refactor: rename apply_blacklist_lazy to apply_blacklist and update documentation

.gitattributes

@@ -0,0 +1 @@
+notebooks/** linguist-vendored

scripts/unix/build.sh

@@ -0,0 +1,33 @@
+#!/bin/bash
+# Exit immediately if a command exits with a non-zero status
+set -e
+
+# Run "maturin --help". If it fails, print an error message and exit.
+if ! maturin --help > /dev/null 2>&1; then
+    echo "Failed to run maturin --help" >&2
+    exit 1
+fi
+
+# Delete any existing build directory and create a new one.
+rm -rf ./build
+mkdir -p ./build
+
+# Copy ./src/msyrs.pyi to ./msyrs.pyi.
+cp ./src/msyrs.pyi ./msyrs.pyi
+
+# Build using maturin.
+maturin build --release --sdist --out ./build/
+
+# Get the first wheel file found in the build directory.
+whl_file=$(ls ./build/*.whl 2>/dev/null | head -n 1)
+if [ -z "$whl_file" ]; then
+    echo "No wheel file found in ./build" >&2
+    exit 1
+fi
+
+# Rename the wheel file from .whl to .zip.
+base_name="${whl_file%.whl}"
+mv "$whl_file" "${base_name}.zip"
+
+# Delete the temporary .pyi file.
+rm ./msyrs.pyi

scripts/unix/install.sh

@@ -0,0 +1,20 @@
+#!/bin/bash
+set -e
+
+# Ensure maturin is installed. For example, you can install it via:
+# pip install maturin
+
+# Run "maturin --help". If it fails, print an error message and exit.
+if ! maturin --help > /dev/null 2>&1; then
+    echo "Failed to run maturin --help" >&2
+    exit 1
+fi
+
+# Copy ./src/msyrs.pyi to the current directory as msyrs.pyi
+cp ./src/msyrs.pyi ./msyrs.pyi
+
+# Run maturin develop in release mode.
+maturin develop --release
+
+# Delete the temporary msyrs.pyi file.
+rm ./msyrs.pyi

src/_py/utils.rs

@@ -1,4 +1,4 @@
-use pyo3::prelude::*;
+use pyo3::{prelude::*, types::PyDict};
 use pyo3_polars::{PyDataFrame, PySeries};
 
 /// Python wrapper for [`crate::utils::qdf`] module.
@@ -7,6 +7,7 @@ use pyo3_polars::{PyDataFrame, PySeries};
 pub fn utils(_py: Python, m: &PyModule) -> PyResult<()> {
     m.add_function(wrap_pyfunction!(get_bdates_series_default_pl, m)?)?;
     m.add_function(wrap_pyfunction!(get_bdates_series_default_opt, m)?)?;
+    m.add_function(wrap_pyfunction!(create_blacklist_from_qdf, m)?)?;
     Ok(())
 }
 
@@ -33,3 +34,29 @@ pub fn get_bdates_series_default_opt(
             .map_err(|e| PyErr::new::<pyo3::exceptions::PyValueError, _>(format!("{}", e)))?,
     ))
 }
+
+#[allow(deprecated)]
+#[pyfunction(signature = (df, group_by_cid=None, blacklist_name=None, metrics=None))]
+pub fn create_blacklist_from_qdf(
+    df: PyDataFrame,
+    group_by_cid: Option<bool>,
+    blacklist_name: Option<String>,
+    metrics: Option<Vec<String>>,
+) -> PyResult<PyObject> {
+    let result = crate::utils::qdf::blacklist::create_blacklist_from_qdf(
+        &df.into(),
+        group_by_cid,
+        blacklist_name,
+        metrics,
+    )
+    .map_err(|e| PyErr::new::<pyo3::exceptions::PyValueError, _>(format!("{}", e)))?;
+    Python::with_gil(|py| {
+        let dict = PyDict::new(py);
+        // for (key, (start_date, end_date)) in result {
+        //     dict.set_item(key, (start_date, end_date))
+        for (key, dates) in result {
+            dict.set_item(key, dates).map_err(|e| PyErr::from(e))?;
+        }
+        Ok(dict.into())
+    })
+}

src/download/jpmaqsdownload.rs

@@ -58,7 +58,7 @@ fn all_jpmaq_expressions(expressions: Vec<String>) -> bool {
 ///
 /// Example Usage:
 ///
-/// ```rust
+/// ```ignore
 /// use msyrs::download::jpmaqsdownload::JPMaQSDownloadGetIndicatorArgs;
 /// use msyrs::download::jpmaqsdownload::JPMaQSDownload;
 ///
@@ -102,7 +102,7 @@ impl Default for JPMaQSDownloadGetIndicatorArgs {
 /// Struct for downloading data from the JPMaQS data from JPMorgan DataQuery API.
 ///
 /// ## Example Usage
-/// ```rust
+/// ```ignore
 /// use msyrs::download::jpmaqsdownload::JPMaQSDownload;
 /// use msyrs::download::jpmaqsdownload::JPMaQSDownloadGetIndicatorArgs;
 /// use polars::prelude::*;
@@ -277,7 +277,7 @@ impl JPMaQSDownload {
     ///  
     /// Usage:
     ///
-    /// ```rust
+    /// ```ignore
     /// use msyrs::download::jpmaqsdownload::JPMaQSDownload;
     /// use msyrs::download::jpmaqsdownload::JPMaQSDownloadGetIndicatorArgs;
     /// let mut jpamqs_download = JPMaQSDownload::default();

src/msyrs.pyi

@@ -56,3 +56,5 @@ class utils:
     def get_bdates_series_default_pl(*args, **kwargs) -> Series: ...
     @staticmethod
     def get_bdates_series_default_opt(*args, **kwargs) -> Series: ...
+    @staticmethod
+    def create_blacklist_from_qdf(*args, **kwargs) -> dict: ...

src/utils/qdf/blacklist.rs

@@ -1,27 +1,373 @@
-use crate::utils::dateutils::{get_bdates_series_default_opt, get_min_max_real_dates};
+use crate::utils::bdates::{get_bdates_list_with_freq, BDateFreq};
-use crate::utils::qdf::core::*;
+use crate::utils::dateutils::get_min_max_real_dates;
-use chrono::{Duration, NaiveDate};
+use crate::utils::misc::get_cid;
+use crate::utils::qdf::core::check_quantamental_dataframe;
+use chrono::NaiveDate;
 use polars::prelude::*;
-use std::collections::HashMap;
+use std::collections::{BTreeMap, HashMap};
 use std::error::Error;
 
-use super::pivots::pivot_dataframe_by_ticker;
+use crate::utils::qdf::get_unique_metrics;
 
-/// The required columns for a Quantamental DataFrame.
+// struct Blacklist which is a wrapper around hashmap and btreemap
-const QDF_INDEX_COLUMNS: [&str; 3] = ["real_date", "cid", "xcat"];
+#[derive(Debug, Clone)]
+pub struct Blacklist {
+    pub blacklist: BTreeMap<String, (String, String)>,
+}
 
+// impl hashmap into
+impl Blacklist {
+    pub fn into_hashmap(self) -> HashMap<String, (String, String)> {
+        self.blacklist.into_iter().collect()
+    }
+}
+
+/// Apply a blacklist to a Quantamental DataFrame.
+///
+/// * `blacklist` is a map from any “ticker‑like” key to a tuple of
+///   `(start_date, end_date)` in **inclusive** `"YYYY‑MM‑DD"` format.
+/// * `metrics` – if `None`, every metric from `get_unique_metrics(df)`
+///   is used.
+/// * `group_by_cid = Some(false)` is not implemented yet.
+pub fn apply_blacklist(
+    df: &mut DataFrame,
+    blacklist: &BTreeMap<String, (String, String)>,
+    metrics: Option<Vec<String>>,
+    group_by_cid: Option<bool>,
+) -> Result<DataFrame, Box<dyn std::error::Error>> {
+    check_quantamental_dataframe(df)?;
+    // dataframe is like:
+    // | cid | xcat | real_date | metric1 | metric2 |
+    // |-----|------|-----------|---------|---------|
+    // | A   | B    | 2023-01-01| 1.0     | 2.0     |
+    // | A   | B    | 2023-01-02| 1.0     | 2.0     |
+    // | A   | C    | 2023-01-01| 1.0     | 2.0     |
+    // | A   | C    | 2023-01-02| 1.0     | 2.0     |
+    // | D   | E    | 2023-01-01| 1.0     | 2.0     |
+    // | D   | E    | 2023-01-02| 1.0     | 2.0     |
+
+    // (real date column is Naive date)
+
+    // blacklist is like:
+    // {'A_B_1': ('2023-01-02', '2023-01-03'),
+    //  'A_B_2': ('2023-01-04', '2023-01-05'),
+    //  'A_C_1': ('2023-01-02', '2023-01-03'), }
+
+    // get_cid('A_B_1') = 'A'
+    // get_cid('A_B_2') = 'A'
+    // get_cid('D_E_1') = 'D'
+
+    Ok(df.clone())
+}
+/// Create a blacklist from a Quantamental DataFrame.
+/// The blacklist is a mapping of tickers to date ranges where the specified metrics are null or NaN.
+/// # Arguments:
+/// * `df` - The Quantamental DataFrame.
+/// * `group_by_cid` - If true, group the blacklist by `cid`. Defaults to true.
+/// * `blacklist_name` - The name of the blacklist. Defaults to "BLACKLIST".
+/// * `metrics` - The metrics to check for null or NaN values. If None, all metrics are used.
 pub fn create_blacklist_from_qdf(
     df: &DataFrame,
-    metric: Option<String>,
+    group_by_cid: Option<bool>,
-    start: Option<String>,
+    blacklist_name: Option<String>,
-    end: Option<String>,
+    metrics: Option<Vec<String>>,
-) -> Result<HashMap<String, Vec<String>>, Box<dyn Error>> {
+) -> Result<BTreeMap<String, (String, String)>, Box<dyn Error>> {
-    // Verify that the DataFrame follows the Quantamental structure.
     check_quantamental_dataframe(df)?;
-    let mut blacklist: HashMap<String, Vec<String>> = HashMap::new();
+    let metrics = metrics.unwrap_or_else(|| get_unique_metrics(df).unwrap());
+    let blacklist_name = blacklist_name.unwrap_or_else(|| "BLACKLIST".into());
+    let group_by_cid = group_by_cid.unwrap_or(true);
 
-    // Use the provided metric or default to "value".
+    let (min_date, max_date) = get_min_max_real_dates(df, "real_date".into())?;
-    let metric = metric.unwrap_or_else(|| "value".into());
+    let min_date_str = min_date.format("%Y-%m-%d").to_string();
+    let max_date_str = max_date.format("%Y-%m-%d").to_string();
+    // let all_bdates = get_bdates_series_default_opt(min_date_str, max_date_str, None)?;
+    let all_bdates = get_bdates_list_with_freq(
+        min_date_str.clone().as_str(),
+        max_date_str.clone().as_str(),
+        BDateFreq::Daily,
+    )?;
 
-    Ok(blacklist)
+    // if none of the metrics are null or NaN, return an empty blacklist
+    if !metrics.iter().any(|metric| {
+        df.column(metric)
+            .map(|col| col.is_null().any())
+            .unwrap_or(false)
+    }) {
+        return Ok(BTreeMap::new());
+    }
+
+    // let null_mask = get_nan_mask(df, metrics)?;
+    // let df = df.filter(&null_mask)?.clone();
+
+    let df = df
+        .clone()
+        .lazy()
+        .with_columns([
+            (cols(metrics.clone()).is_null().or(cols(metrics).is_nan())).alias("null_mask")
+        ])
+        .filter(col("null_mask"))
+        // if is now empty, return an empty blacklist
+        .sort(
+            ["cid", "xcat"],
+            SortMultipleOptions::default().with_maintain_order(true),
+        )
+        .group_by([col("cid"), col("xcat")])
+        // .agg([col("real_date").sort(SortOptions::default())])
+        .agg([col("real_date")
+            .dt()
+            .strftime("%Y-%m-%d")
+            .sort(SortOptions::default())])
+        .select([
+            concat_str([col("cid"), col("xcat")], "_", true).alias("ticker"),
+            col("real_date").alias("real_dates"),
+        ])
+        .collect()?;
+
+    // assert!(0 == 1, "{:?}", df);
+
+    let ticker_vec = df
+        .column("ticker")?
+        .str()?
+        .into_iter()
+        .filter_map(|opt| opt.map(|s| s.to_string()))
+        .collect::<Vec<String>>();
+
+    let rdt = get_vec_of_vec_of_dates_from_df(df)?;
+
+    let mut blk: HashMap<String, Vec<String>> = HashMap::new();
+    for (tkr, dates) in ticker_vec.iter().zip(rdt.iter()) {
+        if group_by_cid {
+            let _cid = get_cid(tkr.clone())?;
+            if blk.contains_key(&_cid) {
+                blk.get_mut(&_cid).unwrap().extend(dates.iter().cloned());
+            } else {
+                blk.insert(_cid, dates.clone());
+            }
+        } else {
+            blk.insert(tkr.to_string(), dates.clone());
+        }
+    }
+    for (_key, vals) in blk.iter_mut() {
+        // order is important - dedup depends on the vec being sorted
+        vals.sort();
+        vals.dedup();
+    }
+
+    let all_bdates_strs = all_bdates
+        .iter()
+        .map(|date| date.format("%Y-%m-%d").to_string())
+        .collect::<Vec<String>>();
+
+    let mut blacklist: HashMap<String, (String, String)> = HashMap::new();
+    for (tkr, dates) in blk.iter() {
+        let date_ranges = convert_dates_list_to_date_ranges(dates.clone(), all_bdates_strs.clone());
+        for (rng_idx, (start_date, end_date)) in date_ranges.iter() {
+            let range_key = format!("{}_{}_{}", tkr, blacklist_name.clone(), rng_idx);
+            blacklist.insert(range_key, (start_date.clone(), end_date.clone()));
+        }
+    }
+    // Ok(blacklist)
+
+    let mut btree_map: BTreeMap<String, (String, String)> = BTreeMap::new();
+    for (key, (start_date, end_date)) in blacklist.iter() {
+        btree_map.insert(key.clone(), (start_date.clone(), end_date.clone()));
+    }
+
+    Ok(btree_map)
+}
+
+/// Get a mask of NaN values for the specified metrics in the DataFrame.
+#[allow(dead_code)]
+fn get_nan_mask(
+    df: &DataFrame,
+    metrics: Vec<String>,
+) -> Result<ChunkedArray<BooleanType>, Box<dyn Error>> {
+    let null_masks: Vec<ChunkedArray<BooleanType>> = metrics
+        .iter()
+        .map(|metric| {
+            let null_mask = df.column(metric.as_str())?.is_null();
+            let nan_mask = df.column(metric.as_str())?.is_nan()?;
+            Ok(null_mask | nan_mask)
+        })
+        .collect::<Result<_, Box<dyn Error>>>()?;
+    let null_mask = null_masks
+        .into_iter()
+        .reduce(|acc, mask| acc | mask)
+        .unwrap_or_else(|| BooleanChunked::full_null("null_mask".into(), df.height()));
+    Ok(null_mask)
+}
+
+fn convert_dates_list_to_date_ranges(
+    blacklist: Vec<String>,
+    all_bdates_strs: Vec<String>,
+) -> HashMap<String, (String, String)> {
+    // Step 1: Map every date in all_bdates_strs to its index
+    let mut all_map: HashMap<String, usize> = HashMap::new();
+    for (i, d) in all_bdates_strs.iter().enumerate() {
+        all_map.insert(d.clone(), i);
+    }
+
+    // Step 2: Convert each blacklisted date into its index, if it exists
+    let mut blacklisted_indices: Vec<usize> = Vec::new();
+    for dt in blacklist {
+        if let Some(&idx) = all_map.get(&dt) {
+            blacklisted_indices.push(idx);
+        }
+    }
+
+    // Step 3: Sort the blacklisted indices
+    blacklisted_indices.sort_unstable();
+
+    // Step 4: Traverse and group consecutive indices into ranges
+    let mut result: HashMap<i64, (String, String)> = HashMap::new();
+    let mut string_result: HashMap<String, (String, String)> = HashMap::new();
+
+    if blacklisted_indices.is_empty() {
+        return string_result;
+    }
+
+    let mut range_idx: i64 = 0;
+    let mut start_idx = blacklisted_indices[0];
+    let mut end_idx = start_idx;
+
+    for &cur_idx in blacklisted_indices.iter().skip(1) {
+        if cur_idx == end_idx + 1 {
+            // We are still in a contiguous run
+            end_idx = cur_idx;
+        } else {
+            // We hit a break in contiguity, so store the last range
+            result.insert(
+                range_idx,
+                (
+                    all_bdates_strs[start_idx].clone(),
+                    all_bdates_strs[end_idx].clone(),
+                ),
+            );
+            range_idx += 1;
+
+            // Start a new range
+            start_idx = cur_idx;
+            end_idx = cur_idx;
+        }
+    }
+
+    // Don't forget to store the final range after the loop
+    result.insert(
+        range_idx,
+        (
+            all_bdates_strs[start_idx].clone(),
+            all_bdates_strs[end_idx].clone(),
+        ),
+    );
+
+    let max_digits = result.keys().max().unwrap_or(&-1).to_string().len();
+    for (key, (start_date, end_date)) in result.iter() {
+        let new_key = format!("{:0width$}", key, width = max_digits);
+        string_result.insert(new_key, (start_date.clone(), end_date.clone()));
+    }
+
+    string_result
+}
+
+fn get_vec_of_vec_of_dates_from_df(df: DataFrame) -> Result<Vec<Vec<String>>, Box<dyn Error>> {
+    let rdt = df
+        .column("real_dates")?
+        // .clone()
+        .as_series()
+        .unwrap()
+        .list()?
+        .into_iter()
+        .filter_map(|opt| opt)
+        .collect::<Vec<Series>>()
+        .iter()
+        .map(|s| {
+            s.str()
+                .unwrap()
+                .into_iter()
+                .filter_map(|opt| opt.map(|s| s.to_string()))
+                .collect::<Vec<String>>()
+        })
+        .collect::<Vec<Vec<String>>>();
+    Ok(rdt)
+}
+
+#[allow(dead_code)]
+fn get_vec_of_vec_of_naivedates_from_df(
+    df: DataFrame,
+) -> Result<Vec<Vec<NaiveDate>>, Box<dyn Error>> {
+    let rdt = df
+        .column("real_dates")?
+        // .clone()
+        .as_series()
+        .unwrap()
+        .list()?
+        .into_iter()
+        .filter_map(|opt| opt)
+        .collect::<Vec<Series>>()
+        .iter()
+        .map(|s| {
+            s.date()
+                .unwrap()
+                .into_iter()
+                .filter_map(|opt| opt.and_then(|date| NaiveDate::from_num_days_from_ce_opt(date)))
+                .collect::<Vec<NaiveDate>>()
+        })
+        .collect::<Vec<Vec<NaiveDate>>>();
+    Ok(rdt)
+}
+
+// fn get_vec_of_vec_of_dates_from_df(df: DataFrame) -> Result<Vec<Vec<String>>, Box<dyn Error>> {
+//     let real_dates_column = df.column("real_dates")?.clone();
+//     let series = real_dates_column.as_series().unwrap().clone();
+//     let rdt = series.list()?.clone();
+//     let rdt = rdt
+//         .into_iter()
+//         .filter_map(|opt| opt)
+//         .collect::<Vec<Series>>();
+//     let rdt = rdt
+//         .iter()
+//         .map(|s| {
+//             s.str()
+//                 .unwrap()
+//                 .into_iter()
+//                 .filter_map(|opt| opt.map(|s| s.to_string()))
+//                 .collect::<Vec<String>>()
+//         })
+//         .collect::<Vec<Vec<String>>>();
+//     Ok(rdt)
+// }
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_convert_dates_list_to_date_ranges() {
+        let all_dates = vec![
+            "2023-01-01".to_string(),
+            "2023-01-02".to_string(),
+            "2023-01-03".to_string(),
+            "2023-01-04".to_string(),
+            "2023-01-05".to_string(),
+            "2023-01-06".to_string(),
+        ];
+        let blacklist = vec![
+            "2023-01-02".to_string(),
+            "2023-01-03".to_string(),
+            "2023-01-05".to_string(),
+        ];
+
+        let result = convert_dates_list_to_date_ranges(blacklist, all_dates);
+        // Expect two ranges:
+        //   range 0 => ("2023-01-02", "2023-01-03")
+        //   range 1 => ("2023-01-05", "2023-01-05")
+        assert_eq!(
+            result["0"],
+            ("2023-01-02".to_string(), "2023-01-03".to_string())
+        );
+        assert_eq!(
+            result["1"],
+            ("2023-01-05".to_string(), "2023-01-05".to_string())
+        );
+    }
 }

src/utils/qdf/core.rs

@@ -17,14 +17,15 @@ use std::error::Error;
 pub fn check_quantamental_dataframe(df: &DataFrame) -> Result<(), Box<dyn Error>> {
     let expected_cols = ["real_date", "cid", "xcat"];
     let expected_dtype = [DataType::Date, DataType::String, DataType::String];
+    let err = "Quantamental DataFrame must have at least 4 columns: 'real_date', 'cid', 'xcat' and one or more metrics.";
     for (col, dtype) in expected_cols.iter().zip(expected_dtype.iter()) {
         let col = df.column(col);
         if col.is_err() {
-            return Err(format!("Column {:?} not found", col).into());
+            return Err(format!("{} Column {:?} not found", err, col).into());
         }
         let col = col?;
         if col.dtype() != dtype {
-            return Err(format!("Column {:?} has wrong dtype", col).into());
+            return Err(format!("{} Column {:?} has wrong dtype", err, col).into());
         }
     }
     Ok(())