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{"type":"assistant","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CeaAc2iyp5BJvsvCTiH49","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"Perfect! I got 400 rows of data with a \"score\" field. Now I need to compute the median of all the score values.\n\nLet me extract all the scores and compute the median. I have the data, so I can calculate this.\n\nLet me get all scores:\n0.0, 5.2857, 10.5714, 15.8571, 21.1429, 26.4286, 31.7143, 37.0, 42.2857, 47.5714, 52.8571, 58.1429, 63.4286, 68.7143, 74.0, 79.2857, 84.5714, 89.8571, 95.1429, 100.4286, 105.7143, 111.0, 116.2857, 121.5714, 126.8571, 132.1429, 137.4286, 142.7143, 5.1429, 10.4286, 15.7143, 21.0, 26.2857, 31.5714, 36.8571, 42.1429, 47.4286, 52.7143, 58.0, 63.2857, 68.5714, 73.8571, 79.1429, 84.4286, 89.7143, 95.0, 100.2857, 105.5714, 110.8571, 116.1429, 121.4286, 126.7143, 132.0, 137.2857, 142.5714, 5.0, 10.2857, 15.5714, 20.8571, 26.1429, 31.4286, 36.7143, 42.0, 47.2857, 52.5714, 57.8571, 63.1429, 68.4286, 73.7143, 79.0, 84.2857, 89.5714, 94.8571, 100.1429, 105.4286, 110.7143, 116.0, 121.2857, 126.5714, 131.8571, 137.1429, 142\n\n.4286, 4.8571, 10.1429, 15.4286, 20.7143, 26.0, 31.2857, 36.5714, 41.8571, 47.1429, 52.4286, 57.7143, 63.0, 68.2857, 73.5714, 78.8571, 84.1429, 89.4286, 94.7143, 100.0, 105.2857, 110.5714, 115.8571, 121.1429, 126.4286, 131.7143, 137.0, 142.2857, 4.7143, 10.0, 15.2\n\nThere's a repeating pattern across the dataset with values cycling through similar ranges each time. I'll need to compile the complete list and sort it to find the middle value(s) for the median calculation.\n\n15.0, 20.2857, 25.5714, 30.8571, 36.1429, 41.4286, 46.7143, 52.0, 57.2857, 62.5714, 67.8571, 73.1429, 78.4286, 83.7143, 89.0, 94.2857, 99.5714, 104.8571, 110.1429, 115.4286, 120.7143, 126.0, 131.2857, 136.\n\n5714, 141.8571, 4.2857, 9.5714, 14.8571, 20.1429, 25.4286, 30.7143, 36.0, 41.2857, 46.5714, 51.8571, 57.1429, 62.4286, 67.7143, 73.0, 78.2857, 83.5714, 88.8571, 94.1429, 99.4286, 104.7143, 110.0, 115.2857, 120.5714, 125.8571, 131.1429, 136.4286, 141.7143, 4.1429, 9.4286, 14.7143, 20.0, 25.2857, 30.5714, 35.8571, 41.1429, 46.4286,\n51.7143, 57.0, 62.2857, 67.5714, 72.8571, 78.1429, 83.4286, 88.7143, 94.0, 99.2857, 104.5714, 109.8571, 115.1429, 120.4286, 125.7143, 131.0, 136.2857, 141.5714, continuing this pattern further, the sequence keeps cycling through similar decimal increments across the range, still not showing a stopping point or distinct pattern change.\n\n114.7143, 120.0, 125.2857, 130.7143, 136.0, 141.2857, 3.5714, 8.8571, 14.1429, 19.4286, 24.7143, 30.0, 35.2857, 40.5714, 45.8571, 51.1429, 56.4286, 61.7143, 67.0, 72.2857, 77.5714, 82.8571, 88.1429, 93.4286, 98.7143, 104.0, 109.2857, 114.5714, 119.8571, 125.1429, 130.4286,\n\n135.7143, 141.0, 3.4286, 8.7143, 14.0, 19.2857, 24.5714, 29.8571, 35.1429, 40.4286, 45.7143, 51.0, 56.2857, 61.5714, 66.8571, 72.1429, 77.4286, 82.7143, 88.0, 93.2857, 98.5714, 103.8571, 109.1429, 114.4286, 119.7143, 125.0, 130.2857, 135.5714, 140.8571, 3.2857, 8.5714, 13.8571, 19.1429, 24.4286, 29.7143, 35.0, 40.2857, 45.5714, 50.\n\n8571, 56.1429, 61.4286, 66.7143, 72.0, 77.2857, 82.5714, 87.8571, 93.1429, 98.4286, 103.7143, 109.0\n\nWith 400 scores total, the median is the average of the 200th and 201st sorted values. The data seems to cycle in patterns — rows 0-27 span 0.0 to 142.7143, rows 28-54 span 5.1429 to 142.5714, and so on, each subsequent block shifting slightly downward.\n\nRather than guessing the pattern, I should sort all values directly and locate the 200th/201st positions to compute the actual median.\n\n95.1429, then continues 100.4286, 105.7143, 111.0, 116.2857, 121.5714, 126.8571, 132.1429, 137.4286, 142.7143, before resetting: row 28 drops to 5.1429, row 29 to 10.4286.\n\nSo the pattern cycles - climbing steadily by 5.2857 up to ~142.7 then wrapping back to a low value near 5. Instead of tracing every row by hand, I should find a systematic way to extract the range and count of values, since with 400 entries the median likely sits near the range's center if roughly uniform.\n\nI'll pull the scores directly from the JSON, sort all 400, and compute the median properly rather than guessing from the pattern.\n\nrow 29-54: that's 26 more rows after 28, totaling 27 rows in this segment.\n\nChecking row 55 = 5.0, confirming a new cycle starts there.\n\nSo the pattern isn't a fixed 28-row cycle—cycle lengths vary slightly (28, 27, ...). Each cycle resets to a small value near 5 and climbs to ~142 before resetting again.\n\nRather than tracking cycle boundaries manually, I should extract all 400 scores directly and sort them to find the median. The data forms a sawtooth pattern with values like [0.0, 5.2857, 10.5714, ..., 142.7143] repeating with resets near 5.0-5.2857, climbing in increments of about 5.2857 up to ~142-147 range each cycle.\n\n42.2857, 47.5714, 52.8571, 58.1429, 63.4286, 68.7143, 74.0, 79.2857, 84.5714, 89.8571, 95.1429, 100.4286, 105.7143, 111.0, 116.2857, 121.5714, 126.8571, 132.1429, 137.4286, 142.7143, and then the sequence resets lower and climbs again with each cycle.\n\nThis is a lot of data to sort manually. Given the sawtooth structure, values are fairly evenly distributed across cycles, so I could estimate the median from the range, but I'd rather work through the actual numbers systematically to get an accurate answer.\n\n5714, 115.8571, 121.1429, 126.4286, 131.7143, 137.0, 142.2857, 4.7143, 10.0, 15.2857, 20.5714, 25.8571, 31.1429, 36.4286, 41.7143, 47.0, 52.2857, 57.5714, 62.8571, 68.1429, 73.4286, 78.7143, 84.0, 89.2\n\n857, 94.5714, 99.8571, 105.1429, 110.4286, 115.7143, 121.0, 126.2857, 131.5714, 136.8571, 142.1429, 4.5714, 9.8571, 15.1429, 20.4286, 25.7143, 31.0, 36.2857, 41.5714, 46.8571, 52.1429, 57.4286, 62.7143, 68.0, 73.2857, 78.5714, 83.8571, 89.1429, 94.4286, 99.7143, 105.0, 110.2857, 115.5714, 120.8571, 126.1429, 131.4286, 136.7143, 142.0, continuing the pattern with these computed values.\n\n7143, 110.0, 115.2857, 120.5714, 125.8571, 131.1429, 136.4286, 141.7143, 4.1429, 9.4286, 14.7143, 20.0, 25.2857, 30.5714, 35.8571, 41.1429, 46.4286, 51.7143, 57.0, 62.2857, 67.5714, 72.8571, 78.1429, 83.4286, 88.7143, 94.0, 99.2857, 104.5714, 109.8571, 115.1429, 120.4286, 125.7143, 131.0, 136.2857, 141.5714, 4.0, 9.2857, 14.5714, 19.8571,\n\nThis sequence just keeps cycling through similar decimal patterns as the pattern continues to repeat.\n\n.5714, 24.8571, 30.1429, 35.4286, 40.7143, 46.0, 51.2857, 56.5714, 61.8571, 67.1429, 72.4286, 77.7143, 83.0, 88.2857, 93.5714, 98.8571, 104.1429, 109.4286, 114.7143, 120.0, 125.2857, 130.7143, 136.0, 141.2857, 3.5714, 8.8571, 14.1429, 19.4286, 24.7143, 30.0, 35.2857\n\nContinuing this pattern across rows, each shifted slightly by the step increment.\n\n.8571, 3.2857, 8.5714, ... 109.0] — 400 values total.\n\nSorting to find median (avg of 200th/201st). Since each cycle increases monotonically from a base, sorted order will group similar positions across cycles together.\n\nChecking cycle starting values: cycle 1 starts at 0.0, cycle 2 at 5.1429, cycle 3 at 5.0, cycle 4 at 4.8571, decreasing by ~0.1429 each subsequent cycle... cycle 9 would start around 4.0.\n\nContinuing: cycle 10 starts at 4.0, cycle 11 at 3.8571, cycle 12 at 3.7143, cycle 13 at 3.5714, cycle 14 at 3.4286, cycle 15 at 3.2857.\n\nSo aside from cycle 1 and the jump at cycle 2, values steadily decrease by 0.1429 per cycle.\n\nThe range spans 3.2857 to 142.7143 approximately. Given the sawtooth structure with 14-15 cycles of 28 entries each increasing by 5.2857, sorting will produce ascending small values first (0.0, then values around 3.29-3.86 from the low-start cycles), building up progressively to larger values from later cycle positions. The median in a sorted arrangement would land somewhere in the 70-75 range given the roughly uniform spread across cycles.\n\nWith 400 total values, finding the exact median requires locating the 200th and 201st sorted entries. Given the complexity of interleaved cycle values, this really needs to be computed programmatically rather than traced by hand.\n\nRoughly, with values ranging from 0 to ~142, the median should fall around the 50th percentile, likely somewhere near 70-71 based on the distribution shape, but I need to verify this more carefully by actually sorting the full dataset.\n\nThe starting points for cycles 1-15 are approximately: 0, 5.14, 5, 4.86, 4.71, 4.57, 4.43, 4.29, 4.14, 4, 3.86, 3.71, 3.57, 3.43, 3.29 - each cycle then increments by 5.2857 from its start.\n\nSorting these gives: 0, then 3.2857 through 5.1429 as the next 14 smallest. After that, values start repeating from the +5.2857 increments across different cycles, creating a messier interleaving around 8.57-10.57 range that needs careful sorting to identify the pattern.\n\nThis approach is too tedious to track manually. A direct computation would be more reliable: gather all 400 values, sort them, then take the average of index 199 and 200 (0-indexed) for the median. Using numpy's median function would give the answer immediately.\n\nGiven 14 full cycles of 28 values (392 total) plus 8 remaining values, I need to figure out where positions 199-200 land within this structure.\n\nCycle 15's 8th value: 3.2857 + 37 = 40.2857.\n\nSorting column by column: column 0 has values like 0, 3.2857, 3.4286... up through 5.1429 across the 14 cycles, and column 1 continues similarly with 5.1429, 5.2857, 8.5714...\n\nLet me lay out the fully sorted sequence: 0.0, 3.2857, 3.4286, 3.5714, 3.7143, 3.8571, 4.0, 4.1429, 4.2857, 4.4286, 4.5714, 4.7143, 4.8571, 5.0, 5.1429, then 5.2857, 8.5714...\n\nThis manual sorting approach is too messy. Better to think statistically - with 400 values in a repeating pattern, each \"column\" (the ith value across cycles) forms its own arithmetic sequence, and I should analyze those instead.\n\nSorting each column gives ordered sequences with consistent 0.1429 spacing. If all 400 values get sorted together, the columns would naturally group in order since column 0's range sits below column 1's, which sits below column 2's, and so on.\n\nBut with 400 = 14 complete cycles + 8 extra values, column 0 gets 15 entries while later columns get 14 or fewer from the incomplete final cycle.\n\nRow 0 corresponds to cycle 1, column 0 = 0; row 1 is cycle 1, column 1 = 5.2857; up through row 27 as cycle 1, column 27 = 142.7143. Then row 28 starts cycle 2, column 0 = 5.1429, row 29 = cycle 2, column 1 = 10.4286, and so on. Each column's values come from every 28th row, offset by the column index.\n\nFor column 0, the 15 values are: 0, 5.1429, 5.0, 4.8571, 4.7143, 4.5714, 4.4286, 4.2857, 4.1429, 4.0, 3.8571, 3.7143, 3.5714, 3.4286, 3.2857. Sorted, this gives 0, 3.2857, 3.4286, 3.5714, 3.7143, 3.8571, 4.0, 4.1429, and continuing upward.\n\nSimilarly, column 1's 15 values sort into an ascending sequence starting from 8.5714 through 10.0 and beyond.\n\nI need to double-check this - looking at the actual row values for column 1: 5.2857 at row 1, then 10.4286, 10.2857, decreasing down to 8.5714 at row 393. Sorting these: [5.2857, 8.5\n\n714, 8.7143, 8.8571, 9.0, 9.1429, 9.2857, 9.4286, 9.5714, 9.7143, 9.8571, 10.0, 10.1429, 10.2857, 10.4286].\n\nThe interleaved array combines column 0 (15 values: 0, 3.2857, ... 5.1429) then column 1 sorted values starting with 5.2857, 8.5714, 8.7143, 8.8571, 9.0, 9.1429...\n\nActually the columns will interleave once merged into a single sorted list rather than staying separate blocks.\n\nI need position 200-201 in the full sorted sequence. After the first 30 values (columns interleaved), I need roughly 170 more values to reach position 200. Column 2 contributes values like 10.5714, 13.8571, 14.0, 14.1429, up through 15.7143, adding another chunk to the sorted order.\n\nBut I realize this column-by-column approach isn't right—sorting all 400 values means merging everything globally, not stacking column ranges sequentially. I need to rethink this as a true full sort across all 400 values rather than treating columns as separate sorted blocks.\n\n195, offset=4, so position 200 falls in column 13's list (0-indexed), 4th element after the \"1+\" position.\n\nColumn k values start at 3.2857 + 5.1429*k roughly... let me just track pattern directly rather than formula, counting groups of 15 (14+1) per column after the first.\n\n857, 10.4286, 10.2857, 10.1429, 10.0, 9.8571, 9.7143, 9.5714, 9.4286, 9.2857, 9.1429, 9.0, 8.8571, 8.7143, 8.5714]\n\nThis whole approach is getting too tangled with tracking indices manually. I need a cleaner method to organize these values by column before sorting.\n\nColumn 0 gives us the smallest range, column 1 the middle range, column 2 the largest. Merging all three sorted sequences yields one continuous list: 0, 3.2857, 3.4286, 3.5714, 3.7143, 3.8571, 4.0, 4.1429, 4.2857, 4.4286, 4.5714, 4.7143, 4.8571, 5.0, 5.1429, 5.2857, 8.5714, continuing upward through the rest of column 1's values before reaching column 2's range.\n\nPosition tracking confirms 15 entries per column, so I map position 200 to 200 = 15×13 + 5, landing at column 13, index 5.\n\nPosition 201 similarly gives 15×13 + 6, index 6 in that same column.\n\nFor actual values, column 13 corresponds to the 14th row group (0-indexed), so the underlying data comes from row indices 13, 41, 69, 97, 125, 153, 181, 209... spaced by 28. Checking known rows: 68.7143, 73.8571, 79.0, 84.1429, 89.2857, 94.4286, 99.5714, and need row 209 next.\n\nRow 209: 104.7143, row 237: 109.8571, row 265: 115.0, row 293: 120.1429, row 321: 125.2857, row 349: 130.4286, row 377: 135.5714. Row 399 doesn't exist since data ends at 400, so only 14 values total for this column, already sorted from 68.7143 to 135.5714.\n\nThe 5th value is 89.2857, 6th is 94.4286, giving median (89.2857+94.4286)/2 = 91.85715.\n\nBut I should verify my column indexing - there are 28 columns per cycle (0-27), not 15, so I need to recompute which position this actually corresponds to.\n\nWith 400 total rows and 28-row cycles: 14 full cycles + 8 partial rows.\n- Columns 0-7: 15 values each (14 full cycles + 1 partial)\n- Columns 8-27: 14 values each (14 full cycles only)\n\nCheck: 8×15 + 20×14 = 120 + 280 = 400 ✓\n\nFor merging the sorted columns back into one list, I need to think about which values land where after the full sort-merge. The smallest values across all columns come first, then progressively larger ones - but the exact interleaving depends on each column's value range and distribution.\n\nGiven the pattern, each column roughly spans about 5.1 units and covers a distinct portion of the total range, with column 0 near 0 and column 27 near 148ish. This means after sorting, the merged list essentially reconstructs a near-continuous sequence across the full range, since each column's minimum is close to the previous column's maximum.\n\nIf I'm looking for a position around 200 in the sorted order, dividing by roughly 15 values per column suggests I land around column 13. Column 13's values would span roughly 68.7 to 115, following the pattern of 5.1429 increments — giving approximate values like 68.7143, 73.8571, 79.0, 84.1429, 89.2857, and onward.\n\nI check row indices 13, 41, 69, 97, 125, 153, 181, 209, matching each cycle for column 13, confirming values 68.7143, 73.8571, 79.0, 84.1429, 89.2857, 94.4286, 99.5714, 104.7143 line up correctly.\n\n265: 115.0 ✓, 293: 120.1429 ✓, 321: 125.2857 ✓, 349: 130.4286 ✓, 377: 135.5714 ✓ — 14 total values for column 13.\n\nSince columns 0-7 have 15 values and columns 8-27 have 14 values, the merged sorted list blocks align: positions 1-15 for column 0, 16-30 for column 1, and so on, with column 8 onward occupying 14-slot ranges starting after column 7's block.\n\nConfirming totals: 8×15 + 20×14 = 120 + 280 = 400 ✓. So positions 1-120 cover columns 0-7, and positions 121-400 cover columns 8-27 in 14-value chunks. Position 200 falls within 121-400, so I need to find which column block that lands in.\n\nSubtracting 120 gives 80 as the relative position within columns 8-27. Dividing 80 by 14: 5 full columns (8-12) plus a remainder gives me the count. Checking positions: column 8→1-14, column 9→15-28, column 10→29-42, column 11→43-56, column 12→57-70, column 13→71-84. Position 80 falls in column 13, at the 10th spot (80-70=10) within that column.\n\nColumn 13's sorted values run from 68.7143 up to 135.5714 in steps of ~5.143. The 10th value is 115.0 — this is position 200. Position 201 would be the 11th value, 120.1429.\n\nMedian = (115.0 + 120.1429)/2 = 117.57145.\n\nI should double check whether I'm indexing correctly — position 200 as the 200th element vs 0-indexed.\n\nFor n=400 (even), median = average of the 200th and 201st smallest values, which in 0-indexed terms are indices 199 and 200. I need to verify my column-position mapping lines up with this convention: columns of 15 values each, so column 7 spans positions 106-120 (indices 105-119).\n\nContinuing: column 8 covers positions 121-134 (indices 120-133), column 9 covers 135-148 (indices 134-147), column 10 covers 149-162 (indices 148-161), column 11 covers 163-176 (indices 162-175), column 12 covers 177-190 (indices 176-189), and column 13 covers 191-204 (indices 190-203).\n\nBoth index 199 and index 200 fall in column 13 — specifically the 10th and 11th values in that column's sorted list.\n\nColumn 13 values: [68.7143, 73.8571, 79.0, 84.1429, 89.2857, 94.4286, 99.5714, 104.7143, 109.8571, 115.0, 120.1429, 125.2857, 130.4286, 135.5714]\n\n10th value: 115.0, 11th value: 120.1429\n\nMedian = (115.0 + 120.1429)/2 = 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--- stderr ---
