feat(algorithms, intervals, data-stream): data stream disjoint intervals

DIRECTORY.md

@@ -133,6 +133,8 @@
       * [Test Car Pooling](https://github.com/BrianLusina/PythonSnips/blob/master/algorithms/intervals/car_pooling/test_car_pooling.py)
     * Count Days
       * [Test Count Days Without Meetings](https://github.com/BrianLusina/PythonSnips/blob/master/algorithms/intervals/count_days/test_count_days_without_meetings.py)
+    * Data Stream
+      * [Test Data Stream As Disjoint Intervals](https://github.com/BrianLusina/PythonSnips/blob/master/algorithms/intervals/data_stream/test_data_stream_as_disjoint_intervals.py)
     * Employee Free Time
       * [Interval](https://github.com/BrianLusina/PythonSnips/blob/master/algorithms/intervals/employee_free_time/interval.py)
       * [Test Employee Free Time](https://github.com/BrianLusina/PythonSnips/blob/master/algorithms/intervals/employee_free_time/test_employee_free_time.py)

algorithms/intervals/data_stream/README.md

@@ -0,0 +1,120 @@
+# Data Stream as Disjoint Intervals
+
+You are given a stream of non-negative integers a1, a2,... ,an . At any point, you need to summarize all numbers seen so
+far as a list of disjoint intervals.
+
+Your task is to implement the Summary Ranges class, where:
+
+1. Constructor: Initializes the Summary Ranges object with an empty stream. 
+2. Add Num(int value): Adds the integer value to the stream. 
+3. Get Intervals(): Returns the current summary of numbers as a list of disjoint intervals [start_i, end_i], sorted by start_i.
+
+> Note: Each number belongs to exactly one interval. Intervals must merge whenever new numbers connect or extend existing 
+> ones, and duplicate insertions should not affect the summary. 
+
+## Constraints
+
+- 0 <= value <= 10^4
+- At most 3*10^4 calls will be made to addNum and getIntervals.
+- At most 10^2 calls will be made to getIntervals.
+
+## Examples
+
+![Example 1](./images/examples/data_stream_as_disjoint_intervals_example_1.png)
+![Example 2](./images/examples/data_stream_as_disjoint_intervals_example_2.png)
+![Example 3](./images/examples/data_stream_as_disjoint_intervals_example_3.png)
+![Example 4](./images/examples/data_stream_as_disjoint_intervals_example_4.png)
+
+## Solution
+
+When numbers arrive one after another in a stream, it’s easy to imagine them as scattered pebbles landing on a number 
+line. If we keep them as-is, the picture quickly becomes messy. Instead, we want to summarize what we’ve seen into clean
+stretches of consecutive values, i.e., intervals. The challenge is that every new number can behave differently:
+- It may fall inside an existing interval.
+- It may extend an interval by one. 
+- It may even act like a missing puzzle piece that connects two intervals into one larger block.
+
+This constant merging and organizing is why the “intervals” pattern is the right fit. Rather than storing every number, 
+we maintain only the boundaries of disjoint intervals and carefully update them when new values arrive. This way, our 
+summary stays compact, sorted, and easy to return.
+
+We start by keeping a sorted collection of intervals instead of recording every number one by one. Each new value starts
+as a small single-point interval, and then we look at the ranges around it to decide how it fits.
+
+- If an existing range already covers the value, we simply ignore it.
+- If the value lies just beyond the end of a range, we extend that range to include it.
+- If the value lies just before the start of a range, we merge it with that range.
+- If the value sits exactly between two ranges, we merge them into one larger range.
+
+![Solution 1](./images/solutions/data_stream_as_disjoint_intervals_solution_1.png)
+![Solution 2](./images/solutions/data_stream_as_disjoint_intervals_solution_2.png)
+![Solution 3](./images/solutions/data_stream_as_disjoint_intervals_solution_3.png)
+![Solution 4](./images/solutions/data_stream_as_disjoint_intervals_solution_4.png)
+
+If none of the above cases apply, the number remains in a new interval. The stored intervals are sorted and disjointed 
+at any time, so generating the summary is as simple as listing them in order.
+
+The following steps can be performed to implement the algorithm above:
+
+1. We keep the data as a sorted map called intervals, where in this map, the key is the start of an interval and the 
+   value is the end of that interval. This ensures intervals are always ordered by their start and remain disjoint.
+2. Constructor: We initialize intervals as an empty map in the constructor.
+3. Add Num(int value): Adding a number to the stream.
+   - We treat the new number value as a small interval by setting newStart = value and newEnd = value. This will be our 
+     candidate interval that may expand or merge.
+   - Then, we work on finding the two neighbors around this number:
+     - nextInterval, which is the first interval whose start is greater than value.
+     - prevInterval, which is the interval immediately before nextInterval, if one exists.
+   - Check the previous interval:
+     - If prevInterval->end (the end of the previous interval) is greater than or equal to the value, then the number 
+       is already covered inside that range. In this case, we simply return without any changes.
+     - If prevInterval->second equals value - 1, then the new number touches the end of the previous interval.
+       - In this case, we extend the candidate’s start (newStart) to prevInterval->first so that the candidate also 
+         includes the previous interval.
+   - Check the next interval:
+     - If nextInterval->start (the start of the next interval) equals value + 1, then the new number touches the start 
+       of the next interval.
+       - We extend the candidate’s end (newEnd) to nextInterval->second and remove nextInterval from the map, since it 
+         will be merged.
+   - If the previous and next conditions apply, the candidate bridges them into one larger interval.
+   - Finally, we insert the merged interval into the map as intervals[newStart] = newEnd.
+     - This overwrites the previous interval if it was extended.
+     - It replaces the next interval if it was merged.
+     - Or it creates a new single-point interval if no merges happened.
+4. Get Intervals(): Getting all intervals.
+   - We create an empty result list.
+   - Then we iterate through all the entries in intervals, where each interval is interval.first as the start and 
+     interval.second as the end.
+     - For each entry, we push [interval.first, interval.second] into the result list.
+   - Finally, we return the result list, and as intervals is always maintained, sorted, and disjointed, the result 
+     requires no further processing.
+
+Let’s look at the following illustration to get a better understanding of the solution:
+
+![Solution 5](./images/solutions/data_stream_as_disjoint_intervals_solution_5.png)
+![Solution 6](./images/solutions/data_stream_as_disjoint_intervals_solution_6.png)
+![Solution 7](./images/solutions/data_stream_as_disjoint_intervals_solution_7.png)
+![Solution 8](./images/solutions/data_stream_as_disjoint_intervals_solution_8.png)
+![Solution 9](./images/solutions/data_stream_as_disjoint_intervals_solution_9.png)
+![Solution 10](./images/solutions/data_stream_as_disjoint_intervals_solution_10.png)
+![Solution 11](./images/solutions/data_stream_as_disjoint_intervals_solution_11.png)
+![Solution 12](./images/solutions/data_stream_as_disjoint_intervals_solution_12.png)
+
+### Time Complexity
+
+Let k be the current number of disjoint intervals stored in the intervals map.
+
+- Add Num(int value): O(logk):
+  - One upper bound O(logk), at most one predecessor check O(1), and up to one erase (of the next interval) plus one 
+    insert/update (each O(logk).
+
+- Get Intervals(): O(k)
+  - We iterate over every stored interval once to build the output.
+
+- Worst case relation to n: If there are n Add Num(int value) calls and nothing ever merges, then k=O(n), giving 
+  Add Num(int value) O(logn) and Get Intervals() O(n).
+
+### Space Complexity
+
+As we store only interval boundaries (start → end) rather than every number seen, the space complexity is O(k). In the 
+worst case with no merges, k=O(n), so space becomes O(n).

algorithms/intervals/data_stream/__init__.py

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+from typing import List, Dict
+from bisect import bisect_right, insort
+
+
+class SummaryRanges:
+    def __init__(self):
+        # Map to store intervals: key = start of interval, value = end of interval.
+        # Intervals are kept sorted automatically by map ordering.
+        self.intervals: Dict[int, int] = {}
+        # stores the starts of the intervals
+        self.starts: List[int] = []
+
+    def add_num(self, value: int) -> None:
+        """
+        Adds the integer value to the stream.
+
+        Args:
+            value (int): The integer value to add to the stream.
+        """
+        new_start = value
+        new_end = value
+
+        # Find the first interval with a start greater than 'value'
+        idx = bisect_right(self.starts, value)
+
+        # Check the interval immediately before 'value' (if it exists)
+        if idx > 0:
+            previous_start = self.starts[idx - 1]
+            previous_end = self.intervals[previous_start]
+
+            # Case 1: 'value' already inside an existing interval -> do nothing
+            if previous_end >= value:
+                return
+
+            # Case 2: 'value' extends the previous interval by exactly 1
+            if previous_end == value - 1:
+                # merge with previous
+                new_start = previous_start
+
+        # Case 3: 'value' connects directly to the start of the next interval
+        next_start = self.starts[idx] if idx < len(self.starts) else None
+        if next_start is not None and next_start == value + 1:
+            # merge with next
+            new_end = self.intervals[next_start]
+            # remove the old interval [next_start, ...]
+            del self.intervals[next_start]
+            self.starts.pop(idx)
+
+        # Insert the merged or new interval into the map
+        if new_start in self.intervals:
+            # update existing interval(merged with previous)
+            self.intervals[new_start] = new_end
+        else:
+            # insert new
+            self.intervals[new_start] = new_end
+            insort(self.starts, new_start)
+
+    def get_intervals(self) -> List[List[int]]:
+        """
+        Returns the current summary of numbers as a list of disjoint intervals [start_i, end_i], sorted by start_i.
+        Returns:
+            List[List[int]]: List of disjoint intervals
+        """
+        result: List[List[int]] = []
+        # Collect all intervals from the map
+        for s in self.starts:
+            result.append([s, self.intervals[s]])
+        return result
+
+
+class SummaryRangesV2:
+    def __init__(self):
+        # stores the intervals
+        self.intervals: List[List[int]] = []
+        # stores the starts of the intervals
+        self.starts: List[int] = []
+
+    def add_num(self, value: int) -> None:
+        # If we have an existing interval, we should join the new value if: start - 1 <= value <= end + 1
+        # So, for an interval [start, end], the condition to see if value is already included is: start <= value <= end
+
+        # 1. Get the index of the interval where the value should be inserted
+        idx = bisect_right(self.starts, value)
+
+        # ... (Case 1: Duplicate)
+        # if idx is greater than 0, our left neighbor is at idx - 1
+        # 2. Check for Duplicate (already in left neighbor)
+        if (
+            idx > 0
+            and self.intervals[idx - 1][0] <= value <= self.intervals[idx - 1][1]
+        ):
+            return
+
+        # identify possible neighbors for merging
+        left_merge = idx > 0 and self.intervals[idx - 1][1] + 1 == value
+        right_merge = idx < len(self.intervals) and self.intervals[idx][0] - 1 == value
+
+        # ... (Case 2: Double Merge)
+        # 3. Check for Double Merge
+        if left_merge and right_merge:
+            self.intervals[idx - 1][1] = self.intervals[idx][1]
+            self.starts.pop(idx)
+            self.intervals.pop(idx)
+        # ... (Case 3: Left Merge)
+        elif left_merge:
+            # Extend the end of the left interval
+            self.intervals[idx - 1][1] = value
+        # ... (Case 4: Right Merge)
+        elif right_merge:
+            # Extend the start of the right interval
+            self.intervals[idx][0] = value
+            # keep the starts list updated
+            self.starts[idx] = value
+        # ... (Case 5: New Island)
+        else:
+            insort(self.starts, value)
+            insort(self.intervals, [value, value])
+
+    def get_intervals(self) -> List[List[int]]:
+        """
+        Returns the current summary of numbers as a list of disjoint intervals [start_i, end_i], sorted by start_i.
+        This is run in O(1) time as it simply returns the list of intervals.
+        Returns:
+            List[List[int]]: List of disjoint intervals
+        """
+        return self.intervals

algorithms/intervals/data_stream/test_data_stream_as_disjoint_intervals.py

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+import unittest
+from typing import List
+from parameterized import parameterized
+from algorithms.intervals.data_stream import SummaryRanges, SummaryRangesV2
+
+TEST_CASES = [
+    ([5], [[5, 5]]),
+    ([5, 5], [[5, 5]]),
+    ([1, 3, 2], [[1, 3]]),
+    ([1, 7, 3], [[1, 1], [3, 3], [7, 7]]),
+    ([10, 12, 11], [[10, 12]]),
+    ([1, 4, 2, 9, 3], [[1, 4], [9, 9]]),
+]
+
+
+class DataStreamAsDisjointIntervalTestCase(unittest.TestCase):
+    @parameterized.expand(TEST_CASES)
+    def test_data_stream_as_disjoint_intervals(
+        self, data: List[int], expected: List[List[int]]
+    ):
+        summary_ranges = SummaryRanges()
+        for d in data:
+            summary_ranges.add_num(d)
+        actual = summary_ranges.get_intervals()
+        self.assertEqual(expected, actual)
+
+    @parameterized.expand(TEST_CASES)
+    def test_data_stream_as_disjoint_intervals_v2(
+        self, data: List[int], expected: List[List[int]]
+    ):
+        summary_ranges = SummaryRangesV2()
+        for d in data:
+            summary_ranges.add_num(d)
+        actual = summary_ranges.get_intervals()
+        self.assertEqual(expected, actual)
+
+
+if __name__ == "__main__":
+    unittest.main()