from idc_index import IDCClient
client = IDCClient()

# get IDC data version
print(client.get_idc_version())

# Get collection count and total series
stats = client.sql_query("""
    SELECT   
        COUNT(DISTINCT collection_id) as collections,
        COUNT(DISTINCT analysis_result_id) as analysis_results,
        COUNT(DISTINCT PatientID) as patients,
        COUNT(DISTINCT StudyInstanceUID) as studies,
        COUNT(DISTINCT SeriesInstanceUID) as series,
        SUM(instanceCount) as instances,
        SUM(series_size_MB)/1000000 as size_TB
    FROM index
""")
print(stats)

from idc_index import IDCClient
client = IDCClient()

# Join index with collections_index to get cancer types
client.fetch_index("collections_index")
result = client.sql_query("""
    SELECT i.SeriesInstanceUID, i.Modality, c.CancerTypes, c.TumorLocations
    FROM index i
    JOIN collections_index c ON i.collection_id = c.collection_id
    WHERE i.Modality = 'MR'
    LIMIT 10
""")

# Join index with sm_index for slide microscopy details
client.fetch_index("sm_index")
result = client.sql_query("""
    SELECT i.collection_id, i.PatientID, s.ObjectiveLensPower, s.min_PixelSpacing_2sf
    FROM index i
    JOIN sm_index s ON i.SeriesInstanceUID = s.SeriesInstanceUID
    LIMIT 10
""")

# Join seg_index with index to find segmentations and their source images
client.fetch_index("seg_index")
result = client.sql_query("""
    SELECT
        s.SeriesInstanceUID as seg_series,
        s.AlgorithmName,
        s.total_segments,
        src.collection_id,
        src.Modality as source_modality,
        src.BodyPartExamined
    FROM seg_index s
    JOIN index src ON s.segmented_SeriesInstanceUID = src.SeriesInstanceUID
    WHERE s.AlgorithmType = 'AUTOMATIC'
    LIMIT 10
""")

from idc_index import IDCClient
client = IDCClient()

# Query the primary index (always available)
results = client.sql_query("SELECT * FROM index WHERE Modality = 'CT' LIMIT 10")

# Fetch and query additional indices
client.fetch_index("collections_index")
collections = client.sql_query("SELECT collection_id, CancerTypes, TumorLocations FROM collections_index")

client.fetch_index("analysis_results_index")
analysis = client.sql_query("SELECT * FROM analysis_results_index LIMIT 5")

# Primary index (always available after client initialization)
df = client.index

# Fetch and access on-demand indices
client.fetch_index("sm_index")
sm_df = client.sm_index

from idc_index import IDCClient
client = IDCClient()

# List all available indices with descriptions
for name, info in client.indices_overview.items():
    print(f"\n{name}:")
    print(f"  Installed: {info['installed']}")
    print(f"  Description: {info['description']}")

# Get complete schema for a specific index (columns, types, descriptions)
schema = client.indices_overview["index"]["schema"]
print(f"\nTable: {schema['table_description']}")
print("\nColumns:")
for col in schema['columns']:
    desc = col.get('description', 'No description')
    # Description indicates if column is from DICOM attribute
    print(f"  {col['name']} ({col['type']}): {desc}")

# Find columns that are DICOM attributes (check description for "DICOM" reference)
dicom_cols = [c['name'] for c in schema['columns'] if 'DICOM' in c.get('description', '').upper()]
print(f"\nDICOM-sourced columns: {dicom_cols}")

schema = client.get_index_schema("index")
# Returns same schema dict: {'table_description': ..., 'columns': [...]}

# Fetch clinical index (also downloads clinical data tables)
client.fetch_index("clinical_index")

# Query clinical index to find available tables and their columns
tables = client.sql_query("SELECT DISTINCT table_name, column_label FROM clinical_index")

# Load a specific clinical table as DataFrame
clinical_df = client.get_clinical_table("table_name")

pip install --upgrade idc-index

pip install pandas numpy pydicom

from idc_index import IDCClient

client = IDCClient()

# Get summary statistics from primary index
query = """
SELECT
  collection_id,
  COUNT(DISTINCT PatientID) as patients,
  COUNT(DISTINCT SeriesInstanceUID) as series,
  SUM(series_size_MB) as size_mb
FROM index
GROUP BY collection_id
ORDER BY patients DESC
"""
collections_summary = client.sql_query(query)

# For richer collection metadata, use collections_index
client.fetch_index("collections_index")
collections_info = client.sql_query("""
    SELECT collection_id, CancerTypes, TumorLocations, Species, Subjects, SupportingData
    FROM collections_index
""")

# For analysis results (annotations, segmentations), use analysis_results_index
client.fetch_index("analysis_results_index")
analysis_info = client.sql_query("""
    SELECT analysis_result_id, analysis_result_title, Subjects, Collections, Modalities
    FROM analysis_results_index
""")

from idc_index import IDCClient

client = IDCClient()

# Check what Modality values exist
modalities = client.sql_query("""
    SELECT DISTINCT Modality, COUNT(*) as series_count
    FROM index
    GROUP BY Modality
    ORDER BY series_count DESC
""")
print(modalities)

# Check what BodyPartExamined values exist for MR modality
body_parts = client.sql_query("""
    SELECT DISTINCT BodyPartExamined, COUNT(*) as series_count
    FROM index
    WHERE Modality = 'MR' AND BodyPartExamined IS NOT NULL
    GROUP BY BodyPartExamined
    ORDER BY series_count DESC
    LIMIT 20
""")
print(body_parts)

# Find breast MRI scans (use actual values from exploration above)
results = client.sql_query("""
    SELECT
      collection_id,
      PatientID,
      SeriesInstanceUID,
      Modality,
      SeriesDescription,
      license_short_name
    FROM index
    WHERE Modality = 'MR'
      AND BodyPartExamined = 'BREAST'
    LIMIT 20
""")

# Access results as pandas DataFrame
for idx, row in results.iterrows():
    print(f"Patient: {row['PatientID']}, Series: {row['SeriesInstanceUID']}")

client.fetch_index("collections_index")
results = client.sql_query("""
    SELECT i.collection_id, i.PatientID, i.SeriesInstanceUID, i.Modality
    FROM index i
    JOIN collections_index c ON i.collection_id = c.collection_id
    WHERE c.CancerTypes LIKE '%Breast%'
      AND i.Modality = 'MR'
    LIMIT 20
""")

from idc_index import IDCClient

client = IDCClient()

# Download small collection (RIDER Pilot ~1GB)
client.download_from_selection(
    collection_id="rider_pilot",
    downloadDir="./data/rider"
)

# First, query for series UIDs
series_df = client.sql_query("""
    SELECT SeriesInstanceUID
    FROM index
    WHERE Modality = 'CT'
      AND BodyPartExamined = 'CHEST'
      AND collection_id = 'nlst'
    LIMIT 5
""")

# Download only those series
client.download_from_selection(
    seriesInstanceUID=list(series_df['SeriesInstanceUID'].values),
    downloadDir="./data/lung_ct"
)

# Simplified hierarchy (omit StudyInstanceUID level)
client.download_from_selection(
    collection_id="tcga_luad",
    downloadDir="./data",
    dirTemplate="%collection_id/%PatientID/%Modality"
)
# Results in: ./data/tcga_luad/TCGA-05-4244/CT/

# Flat structure (all files in one directory)
client.download_from_selection(
    seriesInstanceUID=list(series_df['SeriesInstanceUID'].values),
    downloadDir="./data/flat",
    dirTemplate=""
)
# Results in: ./data/flat/*.dcm

# Download entire collection
idc download rider_pilot --download-dir ./data

# Download specific series by UID
idc download "1.3.6.1.4.1.9328.50.1.69736" --download-dir ./data

# Download multiple items (comma-separated)
idc download "tcga_luad,tcga_lusc" --download-dir ./data

# Download from manifest file (auto-detected)
idc download manifest.txt --download-dir ./data

s3://idc-open-data/cb09464a-c5cc-4428-9339-d7fa87cfe837/*
s3://idc-open-data/88f3990d-bdef-49cd-9b2b-4787767240f2/*

from idc_index import IDCClient

client = IDCClient()

# Query for series URLs
results = client.sql_query("""
    SELECT series_aws_url
    FROM index
    WHERE collection_id = 'rider_pilot' AND Modality = 'CT'
""")

# Save as manifest file
with open('ct_manifest.txt', 'w') as f:
    for url in results['series_aws_url']:
        f.write(url + '\n')

idc download ct_manifest.txt --download-dir ./ct_data

from idc_index import IDCClient
import webbrowser

client = IDCClient()

# First query to get valid UIDs
results = client.sql_query("""
    SELECT SeriesInstanceUID, StudyInstanceUID
    FROM index
    WHERE collection_id = 'rider_pilot' AND Modality = 'CT'
    LIMIT 1
""")

# View single series
viewer_url = client.get_viewer_URL(seriesInstanceUID=results.iloc[0]['SeriesInstanceUID'])
webbrowser.open(viewer_url)

# View all series in a study (useful for multi-series exams like MRI protocols)
viewer_url = client.get_viewer_URL(studyInstanceUID=results.iloc[0]['StudyInstanceUID'])
webbrowser.open(viewer_url)

from idc_index import IDCClient

client = IDCClient()

# Check licenses for all collections
query = """
SELECT DISTINCT
  collection_id,
  license_short_name,
  COUNT(DISTINCT SeriesInstanceUID) as series_count
FROM index
GROUP BY collection_id, license_short_name
ORDER BY collection_id
"""

licenses = client.sql_query(query)
print(licenses)

from idc_index import IDCClient

client = IDCClient()

# Get citations for a collection (APA format by default)
citations = client.citations_from_selection(collection_id="rider_pilot")
for citation in citations:
    print(citation)

# Get citations for specific series
results = client.sql_query("""
    SELECT SeriesInstanceUID FROM index
    WHERE collection_id = 'tcga_luad' LIMIT 5
""")
citations = client.citations_from_selection(
    seriesInstanceUID=list(results['SeriesInstanceUID'].values)
)

# Alternative format: BibTeX (for LaTeX documents)
bibtex_citations = client.citations_from_selection(
    collection_id="tcga_luad",
    citation_format=IDCClient.CITATION_FORMAT_BIBTEX
)

from idc_index import IDCClient
import pandas as pd

client = IDCClient()

# Find chest CT scans from GE scanners
query = """
SELECT
  SeriesInstanceUID,
  PatientID,
  collection_id,
  ManufacturerModelName
FROM index
WHERE Modality = 'CT'
  AND BodyPartExamined = 'CHEST'
  AND Manufacturer = 'GE MEDICAL SYSTEMS'
  AND license_short_name = 'CC BY 4.0'
LIMIT 100
"""

results = client.sql_query(query)

# Save manifest for later
results.to_csv('lung_ct_manifest.csv', index=False)

# Download in batches to avoid timeout
batch_size = 10
for i in range(0, len(results), batch_size):
    batch = results.iloc[i:i+batch_size]
    client.download_from_selection(
        seriesInstanceUID=list(batch['SeriesInstanceUID'].values),
        downloadDir=f"./data/batch_{i//batch_size}"
    )

import pydicom
import os

# Read DICOM files from downloaded series
series_dir = "./data/rider/rider_pilot/RIDER-1007893286/CT_1.3.6.1..."

dicom_files = [os.path.join(series_dir, f) for f in os.listdir(series_dir)
               if f.endswith('.dcm')]

# Load first image
ds = pydicom.dcmread(dicom_files[0])
print(f"Patient ID: {ds.PatientID}")
print(f"Modality: {ds.Modality}")
print(f"Image shape: {ds.pixel_array.shape}")

import pydicom
import numpy as np
from pathlib import Path

def load_ct_series(series_path):
    """Load CT series as 3D numpy array"""
    files = sorted(Path(series_path).glob('*.dcm'))
    slices = [pydicom.dcmread(str(f)) for f in files]

    # Sort by slice location
    slices.sort(key=lambda x: float(x.ImagePositionPatient[2]))

    # Stack into 3D array
    volume = np.stack([s.pixel_array for s in slices])

    return volume, slices[0]  # Return volume and first slice for metadata

volume, metadata = load_ct_series("./data/lung_ct/series_dir")
print(f"Volume shape: {volume.shape}")  # (z, y, x)

import SimpleITK as sitk
from pathlib import Path

# Read DICOM series
series_path = "./data/ct_series"
reader = sitk.ImageSeriesReader()
dicom_names = reader.GetGDCMSeriesFileNames(series_path)
reader.SetFileNames(dicom_names)
image = reader.Execute()

# Apply processing
smoothed = sitk.CurvatureFlow(image1=image, timeStep=0.125, numberOfIterations=5)

# Save as NIfTI
sitk.WriteImage(smoothed, "processed_volume.nii.gz")

from idc_index import IDCClient

client = IDCClient()

# 1. Query for lung CT scans with specific criteria
query = """
SELECT
  PatientID,
  SeriesInstanceUID,
  SeriesDescription
FROM index
WHERE collection_id = 'nlst'
  AND Modality = 'CT'
  AND BodyPartExamined = 'CHEST'
  AND license_short_name = 'CC BY 4.0'
ORDER BY PatientID
LIMIT 100
"""

results = client.sql_query(query)
print(f"Found {len(results)} series from {results['PatientID'].nunique()} patients")

# 2. Download data organized by patient
client.download_from_selection(
    seriesInstanceUID=list(results['SeriesInstanceUID'].values),
    downloadDir="./training_data",
    dirTemplate="%collection_id/%PatientID/%SeriesInstanceUID"
)

# 3. Save manifest for reproducibility
results.to_csv('training_manifest.csv', index=False)

from idc_index import IDCClient
import pandas as pd

client = IDCClient()

# Query for brain MRI grouped by manufacturer
query = """
SELECT
  Manufacturer,
  ManufacturerModelName,
  COUNT(DISTINCT SeriesInstanceUID) as num_series,
  COUNT(DISTINCT PatientID) as num_patients
FROM index
WHERE Modality = 'MR'
  AND BodyPartExamined LIKE '%BRAIN%'
GROUP BY Manufacturer, ManufacturerModelName
HAVING num_series >= 10
ORDER BY num_series DESC
"""

manufacturers = client.sql_query(query)
print(manufacturers)

# Download sample from each manufacturer for comparison
for _, row in manufacturers.head(3).iterrows():
    mfr = row['Manufacturer']
    model = row['ManufacturerModelName']

    query = f"""
    SELECT SeriesInstanceUID
    FROM index
    WHERE Manufacturer = '{mfr}'
      AND ManufacturerModelName = '{model}'
      AND Modality = 'MR'
      AND BodyPartExamined LIKE '%BRAIN%'
    LIMIT 5
    """

    series = client.sql_query(query)
    client.download_from_selection(
        seriesInstanceUID=list(series['SeriesInstanceUID'].values),
        downloadDir=f"./quality_study/{mfr.replace(' ', '_')}"
    )

from idc_index import IDCClient
import webbrowser

client = IDCClient()

series_list = client.sql_query("""
    SELECT SeriesInstanceUID, PatientID, SeriesDescription
    FROM index
    WHERE collection_id = 'acrin_nsclc_fdg_pet' AND Modality = 'PT'
    LIMIT 10
""")

# Preview each in browser
for _, row in series_list.iterrows():
    viewer_url = client.get_viewer_URL(seriesInstanceUID=row['SeriesInstanceUID'])
    print(f"Patient {row['PatientID']}: {row['SeriesDescription']}")
    print(f"  View at: {viewer_url}")
    # webbrowser.open(viewer_url)  # Uncomment to open automatically

from idc_index import IDCClient

client = IDCClient()

# Query ONLY for CC BY licensed data (allows commercial use with attribution)
query = """
SELECT
  SeriesInstanceUID,
  collection_id,
  PatientID,
  Modality
FROM index
WHERE license_short_name LIKE 'CC BY%'
  AND license_short_name NOT LIKE '%NC%'
  AND Modality IN ('CT', 'MR')
  AND BodyPartExamined IN ('CHEST', 'BRAIN', 'ABDOMEN')
LIMIT 200
"""

cc_by_data = client.sql_query(query)

print(f"Found {len(cc_by_data)} CC BY licensed series")
print(f"Collections: {cc_by_data['collection_id'].unique()}")

# Download with license verification
client.download_from_selection(
    seriesInstanceUID=list(cc_by_data['SeriesInstanceUID'].values),
    downloadDir="./commercial_dataset",
    dirTemplate="%collection_id/%Modality/%PatientID/%SeriesInstanceUID"
)

# Save license information
cc_by_data.to_csv('commercial_dataset_manifest_CC-BY_ONLY.csv', index=False)

# What modalities exist?
client.sql_query("SELECT DISTINCT Modality FROM index")

# What body parts for a specific modality?
client.sql_query("""
    SELECT DISTINCT BodyPartExamined, COUNT(*) as n
    FROM index WHERE Modality = 'CT' AND BodyPartExamined IS NOT NULL
    GROUP BY BodyPartExamined ORDER BY n DESC
""")

# What manufacturers for MR?
client.sql_query("""
    SELECT DISTINCT Manufacturer, COUNT(*) as n
    FROM index WHERE Modality = 'MR'
    GROUP BY Manufacturer ORDER BY n DESC
""")

# Find ALL segmentations and structure sets by DICOM Modality
# SEG = DICOM Segmentation, RTSTRUCT = Radiotherapy Structure Set
client.sql_query("""
    SELECT collection_id, Modality, COUNT(*) as series_count
    FROM index
    WHERE Modality IN ('SEG', 'RTSTRUCT')
    GROUP BY collection_id, Modality
    ORDER BY series_count DESC
""")

# Find segmentations for a specific collection (includes non-analysis-result items)
client.sql_query("""
    SELECT SeriesInstanceUID, SeriesDescription, analysis_result_id
    FROM index
    WHERE collection_id = 'tcga_luad' AND Modality = 'SEG'
""")

# List analysis result collections (curated derived datasets)
client.fetch_index("analysis_results_index")
client.sql_query("""
    SELECT analysis_result_id, analysis_result_title, Collections, Modalities
    FROM analysis_results_index
""")

# Find analysis results for a specific source collection
client.sql_query("""
    SELECT analysis_result_id, analysis_result_title
    FROM analysis_results_index
    WHERE Collections LIKE '%tcga_luad%'
""")

# Use seg_index for detailed DICOM Segmentation metadata
client.fetch_index("seg_index")

# Get segmentation statistics by algorithm
client.sql_query("""
    SELECT AlgorithmName, AlgorithmType, COUNT(*) as seg_count
    FROM seg_index
    WHERE AlgorithmName IS NOT NULL
    GROUP BY AlgorithmName, AlgorithmType
    ORDER BY seg_count DESC
    LIMIT 10
""")

# Find segmentations for specific source images (e.g., chest CT)
client.sql_query("""
    SELECT
        s.SeriesInstanceUID as seg_series,
        s.AlgorithmName,
        s.total_segments,
        s.segmented_SeriesInstanceUID as source_series
    FROM seg_index s
    JOIN index src ON s.segmented_SeriesInstanceUID = src.SeriesInstanceUID
    WHERE src.Modality = 'CT' AND src.BodyPartExamined = 'CHEST'
    LIMIT 10
""")

# Find TotalSegmentator results with source image context
client.sql_query("""
    SELECT
        seg_info.collection_id,
        COUNT(DISTINCT s.SeriesInstanceUID) as seg_count,
        SUM(s.total_segments) as total_segments
    FROM seg_index s
    JOIN index seg_info ON s.SeriesInstanceUID = seg_info.SeriesInstanceUID
    WHERE s.AlgorithmName LIKE '%TotalSegmentator%'
    GROUP BY seg_info.collection_id
    ORDER BY seg_count DESC
""")

# sm_index has detailed metadata; join with index for collection_id
client.fetch_index("sm_index")
client.sql_query("""
    SELECT i.collection_id, COUNT(*) as slides,
           MIN(s.min_PixelSpacing_2sf) as min_resolution
    FROM sm_index s
    JOIN index i ON s.SeriesInstanceUID = i.SeriesInstanceUID
    GROUP BY i.collection_id
    ORDER BY slides DESC
""")

# Size for specific criteria
client.sql_query("""
    SELECT SUM(series_size_MB) as total_mb, COUNT(*) as series_count
    FROM index
    WHERE collection_id = 'nlst' AND Modality = 'CT'
""")

client.fetch_index("clinical_index")

# Find collections with clinical data and their tables
client.sql_query("""
    SELECT collection_id, table_name, COUNT(DISTINCT column_label) as columns
    FROM clinical_index
    GROUP BY collection_id, table_name
    ORDER BY collection_id
""")

# Get all column names and types for any table
schema = client.indices_overview["index"]["schema"]
columns = [(c['name'], c['type'], c.get('description', '')) for c in schema['columns']]