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WalkthroughThree new component options and corresponding internal names were added to the layout manager. Kernel density estimation (KDE) calculations for target and decoy proteoform Q-value distributions were implemented in the TNT parser, with results saved for later use. The data initialization logic was updated to support the new FDR plot component, fetching target and decoy density data. The Dockerfile was modified to restrict the Cython version, and the GitHub Actions workflow was updated to discard Docker build output. Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant UI
participant Backend
participant FileManager
participant Parser
User->>UI: Selects "Score Distribution Plot"
UI->>Backend: Requests data for 'id_fdr_plot'
Backend->>FileManager: Fetch 'density_id_target' and 'density_id_decoy'
FileManager-->>Backend: Returns density data
Backend->>UI: Sends density data for rendering
Note over Parser,FileManager: During parsing
Parser->>FileManager: Store 'density_id_target' and 'density_id_decoy' after KDE computation
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Actionable comments posted: 3
🧹 Nitpick comments (2)
src/parse/tnt.py (1)
168-188: Consider adding sample count for context in the returned dataframes.Including the count of samples used in the KDE calculation would provide valuable context for the visualization and could help users interpret the plots.
def fdr_density_distribution(df, logger=None): df = df[df['ProteoformLevelQvalue'] > 0] # Find density targets target_qscores = df[~df['accession'].str.startswith('DECOY_')]['ProteoformLevelQvalue'].dropna() if len(target_qscores) > 0: x_target = np.linspace(target_qscores.min(), target_qscores.max(), 200) kde_target = gaussian_kde(target_qscores) density_target = pd.DataFrame({'x': x_target, 'y': kde_target(x_target)}) + density_target['sample_count'] = len(target_qscores) else: density_target = pd.DataFrame(columns=['x', 'y']) + density_target['sample_count'] = 0 # Find density decoys (if present) decoy_qscores = df[df['accession'].str.startswith('DECOY_')]['ProteoformLevelQvalue'].dropna() if len(decoy_qscores) > 0: x_decoy = np.linspace(decoy_qscores.min(), decoy_qscores.max(), 200) kde_decoy = gaussian_kde(decoy_qscores) density_decoy = pd.DataFrame({'x': x_decoy, 'y': kde_decoy(x_decoy)}) + density_decoy['sample_count'] = len(decoy_qscores) else: density_decoy = pd.DataFrame(columns=['x', 'y']) + density_decoy['sample_count'] = 0 return density_target, density_decoysrc/render/initialize.py (1)
112-112: Consider adding a more descriptive title for the FDR Plot.The current title "FDR Plot" is generic. Consider using a more descriptive title like "Proteoform Q-value Distribution" to better describe the specific data being visualized.
- component_arguments = FDRPlotly(title="FDR Plot") + component_arguments = FDRPlotly(title="Proteoform Q-value Distribution")
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src/render/initialize.py (2)
src/workflow/FileManager.py (1)
get_results(403-445)src/render/components.py (1)
FDRPlotly(69-72)
src/parse/tnt.py (2)
src/parse/deconv.py (1)
fdr_density_distribution(113-130)src/workflow/FileManager.py (1)
store_data(308-323)
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🔇 Additional comments (5)
src/parse/tnt.py (2)
8-8: Added dependency for KDE calculation.The import of
gaussian_kdefromscipy.statsis correctly added to support the new kernel density estimation functionality.
163-165: Good integration of density estimation in the parseTnT workflow.The code computes density distributions for target and decoy proteoforms and stores them in the file manager with appropriate keys that will be used by the Score Distribution Plot component.
content/FLASHTnT/FLASHTnTLayoutManager.py (2)
15-17: Added new visualization component options.The three new visualization components have been appropriately added to the
COMPONENT_OPTIONSlist: Score Distribution Plot, MS1 raw heatmap, and MS1 deconvolved heatmap.
26-28: Added corresponding internal component identifiers.The three new internal component identifiers have been properly added to the
COMPONENT_NAMESlist, matching the order of the previously added component options.src/render/initialize.py (1)
107-112: Properly implemented Score Distribution Plot initialization.The implementation correctly retrieves the target and decoy density data computed by the
fdr_density_distributionfunction in thetnt.pyfile and configures the FDRPlotly component to display the data.
| def fdr_density_distribution(df, logger=None): | ||
| df = df[df['ProteoformLevelQvalue'] > 0] | ||
| # Find density targets | ||
| target_qscores = df[~df['accession'].str.startswith('DECOY_')]['ProteoformLevelQvalue'].dropna() | ||
| if len(target_qscores) > 0: | ||
| x_target = np.linspace(target_qscores.min(), target_qscores.max(), 200) | ||
| kde_target = gaussian_kde(target_qscores) | ||
| density_target = pd.DataFrame({'x': x_target, 'y': kde_target(x_target)}) | ||
| else: | ||
| density_target = pd.DataFrame(columns=['x', 'y']) | ||
|
|
||
| # Find density decoys (if present) | ||
| decoy_qscores = df[df['accession'].str.startswith('DECOY_')]['ProteoformLevelQvalue'].dropna() | ||
| if len(decoy_qscores) > 0: | ||
| x_decoy = np.linspace(decoy_qscores.min(), decoy_qscores.max(), 200) | ||
| kde_decoy = gaussian_kde(decoy_qscores) | ||
| density_decoy = pd.DataFrame({'x': x_decoy, 'y': kde_decoy(x_decoy)}) | ||
| else: | ||
| density_decoy = pd.DataFrame(columns=['x', 'y']) | ||
|
|
||
| return density_target, density_decoy No newline at end of file |
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🛠️ Refactor suggestion
Implement error handling for the KDE computation.
While the function correctly computes the kernel density estimates for target and decoy distributions, it could benefit from additional error handling. The gaussian_kde function may raise exceptions if the input data has specific characteristics (e.g., constant values).
def fdr_density_distribution(df, logger=None):
df = df[df['ProteoformLevelQvalue'] > 0]
# Find density targets
target_qscores = df[~df['accession'].str.startswith('DECOY_')]['ProteoformLevelQvalue'].dropna()
if len(target_qscores) > 0:
+ try:
x_target = np.linspace(target_qscores.min(), target_qscores.max(), 200)
kde_target = gaussian_kde(target_qscores)
density_target = pd.DataFrame({'x': x_target, 'y': kde_target(x_target)})
+ except Exception as e:
+ if logger:
+ logger.warning(f"Failed to compute KDE for target distribution: {str(e)}")
+ density_target = pd.DataFrame(columns=['x', 'y'])
else:
density_target = pd.DataFrame(columns=['x', 'y'])
# Find density decoys (if present)
decoy_qscores = df[df['accession'].str.startswith('DECOY_')]['ProteoformLevelQvalue'].dropna()
if len(decoy_qscores) > 0:
+ try:
x_decoy = np.linspace(decoy_qscores.min(), decoy_qscores.max(), 200)
kde_decoy = gaussian_kde(decoy_qscores)
density_decoy = pd.DataFrame({'x': x_decoy, 'y': kde_decoy(x_decoy)})
+ except Exception as e:
+ if logger:
+ logger.warning(f"Failed to compute KDE for decoy distribution: {str(e)}")
+ density_decoy = pd.DataFrame(columns=['x', 'y'])
else:
density_decoy = pd.DataFrame(columns=['x', 'y'])
return density_target, density_decoy📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def fdr_density_distribution(df, logger=None): | |
| df = df[df['ProteoformLevelQvalue'] > 0] | |
| # Find density targets | |
| target_qscores = df[~df['accession'].str.startswith('DECOY_')]['ProteoformLevelQvalue'].dropna() | |
| if len(target_qscores) > 0: | |
| x_target = np.linspace(target_qscores.min(), target_qscores.max(), 200) | |
| kde_target = gaussian_kde(target_qscores) | |
| density_target = pd.DataFrame({'x': x_target, 'y': kde_target(x_target)}) | |
| else: | |
| density_target = pd.DataFrame(columns=['x', 'y']) | |
| # Find density decoys (if present) | |
| decoy_qscores = df[df['accession'].str.startswith('DECOY_')]['ProteoformLevelQvalue'].dropna() | |
| if len(decoy_qscores) > 0: | |
| x_decoy = np.linspace(decoy_qscores.min(), decoy_qscores.max(), 200) | |
| kde_decoy = gaussian_kde(decoy_qscores) | |
| density_decoy = pd.DataFrame({'x': x_decoy, 'y': kde_decoy(x_decoy)}) | |
| else: | |
| density_decoy = pd.DataFrame(columns=['x', 'y']) | |
| return density_target, density_decoy | |
| def fdr_density_distribution(df, logger=None): | |
| df = df[df['ProteoformLevelQvalue'] > 0] | |
| # Find density targets | |
| target_qscores = df[~df['accession'].str.startswith('DECOY_')]['ProteoformLevelQvalue'].dropna() | |
| if len(target_qscores) > 0: | |
| try: | |
| x_target = np.linspace(target_qscores.min(), target_qscores.max(), 200) | |
| kde_target = gaussian_kde(target_qscores) | |
| density_target = pd.DataFrame({'x': x_target, 'y': kde_target(x_target)}) | |
| except Exception as e: | |
| if logger: | |
| logger.warning(f"Failed to compute KDE for target distribution: {e}") | |
| density_target = pd.DataFrame(columns=['x', 'y']) | |
| else: | |
| density_target = pd.DataFrame(columns=['x', 'y']) | |
| # Find density decoys (if present) | |
| decoy_qscores = df[df['accession'].str.startswith('DECOY_')]['ProteoformLevelQvalue'].dropna() | |
| if len(decoy_qscores) > 0: | |
| try: | |
| x_decoy = np.linspace(decoy_qscores.min(), decoy_qscores.max(), 200) | |
| kde_decoy = gaussian_kde(decoy_qscores) | |
| density_decoy = pd.DataFrame({'x': x_decoy, 'y': kde_decoy(x_decoy)}) | |
| except Exception as e: | |
| if logger: | |
| logger.warning(f"Failed to compute KDE for decoy distribution: {e}") | |
| density_decoy = pd.DataFrame(columns=['x', 'y']) | |
| else: | |
| density_decoy = pd.DataFrame(columns=['x', 'y']) | |
| return density_target, density_decoy |
| 'combined_spectrum', | ||
| 'id_fdr_plot', |
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💡 Verification agent
🧩 Analysis chain
Verify component mapping between options and names.
The component name 'combined_spectrum' at line 25 appears to map to 'Sequence tag view (Tag table needed)' in the options list based on position. Please verify this mapping is correct, as there seems to be a potential mismatch.
🏁 Script executed:
#!/bin/bash
# Check for potential mismatch between COMPONENT_OPTIONS and COMPONENT_NAMES
echo "Checking component mappings in FLASHTnTLayoutManager.py..."
echo "Component 'combined_spectrum' should map to 'Sequence tag view (Tag table needed)'"
# Verify the option at index 4 in both lists
grep -A 7 "COMPONENT_OPTIONS=\[" content/FLASHTnT/FLASHTnTLayoutManager.py | tail -n 7 | head -n 1
grep -A 6 "COMPONENT_NAMES=\[" content/FLASHTnT/FLASHTnTLayoutManager.py | tail -n 6 | head -n 1Length of output: 556
🏁 Script executed:
#!/bin/bash
# Extract full COMPONENT_OPTIONS and COMPONENT_NAMES lists for mapping review
echo "=== COMPONENT_OPTIONS ==="
grep -n "COMPONENT_OPTIONS" content/FLASHTnT/FLASHTnTLayoutManager.py -A 20
echo
echo "=== COMPONENT_NAMES ==="
grep -n "COMPONENT_NAMES" content/FLASHTnT/FLASHTnTLayoutManager.py -A 20Length of output: 12478
Fix misaligned COMPONENT_OPTIONS/COMPONENT_NAMES entries
The 5th and 6th elements in your arrays are currently swapped/misnamed:
- COMPONENT_OPTIONS[4] =
'Sequence tag view (Tag table needed)'↔ COMPONENT_NAMES[4] ='combined_spectrum' - COMPONENT_OPTIONS[5] =
'Score Distribution Plot'↔ COMPONENT_NAMES[5] ='id_fdr_plot'
These mappings don’t align semantically. Please update one or both lists so that:
'Sequence tag view (Tag table needed)'maps to a name like'sequence_tag_view''Score Distribution Plot'maps to something like'score_distribution_plot''combined_spectrum'pairs with its intended option label (e.g.'Combined spectrum')
Locations:
- content/FLASHTnT/FLASHTnTLayoutManager.py lines 10–18 (COMPONENT_OPTIONS)
- content/FLASHTnT/FLASHTnTLayoutManager.py lines 20–28 (COMPONENT_NAMES)
Suggested diff sketch:
COMPONENT_OPTIONS = [
…,
- 'Sequence tag view (Tag table needed)',
+ 'Combined spectrum', # new or corrected label
+ 'Sequence tag view (Tag table needed)',
'Score Distribution Plot',
…
]
COMPONENT_NAMES = [
…,
- 'combined_spectrum',
- 'id_fdr_plot',
+ 'combined_spectrum', # match new/renamed option above
+ 'sequence_tag_view', # maps to Sequence tag view
+ 'score_distribution_plot', # maps to Score Distribution Plot
…
]Committable suggestion skipped: line range outside the PR's diff.
| elif comp_name == 'id_fdr_plot': | ||
| data = file_manager.get_results(selected_data, ['density_id_target']) | ||
| data_to_send['density_target'] = data['density_id_target'] | ||
| data = file_manager.get_results(selected_data, ['density_id_decoy']) | ||
| data_to_send['density_decoy'] = data['density_id_decoy'] | ||
| component_arguments = FDRPlotly(title="FDR Plot") |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Consider adding error handling for missing density datasets.
The code doesn't have explicit error handling for cases where density_id_target or density_id_decoy datasets might be missing. Consider adding a try-except block or using the partial=True parameter in the get_results call to handle such cases gracefully.
elif comp_name == 'id_fdr_plot':
+ try:
data = file_manager.get_results(selected_data, ['density_id_target'])
data_to_send['density_target'] = data['density_id_target']
data = file_manager.get_results(selected_data, ['density_id_decoy'])
data_to_send['density_decoy'] = data['density_id_decoy']
component_arguments = FDRPlotly(title="FDR Plot")
+ except KeyError:
+ # Handle missing datasets
+ data_to_send['density_target'] = pd.DataFrame(columns=['x', 'y'])
+ data_to_send['density_decoy'] = pd.DataFrame(columns=['x', 'y'])
+ component_arguments = FDRPlotly(title="FDR Plot (No Data Available)")Alternatively, using the partial=True parameter:
elif comp_name == 'id_fdr_plot':
- data = file_manager.get_results(selected_data, ['density_id_target'])
- data_to_send['density_target'] = data['density_id_target']
- data = file_manager.get_results(selected_data, ['density_id_decoy'])
- data_to_send['density_decoy'] = data['density_id_decoy']
+ data = file_manager.get_results(selected_data, ['density_id_target', 'density_id_decoy'], partial=True)
+ data_to_send['density_target'] = data.get('density_id_target', pd.DataFrame(columns=['x', 'y']))
+ data_to_send['density_decoy'] = data.get('density_id_decoy', pd.DataFrame(columns=['x', 'y']))
component_arguments = FDRPlotly(title="FDR Plot")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| elif comp_name == 'id_fdr_plot': | |
| data = file_manager.get_results(selected_data, ['density_id_target']) | |
| data_to_send['density_target'] = data['density_id_target'] | |
| data = file_manager.get_results(selected_data, ['density_id_decoy']) | |
| data_to_send['density_decoy'] = data['density_id_decoy'] | |
| component_arguments = FDRPlotly(title="FDR Plot") | |
| elif comp_name == 'id_fdr_plot': | |
| # load both densities at once, allowing missing keys | |
| data = file_manager.get_results( | |
| selected_data, | |
| ['density_id_target', 'density_id_decoy'], | |
| partial=True | |
| ) | |
| data_to_send['density_target'] = data.get( | |
| 'density_id_target', | |
| pd.DataFrame(columns=['x', 'y']) | |
| ) | |
| data_to_send['density_decoy'] = data.get( | |
| 'density_id_decoy', | |
| pd.DataFrame(columns=['x', 'y']) | |
| ) | |
| component_arguments = FDRPlotly(title="FDR Plot") |
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Dockerfile (1)
84-84: Use a consistentpipinvocation for clarity.To ensure the right interpreter is used in both upgrade and install steps, consider invoking
pipviapython -m pip:-RUN pip install --upgrade pip && python -m pip install -U setuptools nose 'Cython<3.10' autowrap pandas 'numpy==1.26.4' pytest +RUN python -m pip install --upgrade pip && python -m pip install -U setuptools nose 'Cython<3.10' autowrap pandas 'numpy==1.26.4' pytest🧰 Tools
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84-84: Pin Cython version below 3.10 to avoid incompatibilities.Locking Cython to
<3.10here is a sensible precaution against breaking API changes introduced in Cython 3.10+. This aligns with the new visualization components’ dependencies.🧰 Tools
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Dockerfile (1)
84-84: Restrict Cython to <3.1 for compatibility
The updated command properly pinsCython<3.1, preventing potential breakages in the Cython extensions used by the new visualization components. Consider adding--no-cache-dirto the pip install to reduce image layer size and caching overhead, and optionally pin other critical Python dependencies (e.g., pandas, pytest) to specific versions for end-to-end reproducibility.🧰 Tools
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.github/workflows/build-docker-images.yml (2)
18-27: Enable Docker BuildKit and inline caching in CI
SettingDOCKER_BUILDKIT: 1andCOMPOSE_DOCKER_CLI_BUILD: 1along with--build-argflags ensures BuildKit features and caching are active during thedocker build. Consider using the docker/setup-buildx-action to standardize multi-platform builds across runners.🧰 Tools
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1-1: Declare Dockerfile syntax version for BuildKit features
Specifying# syntax=docker/dockerfile:1.4at the top ensures BuildKit directives are recognized.
13-13: Enable multi-platform builds in the JS stage
Using--platform=$BUILDPLATFORMon thenode:21image allows cross-architecture builds when invoked with BuildKit.
44-47: Add BuildKit-specific build arguments
IntroducingARG BUILDKIT_INLINE_CACHE=1andARG DOCKER_BUILDKIT=1enables inline caching and faster incremental builds under BuildKit.
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84-84: Pin Cython<3.1 to maintain compatibility; consider broader version pinning and cache control.The updated pip install step now pins
Cython<3.1, which is excellent to prevent future breakage when Cython 3.1 is released. For improved build reproducibility and smaller image layers, consider:
- Explicitly pinning versions for other packages (
setuptools,nose,autowrap,pandas,pytest).- Adding
--no-cache-dirto thepip installcommand.Example refactor:
RUN pip install --upgrade pip && \ - python -m pip install -U setuptools nose 'Cython<3.1' autowrap pandas 'numpy==1.26.4' pytest + python -m pip install --no-cache-dir \ + setuptools==<version> \ + nose==<version> \ + autowrap==<version> \ + 'Cython<3.1' \ + pandas==<version> \ + 'numpy==1.26.4' \ + pytest==<version>🧰 Tools
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[error] 84-84: Ensure the shebang uses an absolute path to the interpreter.
(SC2239)
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- GitHub Check: build-openms
- GitHub Check: build-full-app
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.github/workflows/build-docker-images.yml (1)
16-19: Streamline the build to target onlyrun-appThe new workflow step focuses on building the
run-appstage, which aligns with your multi-stage Dockerfile and speeds up CI by skipping earlier layers that aren’t needed for validation. This is a solid approach for rapid feedback.🧰 Tools
🪛 actionlint (1.7.4)
16-16: the runner of "actions/checkout@v3" action is too old to run on GitHub Actions. update the action's version to fix this issue
(action)
🪛 YAMLlint (1.35.1)
[error] 19-19: no new line character at the end of file
(new-line-at-end-of-file)
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🧹 Nitpick comments (1)
.github/workflows/build-docker-images.yml (1)
18-18: Add newline at end of file
YAML parsers typically expect a trailing newline. Please add a blank line at the end of this file to satisfy linting.🧰 Tools
🪛 YAMLlint (1.35.1)
[error] 18-18: no new line character at the end of file
(new-line-at-end-of-file)
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Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro
Cache: Disabled due to data retention organization setting
Knowledge Base: Disabled due to data retention organization setting
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.github/workflows/build-docker-images.yml(1 hunks)
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🪛 actionlint (1.7.4)
.github/workflows/build-docker-images.yml
16-16: the runner of "actions/checkout@v3" action is too old to run on GitHub Actions. update the action's version to fix this issue
(action)
🪛 YAMLlint (1.35.1)
.github/workflows/build-docker-images.yml
[error] 18-18: no new line character at the end of file
(new-line-at-end-of-file)
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- GitHub Check: build-openms
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.github/workflows/build-docker-images.yml (1)
16-16: Skip: No issues with the checkout action version
Theactions/checkout@v3action is the current major release and appropriate for this workflow.🧰 Tools
🪛 actionlint (1.7.4)
16-16: the runner of "actions/checkout@v3" action is too old to run on GitHub Actions. update the action's version to fix this issue
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🧹 Nitpick comments (1)
.github/workflows/build-docker-images.yml (1)
18-18: Remove trailing whitespace and ensure EOF newline.YAMLlint flagged a trailing space on this line and the absence of a newline at end of file. Removing the extra space after the period and adding a newline will resolve both issues.
Apply this diff:
- run: docker build --file Dockerfile --tag streamlitapp:latest --output type=tar,dest=/dev/null . + run: docker build --file Dockerfile --tag streamlitapp:latest --output type=tar,dest=/dev/null .Also add a blank line at the very end of the file.
<details> <summary>🧰 Tools</summary> <details> <summary>🪛 YAMLlint (1.35.1)</summary> [error] 18-18: no new line character at the end of file (new-line-at-end-of-file) --- [error] 18-18: trailing spaces (trailing-spaces) </details> </details> </blockquote></details> </blockquote></details> <details> <summary>📜 Review details</summary> **Configuration used: CodeRabbit UI** **Review profile: CHILL** **Plan: Pro** **Cache: Disabled due to data retention organization setting** **Knowledge Base: Disabled due to data retention organization setting** <details> <summary>📥 Commits</summary> Reviewing files that changed from the base of the PR and between 1b842d545740a001cabcee0378ddfe8b98ff8930 and 8dc755abae574f73d76b6f686b4c76c08029e2c1. </details> <details> <summary>📒 Files selected for processing (1)</summary> * `.github/workflows/build-docker-images.yml` (1 hunks) </details> <details> <summary>🧰 Additional context used</summary> <details> <summary>🪛 actionlint (1.7.4)</summary> <details> <summary>.github/workflows/build-docker-images.yml</summary> 16-16: the runner of "actions/checkout@v3" action is too old to run on GitHub Actions. update the action's version to fix this issue (action) </details> </details> <details> <summary>🪛 YAMLlint (1.35.1)</summary> <details> <summary>.github/workflows/build-docker-images.yml</summary> [error] 18-18: no new line character at the end of file (new-line-at-end-of-file) --- [error] 18-18: trailing spaces (trailing-spaces) </details> </details> </details> <details> <summary>⏰ Context from checks skipped due to timeout of 90000ms (2)</summary> * GitHub Check: build-openms * GitHub Check: build-full-app </details> <details> <summary>🔇 Additional comments (2)</summary><blockquote> <details> <summary>.github/workflows/build-docker-images.yml (2)</summary> `16-16`: **Verify the GitHub Actions checkout action version.** The static analysis hint suggests that `actions/checkout@v3` might be outdated. While `v3` is still maintained and widely used, `v4` is available. Please confirm whether you intend to upgrade to `actions/checkout@v4` or keep using `v3`. <details> <summary>🧰 Tools</summary> <details> <summary>🪛 actionlint (1.7.4)</summary> 16-16: the runner of "actions/checkout@v3" action is too old to run on GitHub Actions. update the action's version to fix this issue (action) </details> </details> --- `17-17`: **Build step declaration looks good.** The `- name: Build the full Docker image` entry is correctly formatted and self-explanatory. The placement of the build context at the end enhances readability and aligns with best practices. </details> </blockquote></details> </details> <!-- This is an auto-generated comment by CodeRabbit for review status -->
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Enables the following components for FLASHTnT:
Summary by CodeRabbit
New Features
Bug Fixes
Chores