+jcp_translated = jcp_df.with_columns(
+    pl.col("JCP2022").replace(mapper).alias(std_outname),
+    pl.col(match_jcp_col).replace(mapper),
+    pl.col(match_jcp_col).replace(ncbi_mapper).alias(ext_links_col),
+)
+
+
+matches = jcp_translated.with_columns(
+    (pl.col("Similarity") * 1000).cast(pl.Int16)
+).rename({url_col: f"{std_outname} Example"})
+
+order = [
+    std_outname,
+    "Match",
+    "Gene/Compound Example",
+    match_url_col,
+    "Similarity",
+    ext_links_col,
+    "JCP2022",
+]
+matches_translated = matches.select(order)
+
+final_output = "crispr.parquet"
+matches_translated.write_parquet(final_output, compression="zstd")
+
+
+"""
+
+# %% Upload to Zenodo
+# Automated uploads are not working
+# https://github.com/zenodo/zenodo/issues/2506
+
+from os.path import expanduser
+import requests
+with open(expanduser("~") + "/.zenodo") as f:
+    for line in f.readlines():
+        name, access_token = line.strip("\n").split(": ")
+
+params = {"access_token": access_token}
+bucket_url = "https://zenodo.org/api/files/9ffb1b5b-1e3c-4618-ad91-36255e87b57e"
+headers = {"Content-Type": "application/json"}
+with open(final_output, "rb") as fp:
+    r = requests.post(
+        f"{bucket_url}?access_token={params['access_token']}",
+        data=fp,
+        headers=headers,
+        params=params,
+    )
+
+
+depos_url =  "https://zenodo.org/api/deposit/depositions/10416605"
+depos=requests.get(depos_url,params=params)
+new = requests.post(depos.json()["links"]["newversion"], params=params, headers={"Accept": "application/json" ,**headers})
+
+d = r.post(depos_url.json(""))
+curl -X POST-H "Accept: application/json" -H "Content-Type: application/json" -H "Authorization: Bearer <TOKEN>" https://sandbox.zenodo.org/api/deposit/depositions/165/actions/newversion
+
+"""
+
+"""
+Future plans:
+- TODO Incorporate NCBI ids (already added on broad-babel)
+- TODO add differentiating features when compared to their controls
+- TODO add Average precision metrics
+"""