diff --git a/Cargo.lock b/Cargo.lock
index 325cc11..da1c37a 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -250,7 +250,7 @@ checksum = "3304a64d199bb964be99741b7a14d26972741915b3649639149b2479bb46f4b5"
 
 [[package]]
 name = "lightguide"
-version = "0.2.1"
+version = "0.4.0"
 dependencies = [
  "fftw",
  "ndarray 0.15.6",
diff --git a/README.md b/README.md
index be29a91..944a0ec 100644
--- a/README.md
+++ b/README.md
@@ -104,4 +104,4 @@ pre-commit install
 
 Contribution and merge requests by the community are welcome!
 
-Lightguide was written by Marius Paul Isken and is licensed under the GNU GENERAL PUBLIC LICENSE v3.
+Lightguide was written by Marius Paul Isken and Juliane Starke and is licensed under the GNU GENERAL PUBLIC LICENSE v3.
diff --git a/examples/3-event-analysis.ipynb b/examples/3-event-analysis.ipynb
index 3b0c187..362d64b 100644
--- a/examples/3-event-analysis.ipynb
+++ b/examples/3-event-analysis.ipynb
@@ -25,19 +25,21 @@
     "3. Evaluate maximum cross-correlation in allowed window (`max_shift`).\n",
     "4. Update template trace and go to 2.\n",
     "\n",
-    "5. Repeat for upward neighbors."
+    "5. Repeat for upward neighbors.\n",
+    "\n",
+    "## Example 1 - Drop weight vsp-shot"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/jpeg": 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",
       "text/plain": [
-       "<Figure size 1200x600 with 1 Axes>"
+       "<Figure size 1200x600 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -49,37 +51,161 @@
     "import matplotlib.pyplot as plt\n",
     "from datetime import timedelta\n",
     "from lightguide.client import ExampleData\n",
-    "\n",
+    "from lightguide.blast import *\n",
     "\n",
     "# This will download example data\n",
     "blast = ExampleData.vsp_shot()\n",
     "\n",
     "# Pre-Processing\n",
     "blast = blast.trim_channels(begin=120, end=550)\n",
-    "blast = blast.trim_time(begin=1.5, end=2.5)\n",
+    "blast = blast.trim_time(begin=1.7, end=2.5)\n",
     "blast.lowpass(70.0)\n",
     "blast.afk_filter()\n",
     "\n",
-    "pick_time = blast.start_time + timedelta(seconds=0.573)\n",
-    "pick_channel = 350\n",
+    "pick_time = blast.start_time + timedelta(seconds=0.359)\n",
+    "pick = Picks(channel=[350],time = [pick_time],phase=['P'],kind=[1])\n",
     "\n",
     "# Follow the phase from initial pick\n",
-    "pick_channels, pick_times, pick_correlation = blast.follow_phase(\n",
-    "    pick_time,\n",
-    "    pick_channel,\n",
-    "    window_size=10,\n",
+    "picks = blast.follow_phase(\n",
+    "    picks=pick,\n",
+    "    window_size=(50,100),\n",
     "    max_shift=100,\n",
-    "    threshold=1e-2\n",
+    "    threshold=0.05,\n",
+    "    template_stacks=15,\n",
     ")\n",
     "\n",
     "fig = plt.figure(figsize=(12, 6))\n",
     "ax = fig.gca()\n",
     "\n",
     "blast.plot(axes=ax, cmap=\"binary\", show_date=True)\n",
-    "ax.scatter(pick_channels, pick_times, s=4, c=\"fuchsia\", alpha=0.6)\n",
+    "sc = ax.scatter(picks.channel, picks.time, s=4,c=picks.correlation, cmap=\"jet\", alpha=0.6)\n",
+    "cbar = plt.colorbar(sc)\n",
+    "cbar.set_label(\"pick correlation\")\n",
+    "# # Plot initial pick\n",
+    "ax.scatter(pick.channel, pick.time, marker=\"x\", c=\"k\");"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Save picks to a pyrocko markerfile...\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = 'markers_VSP-DAS-G1-120.txt'\n",
+    "picks.save_picks(filename=filename)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A blast (and the calculated picks) can also be visualized in the snuffler waveform browser or the built-in plot function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# merge initial pick with picks from phase follow\n",
+    "picks_joined = picks.add(picks=pick)\n",
+    "# built-in plot\n",
+    "blast.plot(picks=picks_joined,cmap='binary')\n",
+    "# snuffler\n",
+    "blast.snuffle(markers=picks_joined.as_markers())\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 2 - Icequake"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from lightguide.client import ExampleData\n",
+    "from datetime import timedelta\n",
+    "import matplotlib.pyplot as plt\n",
+    "from lightguide.blast import *\n",
+    "\n",
+    "# (down)load data\n",
+    "blast = ExampleData.icequake()\n",
     "\n",
+    "# Pre-Processing\n",
+    "blast = blast.trim_channels(begin=150, end=660)\n",
+    "blast = blast.trim_time(begin=0,end=0.8)\n",
+    "blast.lowpass(70.0)\n",
+    "blast.afk_filter()\n",
+    "\n",
+    "# define pick times\n",
+    "pick_time1 = blast.start_time + timedelta(seconds=0.288)\n",
+    "pick_time2 = blast.start_time + timedelta(seconds=0.551)\n",
+    "pick1 = Picks(channel=[365],time = [pick_time1],phase=['P'])\n",
+    "pick2 = Picks(channel=[365],time = [pick_time2],phase=['P'])\n",
+    "\n",
+    "# Follow the phase from initial pick\n",
+    "picks1 = blast.follow_phase(picks=pick1,\n",
+    "    window_size=20,\n",
+    "    max_shift=10,\n",
+    "    threshold=0.69,\n",
+    "    template_stacks=8,\n",
+    ")\n",
+    "picks2 = blast.follow_phase(picks=pick2,\n",
+    "    window_size=20,\n",
+    "    max_shift=10,\n",
+    "    template_stacks=8,\n",
+    ")\n",
+    "\n",
+    "fig = plt.figure(figsize=(12, 6))\n",
+    "ax = fig.gca()\n",
+    "blast.plot(axes=ax, cmap=\"binary\", show_date=True)\n",
+    "sc1 = ax.scatter(picks1.channel, picks1.time, s=4,c=picks1.correlation, cmap=\"jet\", alpha=0.6)\n",
+    "sc2 = ax.scatter(picks2.channel, picks2.time, s=4,c=picks2.correlation, cmap=\"jet\", alpha=0.6)\n",
+    "cbar = plt.colorbar(sc2)\n",
+    "cbar.set_label(\"pick correlation 2\")\n",
+    "cbar = plt.colorbar(sc1)\n",
+    "cbar.set_label(\"pick correlation 1\")\n",
     "# Plot initial pick\n",
-    "ax.scatter(pick_channel, pick_time, marker=\"x\", c=\"limegreen\");"
+    "ax.scatter(pick1.channel, pick1.time, marker=\"x\", c=\"k\"),ax.text(pick1.channel[0], pick1.time[0],'1',size=16,c=\"k\")\n",
+    "ax.scatter(pick2.channel, pick2.time, marker=\"x\", c=\"k\"),ax.text(pick2.channel[0], pick2.time[0],'2',size=16,c=\"k\");"
    ]
   }
  ],
@@ -99,7 +225,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.2"
+   "version": "3.10.12"
   },
   "orig_nbformat": 4
  },
diff --git a/lightguide/blast.py b/lightguide/blast.py
index 14fa55a..c07c562 100644
--- a/lightguide/blast.py
+++ b/lightguide/blast.py
@@ -1,4 +1,5 @@
 from __future__ import annotations
+from collections import deque
 
 import logging
 from copy import deepcopy
@@ -8,6 +9,7 @@
     TYPE_CHECKING,
     Any,
     Callable,
+    Deque,
     Iterable,
     Iterator,
     Literal,
@@ -19,11 +21,14 @@
 import numpy as np
 from matplotlib import colors, dates
 from matplotlib.colors import Colormap
-from pyrocko import io
+from pyrocko import io, pile, obspy_compat, trace
 from pyrocko.trace import Trace
-from scipy import signal
+from scipy import ndimage, signal
+import re
+import math
 
 from lightguide.utils import PathStr
+from lightguide.models.picks import *
 
 from .filters import afk_filter
 from .signal import decimation_coefficients
@@ -57,6 +62,7 @@ class Blast:
 
     start_channel: int
     channel_spacing: float
+    channel_list: np.ndarray
 
     def __init__(
         self,
@@ -65,6 +71,7 @@ def __init__(
         sampling_rate: float,
         start_channel: int = 0,
         channel_spacing: float = 0.0,
+        channel_list: list = [],
         unit: MeasurementUnit = "strain rate",
     ) -> None:
         """Create a new blast from NumPy array.
@@ -91,6 +98,10 @@ def __init__(
 
         self.start_channel = start_channel
         self.channel_spacing = channel_spacing
+        self.channel_list = channel_list
+
+        if len(self.channel_list) == 0:
+            self.channel_list = np.arange(start_channel, len(data), 1)
 
         self.processing_flow = []
 
@@ -112,7 +123,7 @@ def n_channels(self) -> int:
     @property
     def end_channel(self) -> int:
         """End Channel."""
-        return self.start_channel + self.n_channels
+        return self.channel_list[-1]
 
     @property
     def n_samples(self) -> int:
@@ -124,18 +135,68 @@ def duration(self) -> float:
         """Duration in seconds."""
         return self.n_samples * self.delta_t
 
+    def reduce_channels(self, n: int) -> None:
+        """Returns sparsed blast containing only every n-th channel"""
+        self.data = self.data[:-1:n, :]
+        self.channel_spacing = self.channel_spacing * n
+        self.channel_list = self.channel_list[:-1:n]
+
+    def exlude_channel(self, channel) -> None:
+        """Deletes selected channel, in-place.
+        Args:
+            channel (int): number of channel to be removed.
+        """
+        idx = self.get_channel_index(channel, strict=True)
+        if idx == None:
+            print(f"#{channel} not in range.")
+            return self
+        self.data = np.delete(self.data, idx, 0)
+        self.channel_list = np.delete(self.channel_list, idx)
+
+    def exlude_channels(self, channels) -> None:
+        """Deletes channels given in list from blast, in-place."""
+        for channel in channels:
+            self.exlude_channel(channel=channel)
+
+    def get_channel_name(self, channel_index: int) -> int:
+        """Gets name of channel from it's index as given in channel_list.
+        Args:
+            channel_index (int): index of channel of interest.
+        Returns:
+            int: Channel name.
+        """
+        return self.channel_list[channel_index]
+
+    def get_channel_index(self, channel: int, strict=True) -> int:
+        """Finds index of a given channel or channel closest to it and returns it.
+        Args:
+            channel (int): Channel name.
+            strict (bool): if False, return channel closest to channel
+        Returns:
+            int: channel index
+        """
+        channels = self.channel_list
+        idx = (np.abs(channels - channel)).argmin()
+        # idx = np.searchsorted(channels, channel, side="left")  # maybe faster??
+        if channels[idx] == channel:
+            return idx
+        elif strict == False:
+            return idx
+        else:
+            raise ValueError(
+                f"#{channel} not in channel list. To get closest channel (#{channels[idx]}) set 'strict==False'."
+            )
+
     def get_trace(self, channel: int) -> np.ndarray:
         """Get data from a singular channel.
 
         Args:
-            channel (int): Channel number.
+            channel (int): Channel name.
 
         Returns:
             np.ndarray: 1D Trace.
         """
-        if not self.start_channel <= channel < self.end_channel:
-            raise ValueError(f"Channel {channel} is out of bounds")
-        return self.data[channel - self.start_channel]
+        return self.data[self.get_channel_index(channel)]
 
     def _time_to_sample(self, time: datetime) -> int:
         """Get sample index for a time.
@@ -297,6 +358,21 @@ def bandpass(
         else:
             self.data = signal.sosfilt(sos, self.data, axis=1)
 
+    def wiener_filter(self, size: int = 5):
+        """Apply Wiener filter in-place.
+        Args:
+            size (int, optional): Size of the footprint. Defaults to 5.
+        """
+        self.data = signal.wiener(self.data, mysize=size)
+
+    def median_filter(self, size: int = 3) -> None:
+        """Apply median filter in-place.
+        Args:
+            size (int, optional): Footprint of the filter.
+                Defaults to 3.
+        """
+        self.data = ndimage.median_filter(self.data, size=size)
+
     def afk_filter(
         self,
         exponent: float = 0.8,
@@ -323,13 +399,24 @@ def afk_filter(
             normalize_power=normalize_power,
         )
 
+    def average_traces(self, no_of_traces) -> Blast:
+        """Average over number of neighbouring traces, in place.
+        Args:
+            no_of_traces (int): number of channels to be used for averaging
+        """
+        kernel = np.ones(shape=(no_of_traces, 1)) / no_of_traces
+        avs = signal.fftconvolve(self.data, kernel, mode="valid")
+        self.data = avs
+
     def follow_phase(
         self,
-        pick_time: datetime,
-        pick_channel: int,
+        picks: Picks,
         window_size: int | tuple[int, int] = 50,
         threshold: float = 5e-1,
         max_shift: int = 20,
+        template_stacks: int = 1,
+        taper: bool = False,
+        correlation_mode="same",
     ) -> tuple[np.ndarray, list[datetime], np.ndarray]:
         """Follow a phase pick through a Blast.
 
@@ -337,15 +424,14 @@ def follow_phase(
         phase through iterative cross-correlation of neighboring traces:
 
         1. Get windowed root pick template.
-        2. Calculate normalized cross correlate with downwards neighbor.
+        2. Calculate normalized cross correlate with downwards neighbor (window of data trace is twice as long as template for correlation).
         3. Evaluate maximum x-correlation in allowed window (max_shift).
         4. Update template trace and go to 2.
-
+            4a. if template_stacks > 1: stack templates for correlation to stabilize
         5. Repeat for upward neighbors.
 
         Args:
-            pick_time (datetime): Initial pick in the data set.
-            pick_channel (int): Corresponding channel for the initial pick.
+            picks (Picks): Lightguide Picks object containing the 'seeds' for the correlation. If more than one pick time is given a phase will be followed up to the channel positioned in the middle of two seed picks.
             window_size (int | tuple[int, int], optional): Window size around the pick
                 for the correlation template. If an integer is given it is the
                 half-width. A Tuple gives pre- and post-windows. Defaults to 50.
@@ -353,6 +439,12 @@ def follow_phase(
                 Defaults to 5e-1.
             max_shift (int, optional): Maximum allowed shift in samples for
                 neighboring picks. Defaults to 20.
+            template_stacks (int): Numbers of traces to stack to define the template. Default is 1,
+                i.e. a single trace.
+                Stacking close to root template is limited by the distance to the
+                root template.
+            taper (bool) = False: taper trace & template before correlating
+            correlation_mode (str): choose numpy correlation mode valid arguments are 'same','full' & 'valid'
 
         Returns:
             tuple[np.ndarray, list[datetime], np.ndarray]: Tuple of channel number,
@@ -361,63 +453,171 @@ def follow_phase(
         if isinstance(window_size, int):
             window_size = (window_size, window_size)
 
-        pick_channel -= self.start_channel
-        root_idx = self._time_to_sample(pick_time)
-
-        # Ensure the window is odd-sized with the pick in center.
-        root_template = self.get_trace(pick_channel)[
-            root_idx - window_size[0] : root_idx + window_size[1] + 1
-        ].copy()
-        template_taper = signal.windows.tukey(root_template.size, alpha=0.1)
+        picks.sort_by(attribute="channel")
+        # select only pics that are in channel list
+        inlist = np.where(np.isin(picks.channel, self.channel_list))
+        for attr, values in picks:
+            if len(values) == 0:
+                continue
+            newvals = []
+            for idx in inlist[0]:
+                newvals.append(values[idx])
+            setattr(picks, attr, newvals)
+        # set phase name to 'P' if not determined otherwise in first pick in list
+        if picks.phase[0] == []:
+            phase = ["P"]
+        else:
+            phase = [picks.phase[0]]
 
+        # empty lists to store results
         pick_channels, pick_times, pick_correlations = [], [], []
 
-        def prepare_template(data: np.ndarray) -> np.ndarray:
-            return data * template_taper
-
-        def correlate(data: np.ndarray, direction: Literal[1, -1] = 1) -> None:
-            template = root_template.copy()
-            index = root_idx
-
-            for ichannel, trace in enumerate(data):
-                template = prepare_template(template)
-                norm = np.sqrt(np.sum(template**2)) * np.sqrt(np.sum(trace**2))
-                correlation = np.correlate(trace, template, mode="same")
-                correlation = np.abs(correlation / norm)
-
-                # Take maximum only in allowed window
-                phase_idx = (
-                    np.argmax(correlation[index - max_shift : index + max_shift + 1])
-                    + index
-                    - max_shift
+        # set up deque to find indexes for correlation windows (if more than one pick time is given)
+        first_channel = self.channel_list[0]
+        chs = [value for value in picks.channel if value in self.channel_list]
+        d = deque([first_channel, first_channel, first_channel], maxlen=3)
+
+        # loop over all seeds
+        for i in range(0, len(chs)):
+            if i == len(chs) - 1:  # fill deque with right inex numbers
+                d.append(chs[i])
+                d.append(self.channel_list[-1])
+            else:
+                d.append(chs[i])
+                d.append(int((chs[i] + chs[i + 1]) / 2))
+
+            try:
+                sta_idx = self.get_channel_index(d[0])
+                sto_idx = self.get_channel_index(d[2])
+            except:
+                continue
+
+            # get seed pick times and channel
+            pick_channel = picks.channel[i]
+            pick_time = picks.time[i]
+
+            root_idx_abs = self._time_to_sample(pick_time)
+
+            root_template = self.get_trace(pick_channel)[
+                root_idx_abs - window_size[0] : root_idx_abs + window_size[1] + 1
+            ].copy()
+
+            if taper:  # apply taper, if True
+                template_taper = signal.windows.tukey(root_template.size, alpha=0.1)
+                trace_taper = signal.windows.tukey(
+                    root_template.size + window_size[0] + window_size[1], alpha=0.1
+                )
+            else:
+                template_taper = np.ones(root_template.size)
+                trace_taper = np.ones(
+                    root_template.size + window_size[0] + window_size[1]
                 )
 
-                phase_correlation = correlation[phase_idx]
-                phase_time = self._sample_to_time(int(phase_idx))
-
-                if phase_correlation < threshold:
-                    continue
-
-                # Avoid the edges
-                if not window_size[0] < phase_idx < self.n_samples - window_size[1]:
-                    continue
-
-                pick_times.append(phase_time)
-                pick_channels.append(pick_channel + ichannel * direction)
-                pick_correlations.append(phase_correlation)
-
-                template = trace[
-                    phase_idx - window_size[0] : phase_idx + window_size[1] + 1
-                ].copy()
-                index = phase_idx
-
-        correlate(self.data[pick_channel:])
-        correlate(self.data[: pick_channel - 1][::-1], direction=-1)
+            def prepare_template(data: Deque[np.ndarray]) -> np.ndarray:
+                data = np.mean(data, axis=0)
+                return data * template_taper
 
-        pick_channels = np.array(pick_channels) + self.start_channel
-        pick_correlations = np.array(pick_correlations)
+            def correlate(data: np.ndarray, direction: Literal[1, -1] = 1) -> None:
+                template = root_template.copy()
+                template_stack: Deque[np.ndarray] = deque(
+                    [np.array(template)], maxlen=template_stacks
+                )
 
-        return pick_channels, pick_times, pick_correlations
+                # set up a padding tuple in case correlation window reaches end of trace, traec will be padded with zeros
+                padding = (0, 0)
+                pick_idx_abs = root_idx_abs  # absolute index of first pick
+                for ichannel, trace in enumerate(data):
+                    template = prepare_template(template_stack)
+
+                    # select window of interest
+                    if padding[0] > 0:
+                        tr = trace[
+                            pick_idx_abs
+                            - 2 * window_size[0]
+                            + padding[0] : pick_idx_abs
+                            + 2 * window_size[1]
+                            + 1
+                        ]
+                        tr = np.insert(tr, 0, np.zeros(padding[0]))
+                    elif padding[1] > 0:
+                        tr = trace[
+                            pick_idx_abs
+                            - 2 * window_size[0] : pick_idx_abs
+                            + 2 * window_size[1]
+                            + 1
+                            - padding[1]
+                        ]
+                        tr = np.append(tr, np.zeros(padding[1]))
+                    else:
+                        tr = trace[
+                            pick_idx_abs
+                            - 2 * window_size[0] : pick_idx_abs
+                            + 2 * window_size[1]
+                            + 1
+                        ]
+                    tr = tr * trace_taper
+
+                    # correlate traces normalize them and find lag time
+                    norm = np.sqrt(np.sum(template**2)) * np.sqrt(np.sum(tr**2))
+                    correlation = np.correlate(tr, template, mode=correlation_mode)
+                    correlation = np.abs(correlation / norm)
+                    lags = signal.correlation_lags(
+                        tr.size, template.size, mode=correlation_mode
+                    )
+                    max_corr_idx = np.argmax(correlation)
+                    lag = lags[max_corr_idx]
+                    temp_idx = lag + window_size[0]
+                    temp_idx_abs = pick_idx_abs - 2 * window_size[0] + temp_idx
+
+                    phase_correlation = correlation[max_corr_idx]
+                    if phase_correlation < threshold:
+                        continue
+
+                    # Avoid the edges
+                    if (temp_idx_abs - 2 * window_size[0]) < 0:
+                        print("Trace too short! Zero padding in front.")
+                        print(pick_channel_idx + ichannel * direction)
+                        padding = (temp_idx_abs - 2 * window_size[0], 0)
+                    elif (temp_idx_abs + 2 * window_size[1]) > len(trace):
+                        padding = (0, len(trace) - temp_idx_abs + 2 * window_size[1])
+                        print("Trace too short! Zero padding in end.")
+                        print(pick_channel_idx + ichannel * direction)
+
+                    # set new absolute pick index if it is within max shift
+                    if abs(pick_idx_abs - temp_idx_abs) < max_shift:
+                        pick_idx_abs = temp_idx_abs
+
+                    phase_time = self._sample_to_time(int(pick_idx_abs))
+                    pick_times.append(phase_time)
+                    pick_channels.append(pick_channel_idx + ichannel * direction)
+                    pick_correlations.append(phase_correlation)
+
+                    # update template
+                    template = trace[
+                        pick_idx_abs
+                        - window_size[0] : pick_idx_abs
+                        + window_size[1]
+                        + 1
+                    ]
+
+                    template_stack.append(template)
+
+            pick_channel_idx = self.get_channel_index(pick_channel)
+            correlate(self.data[pick_channel_idx:sto_idx])
+            correlate(self.data[sta_idx : (pick_channel_idx + 1)][::-1], direction=-1)
+
+            channels = []
+            for idx in pick_channels:
+                channels.append(self.get_channel_name(idx))
+
+        phase = phase * len(pick_times)
+
+        return Picks(
+            channel=channels,
+            time=pick_times,
+            correlation=pick_correlations,
+            phase=phase,
+        )
 
     def taper(self, alpha: float = 0.05) -> None:
         """Taper in time-domain and in-place with a Tukey window.
@@ -459,7 +659,10 @@ def trim_channels(self, begin: int = 0, end: int = -1) -> Blast:
             Blast: Trimmed Blast.
         """
         blast = self.copy()
+        begin = blast.get_channel_index(begin, strict=False)
+        end = blast.get_channel_index(end, strict=False)
         blast.start_channel += begin
+        blast.channel_list = blast.channel_list[begin:end]
         blast.data = blast.data[begin:end]
         return blast
 
@@ -486,6 +689,45 @@ def trim_time(self, begin: float = 0.0, end: float = -1.0) -> Blast:
         blast.start_time += timedelta(seconds=begin)
         return blast
 
+    def trim_from_picks(
+        self, picks: Picks, time_window: int | tuple[int, int] = 1
+    ) -> list[Trace]:
+        """Trims channels to a time window defined around a given pick time.
+
+        Args:
+            picks (Picks):
+            time_window (int|tuple): Window size [s] around the pick. If an integer is given
+            only time window after pick is chosen. A Tuple gives pre- and post-windows.
+            Default is to 1 s post window.
+
+        Returns:
+            List: of trimmed pyrocko traces
+        """
+        if not isinstance(time_window, tuple):
+            time_window = (0, time_window)
+
+        blast = self.copy()
+        blast = blast.as_traces()
+
+        channels = picks.channel
+        times = picks.time
+
+        trimmed_traces = []
+        for channel, time in zip(channels, times):
+            time = time.timestamp()
+            # find channel
+            tr = next((x for x in blast if int(x.station) == channel), None)
+            if tr == None:
+                continue
+            # check if marker is in time range of trace
+            if not tr.time_span[0] <= time <= tr.time_span[1]:
+                continue
+
+            trchop = tr.chop(tmin=time - time_window[0], tmax=time + time_window[1])
+            trimmed_traces.append(trchop)
+
+        return trimmed_traces
+
     def to_strain(self, detrend: bool = True) -> Blast:
         """Convert the traces to strain.
 
@@ -557,6 +799,7 @@ def plot(
         cmap: str | Colormap = "seismic",
         show_date: bool = False,
         show_channel: bool = False,
+        picks: Picks = None,
     ) -> image.AxesImage:
         """Plot data of the blast.
 
@@ -590,19 +833,48 @@ def plot(
             dates.date2num(self.start_time) if show_date else 0.0,
         )
 
-        data = self.data.copy()
+        data = self.data.copy().astype(float)
         if normalize_traces:
             data /= np.abs(data.max(axis=1, keepdims=True))
 
         img = ax.imshow(
             data.T,
             aspect="auto",
-            # interpolation="nearest",
+            interpolation="nearest",
             cmap=cmap,
             extent=extent,
             norm=colors.CenteredNorm(),
         )
 
+        def plot_picks(ax, picks):
+            # marker kind specifiying color
+            colours = {
+                0: "r",
+                1: "lightgreen",
+                2: "b",
+                3: "plum",
+                4: "brown",
+                5: "y",
+                6: "grey",
+            }
+            if show_channel:
+                ch = picks.channel
+            else:
+                ch = [c * d_channel for c in picks.channel]
+            if show_date:
+                tt = picks.time
+            else:
+                tt = [self._time_to_sample(t) * self.delta_t for t in picks.time]
+
+            if len(picks.kind) > 0:
+                c = [colours[k] for k in picks.kind]
+            else:
+                c = "k"
+            ax.scatter(ch, tt, marker="x", c=c)
+
+        if picks != None:
+            plot_picks(ax=ax, picks=picks)
+
         ax.set_ylabel("Time [s]")
         if show_date:
             ax.set_ylabel("Time UTC")
@@ -641,11 +913,11 @@ def as_traces(self) -> list[Trace]:
         """
 
         traces = []
-        for icha in range(self.n_channels):
-            channel = icha + self.start_channel
+        for icha, data in enumerate(self.data):
+            channel = self.channel_list[icha]
             traces.append(
                 Trace(
-                    ydata=self.data[icha],
+                    ydata=data,
                     tmin=self.start_time.timestamp(),
                     deltat=self.delta_t,
                     station=f"{channel:05d}",
@@ -653,6 +925,20 @@ def as_traces(self) -> list[Trace]:
             )
         return traces
 
+    def to_obspy_stream(self):
+        """Converts blast to an obspy stream
+
+        Returns:
+            Obspy stream containing traces of blast.
+        """
+        p = pile.Pile()
+        p.add(self.as_traces())
+        return obspy_compat.to_obspy_stream(p)
+
+    def snuffle(self, **kwargs) -> None:
+        """Show traces of blast in a snuffler window."""
+        trace.snuffle(self.as_traces(), **kwargs)
+
     @classmethod
     def from_pyrocko(cls, traces: list[Trace], channel_spacing: float = 4.0) -> Blast:
         """Create Blast from a list of Pyrocko traces.
@@ -671,7 +957,8 @@ def from_pyrocko(cls, traces: list[Trace], channel_spacing: float = 4.0) -> Blas
         if not traces:
             raise ValueError("Empty list of traces")
 
-        traces = sorted(traces, key=lambda tr: int(tr.station))
+        traces = sorted(traces, key=lambda tr: int(re.sub(r"\D", "", tr.station)))
+        channel_list = np.array([int(re.sub(r"\D", "", tr.station)) for tr in traces])
         ntraces = len(traces)
 
         tmin = set()
@@ -702,8 +989,10 @@ def from_pyrocko(cls, traces: list[Trace], channel_spacing: float = 4.0) -> Blas
             data=data,
             start_time=datetime.fromtimestamp(tmin.pop(), tz=timezone.utc),
             sampling_rate=int(1.0 / delta_t.pop()),
-            start_channel=min(int(tr.station) for tr in traces),
+            # start_channel=min(int(re.sub(r"\D", "", tr.station)) for tr in traces),
+            start_channel=channel_list[0],
             channel_spacing=channel_spacing,
+            channel_list=channel_list,
         )
 
     @classmethod
@@ -721,7 +1010,10 @@ def from_miniseed(cls, file: PathStr, channel_spacing: float = 4.0) -> Blast:
         from pyrocko import io
 
         traces = io.load(str(file), format="mseed")
-        return cls.from_pyrocko(traces, channel_spacing=channel_spacing)
+        return cls.from_pyrocko(
+            traces,
+            channel_spacing=channel_spacing,
+        )
 
 
 TFun = TypeVar("TFun", bound=Callable[..., Any])
@@ -791,11 +1083,6 @@ def __len__(self) -> int:
 
     mute_median = shared_function(Blast.mute_median)
     one_bit_normalization = shared_function(Blast.one_bit_normalization)
-    afk_filter = shared_function(Blast.afk_filter)
-    decimate = shared_function(Blast.decimate)
-
-    trim_time = shared_function(Blast.trim_time)
-    trim_channels = shared_function(Blast.trim_channels)
 
     to_strain = shared_function(Blast.to_strain)
     to_relative_velocity = shared_function(Blast.to_relative_velocity)
diff --git a/lightguide/client.py b/lightguide/client.py
index 94bc0ea..843fc15 100644
--- a/lightguide/client.py
+++ b/lightguide/client.py
@@ -2,6 +2,7 @@
 
 import numpy as np
 
+from pathlib import Path
 from lightguide.blast import Blast
 from lightguide.utils import cache_dir, download_http
 
@@ -10,6 +11,8 @@ class ExampleData:
     VSPDataUrl = "https://data.pyrocko.org/testing/lightguide/VSP-DAS-G1-120.mseed"
     DataUrl = "https://data.pyrocko.org/testing/lightguide/das-data.npy"
     EQDataUrl = "https://data.pyrocko.org/testing/lightguide/data-DAS-gfz2020wswf.npy"
+    IceQDataUrl = "https://github.com/sachalapins/DAS-N2N/raw/main/data/BPT1_UTC_20200117_044207.903.mseed"
+    # MarkersUrl = "https://data.pyrocko.org/testing/lightguide/markers_VSP-DAS-G1-120.txt"
 
     @classmethod
     def earthquake(cls) -> Blast:
@@ -31,3 +34,24 @@ def vsp_shot(cls) -> Blast:
             download_http(cls.VSPDataUrl, file)
 
         return Blast.from_miniseed(file, channel_spacing=1.0)
+
+    @classmethod
+    def icequake(cls) -> Blast:
+        file = cache_dir() / "BPT1_UTC_20200117_044207.903.mseed"
+        if not file.exists():
+            download_http(cls.IceQDataUrl, file)
+
+        return Blast.from_miniseed(file, channel_spacing=1.0)
+
+    @classmethod
+    def markerfile(cls) -> Path:
+        file = Path("../examples/markers_VSP-DAS-G1-120.txt")
+        if not file.exists():
+            print(
+                "Markerfile has not yet been created.\n \
+                to create a markerfile please see EXAMPLE 3"
+            )
+            # file = cache_dir() / "markers_VSP-DAS-G1-120.txt"
+            # download_http(cls.MarkersUrl, file)
+
+        return file
diff --git a/lightguide/models/picks.py b/lightguide/models/picks.py
new file mode 100644
index 0000000..beb7b1d
--- /dev/null
+++ b/lightguide/models/picks.py
@@ -0,0 +1,143 @@
+from pydantic import BaseModel
+from typing import List, Tuple, Dict
+from datetime import datetime
+from pyrocko import marker
+import numpy as np
+from lightguide.utils import PathStr
+
+
+class Picks(BaseModel):
+    channel: list[int]
+    time: list[datetime]
+    correlation: list[float] = []
+    kind: list[int] = []
+    phase: list[str] = []
+
+    def save_picks(self, filename: PathStr) -> None:
+        """
+        Saves picks as a pyrocko markerfile.
+
+        Args:
+            filename (str): path to filename
+        """
+        marker.save_markers(markers=self.as_markers(), filename=str(filename))
+
+    def add(self, picks) -> "Picks":
+        """Adds picks to existing picks object and returns new pick.
+        Args:
+            picks (Picks): picks to add
+        """
+        mlist = self.as_markers()
+        mlist.extend(picks.as_markers())
+        return Picks.from_pyrocko_markers(mlist)
+
+    def as_markers(self) -> list[marker.PhaseMarker]:
+        """
+        Converts picks object to pyrocko markers.
+        """
+        channels = self.channel
+        times = self.time
+        kinds = self.kind
+        phases = self.phase
+
+        if not self.kind:
+            kinds = [0] * len(channels)
+        if not self.phase:
+            phases = [""] * len(channels)
+
+        markers = []
+        for ch, ptime, kind, phase in zip(channels, times, kinds, phases):
+            nslc_id = [("", "%05d" % ch, "", "")]
+            tmin = ptime.timestamp()
+            m = marker.PhaseMarker(
+                nslc_ids=nslc_id, tmin=tmin, tmax=tmin, kind=kind, phasename=phase
+            )
+            markers.append(m)
+        return markers
+
+    def as_array(self):
+        """
+        Transforms Picks lists into numpy array.
+        """
+        N = len(self.channel)
+        temp = []
+        for _, values in self:
+            if len(values) > 0:
+                temp.append(values)
+            else:
+                temp.append(N * [None])
+        return np.transpose(np.asarray(temp))
+
+    # @classmethod
+    def sort_by(self, attribute: str = "channel", reverse: bool = False):
+        """
+        Sort Picks by a given attribute, in place. Default: sorting by channel number.
+
+        Args:
+            attribute (str): attribute to be sorted by, default: channel (number). Options: 'channel', 'time','correlation','kind','phase'
+            reverse (bool): sorting reversed or normal, default: False
+        Returns:
+            self: sorted by given criteria
+        """
+        # find column index of attribute to be sorted by
+        for i, (attr, _) in enumerate(self):
+            if attr == attribute:
+                col = i
+
+        # convert to array & check if attribute is specified
+        temp = self.as_array()
+        if temp[0, col] == None:
+            print("Attribute not specified. Default: sorting by channel.")
+            col = 0
+        # do the sorting
+        temp = temp[temp[:, col].argsort()]
+        if reverse == True:
+            temp = temp[::-1]
+
+        # update entries of picks object
+        for i, (attr, _) in enumerate(self):
+            if temp[0, i] == None:
+                setattr(self, attr, [])
+            else:
+                setattr(self, attr, list(temp[:, i]))
+
+    def get_picktime(self, channel: int) -> datetime | None:
+        """
+        Returns time of pick of a selected channel (first occurence).
+
+        Args:
+            channel (int): name of channel
+        Returns:
+            pick time (datetime| None): pick time at channel (first occurence in list) of channel not in list, returns none
+        """
+        try:
+            idx = self.channel.index(channel)
+            return self.time[idx]
+        except:
+            return None
+
+    @classmethod
+    def from_pyrocko_markers(cls, markers: List) -> "Picks":
+        """
+        Converts list of pyrocko markers to a Picks object
+
+        Agrs:
+            markers (list): list of pyrocko markers
+        """
+        channels = ["%05d" % int(m.nslc_ids[0][1]) for m in markers]
+        times = [m.tmin for m in markers]
+        kinds = [m.kind for m in markers]
+        phases = [m._phasename for m in markers]
+
+        return Picks(channel=channels, time=times, kind=kinds, phase=phases)
+
+    @classmethod
+    def from_pyrocko_picks(cls, filename: PathStr) -> "Picks":
+        """
+        Loads pyrocko picks from file.
+
+        Agrs:
+            filename (str): filename to read
+        """
+        markers = marker.load_markers(filename)
+        return Picks.from_pyrocko_markers(markers=markers)
diff --git a/pyproject.toml b/pyproject.toml
index 5dfa890..a15ab9b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -11,7 +11,8 @@ name = "lightguide"
 description = "Tools for distributed acoustic sensing and modelling."
 dynamic = ["version"]
 requires-python = ">=3.8"
-authors = [{ name = "Marius Paul Isken" }, { email = "mi@gfz-potsdam.de" }]
+authors = [{ name = "Marius Paul Isken" }, { email = "mi@gfz-potsdam.de" },
+{ name = "Juliane Starke" }, { email = "justarke@gfz-potsdam.de" }]
 keywords = [
   "distributed acoustic sensing",
   "DAS",