diff --git a/hypy_utils/__init__.py b/hypy_utils/__init__.py
index b7393dc..d18117b 100644
--- a/hypy_utils/__init__.py
+++ b/hypy_utils/__init__.py
@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-__version__ = "1.0.7"
+__version__ = "1.0.8"
 
 import dataclasses
 import hashlib
@@ -8,7 +8,7 @@ import json
 import time
 from datetime import datetime, date
 from pathlib import Path
-from typing import Union
+from typing import Union, Callable
 
 
 def ansi_rgb(r: int, g: int, b: int, foreground: bool = True) -> str:
@@ -202,3 +202,16 @@ class Timer:
 
     def reset(self):
         self.start = time.time_ns()
+
+
+def mem(var: str):
+    print(f'Memory usage for {var}: {eval(f"sys.getsizeof({var})") / 1024:.1f}KB')
+
+
+def run_time(func: Callable, *args, **kwargs):
+    name = getattr(func, '__name__', 'function')
+    start = time.time_ns()
+    iter = kwargs.pop('iter', 10)
+    _ = [func(*args, **kwargs) for _ in range(iter)]
+    ms = (time.time_ns() - start) / 1e6
+    print(f'RT {name:30} {ms:6.1f} ms')
diff --git a/hypy_utils/scientific_utils.py b/hypy_utils/scientific_utils.py
new file mode 100644
index 0000000..5e3519d
--- /dev/null
+++ b/hypy_utils/scientific_utils.py
@@ -0,0 +1,72 @@
+"""
+Importing this file requires numpy, matplotlib, and numba
+"""
+from __future__ import annotations
+
+import time
+from dataclasses import dataclass
+from typing import Callable
+
+import numpy as np
+from matplotlib import pyplot as plt
+from numba import njit
+
+
+@dataclass
+class Statistics:
+    mean: float
+    median: float
+    lower_quartile: float
+    upper_quartile: float
+    iqr: float
+    minimum: float
+    maximum: float
+    count: int
+    total: float
+
+    def get_metric_6(self) -> tuple[float, float, float, float, float, float]:
+        return self.mean, self.median, self.minimum, self.maximum, self.lower_quartile, self.upper_quartile
+
+
+@njit(cache=True)
+def _calc_col_stats_helper(col: np.ndarray) -> tuple[float, float, float, float, float, float, float, int, float]:
+    q1 = np.quantile(col, 0.25)
+    q3 = np.quantile(col, 0.75)
+    return (
+        float(np.mean(col)),
+        float(np.median(col)),
+        float(q1),
+        float(q3),
+        float(q3 - q1),
+        float(np.min(col)),
+        float(np.max(col)),
+        len(col),
+        float(np.sum(col))
+    )
+
+
+def calc_col_stats(col: np.ndarray | list) -> Statistics:
+    """
+    Compute statistics for a data column
+
+    :param col: Input column (tested on 1D array)
+    :return: Statistics
+    """
+    if isinstance(col, list):
+        col = np.array(col)
+    return Statistics(*_calc_col_stats_helper(col))
+
+
+def plot(**kwargs) -> plt:
+    """
+    Pyplot configurator shorthand
+
+    Example: plt_cfg(xlabel="X", ylabel="Y") is equivalent to plt.xlabel("X"); plt.ylabel("Y")
+    """
+    for k, args in kwargs.items():
+        if isinstance(args, dict):
+            getattr(plt, k)(**args)
+        else:
+            getattr(plt, k)(args)
+    return plt
+
diff --git a/hypy_utils/serializer.py b/hypy_utils/serializer.py
index 736ba28..2f4e6d1 100644
--- a/hypy_utils/serializer.py
+++ b/hypy_utils/serializer.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import io
 import pickle
 
@@ -6,7 +8,11 @@ def pickle_encode(obj: any, protocol=None, fix_imports=True) -> bytes:
     """
     Encode object to pickle bytes
 
-    >>> by = pickle_encode({'meow': 565656})
+    >>> by = pickle_encode({'function': pickle_encode})
+    >>> len(by)
+    57
+    >>> decoded = pickle_decode(by)
+    >>> by = decoded['function']({'meow': 565656})
     >>> pickle_decode(by)
     {'meow': 565656}
     """
diff --git a/hypy_utils/tqdm_utils.py b/hypy_utils/tqdm_utils.py
new file mode 100644
index 0000000..3d48ab6
--- /dev/null
+++ b/hypy_utils/tqdm_utils.py
@@ -0,0 +1,37 @@
+"""
+Importing this file requires installing tqdm.
+"""
+from __future__ import annotations
+
+from functools import partial
+from typing import Callable, Iterable
+
+import tqdm
+from tqdm.contrib.concurrent import process_map, thread_map
+
+
+def smap(fn: Callable, lst: Iterable, *args, **kwargs) -> list:
+    return [fn(i) for i in tqdm.tqdm(lst, position=0, leave=True)]
+
+
+def pmap(fn: Callable, lst: Iterable, *args, **kwargs) -> list:
+    tqdm_args = dict(position=0, leave=True, chunksize=1, tqdm_class=tqdm.tqdm, max_workers=os.cpu_count())
+    return process_map(fn, lst, *args, **{**tqdm_args, **kwargs})
+
+
+def tmap(fn: Callable, lst: Iterable, *args, **kwargs) -> list:
+    tqdm_args = dict(position=0, leave=True, chunksize=1, tqdm_class=tqdm.tqdm, max_workers=os.cpu_count())
+    return process_map(fn, lst, *args, **{**tqdm_args, **kwargs})
+
+
+def tq(it: Iterable, desc: str, *args, **kwargs) -> tqdm:
+    tqdm_args = dict(position=0, leave=True)
+    return tqdm.tqdm(it, desc, *args, **{**tqdm_args, **kwargs})
+
+
+def patch_tqdm():
+    tqdm_args = dict(chunksize=1, position=0, leave=True, tqdm_class=tqdm.tqdm, max_workers=os.cpu_count())
+    tq: Callable[[Iterable], tqdm.tqdm] = partial(tqdm.tqdm, position=0, leave=True)
+    pmap = partial(process_map, **tqdm_args)
+    tmap = partial(thread_map, **tqdm_args)
+    return tq, pmap, tmap