diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
new file mode 100644
index 0000000..3898aa8
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -0,0 +1,28 @@
+---
+name: Bug Report
+about: Create a bug report about an issue with the library
+
+---
+
+
+**Describe the bug**
+
+<INSERT TEXT HERE>
+
+**To Reproduce**
+
+Steps to reproduce the behavior:
+
+1.
+2.
+3.
+
+**Expected Behavior**
+
+<INSERT TEXT HERE>
+
+**python-adc-eval version:** <Library version>
+
+**Additional Information**
+
+<Any other information relevent to the bug report>
diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md
new file mode 100644
index 0000000..67815eb
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/feature_request.md
@@ -0,0 +1,13 @@
+---
+name: Feature request
+about: Suggest a new feature for this library to support
+
+---
+
+**Description**
+
+<Clear and concise description of the feature>
+
+**Additional Information**
+
+<Additional information relevent to the feature request.>
diff --git a/.github/dependabot.yml b/.github/dependabot.yml
new file mode 100644
index 0000000..b396869
--- /dev/null
+++ b/.github/dependabot.yml
@@ -0,0 +1,9 @@
+version: 2
+updates:
+- package-ecosystem: pip
+  directory: "/"
+  schedule:
+    interval: weekly
+  open-pull-requests-limit: 10
+  reviewers:
+  - fronzbot
diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
new file mode 100644
index 0000000..9bc50f3
--- /dev/null
+++ b/.github/workflows/lint.yml
@@ -0,0 +1,29 @@
+name: Lint
+
+on:
+  push:
+    branches: [main, dev]
+  pull_request:
+    branches: [main, dev]
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [3.9]
+
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v1
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -r requirements.txt
+        pip install -r requirements_test.txt
+    - name: Lint
+      run: |
+        tox -r -e lint
diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml
new file mode 100644
index 0000000..57427e9
--- /dev/null
+++ b/.github/workflows/publish.yml
@@ -0,0 +1,26 @@
+name: Upload Python Package
+
+on:
+  release:
+    types: [created]
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python
+      uses: actions/setup-python@v1
+      with:
+        python-version: '3.9'
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install twine build
+    - name: Build and publish
+      env:
+        TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}
+        TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
+      run: |
+        python -m build
+        twine upload dist/*
diff --git a/.gitignore b/.gitignore
index ed8ebf5..f575ec5 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,4 @@
-__pycache__
\ No newline at end of file
+__pycache__
+.tox
+python_adc_eval.egg-info
+dist
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..1a415fc
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 Kevin Fronczak
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/MANIFEST.in b/MANIFEST.in
new file mode 100644
index 0000000..8da04a0
--- /dev/null
+++ b/MANIFEST.in
@@ -0,0 +1,3 @@
+include README.rst
+include LICENSE
+include requirements.txt
diff --git a/README.md b/README.md
deleted file mode 100644
index 7173c5b..0000000
--- a/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
-# ADC evaluation tool
-
-Tiny tools collection (Python [NumPy](https://numpy.org/)+[Matplotlib](https://matplotlib.org/) based) to do spectral analysis and calculate the key performance parameters of an ADC. Just collect some data from the ADC, specify basic ADC parameters and run analysis. See [example.ipynb](example.ipynb) (you will need [Jupyter Notebook](https://jupyter.org/) to be installed).
-
-![analyser](analyser.png)
-
-References:
-- [Analog Devices MT-003 TUTORIAL "Understand SINAD, ENOB, SNR, THD, THD + N, and SFDR so You Don't Get Lost in the Noise Floor"](https://www.analog.com/media/en/training-seminars/tutorials/MT-003.pdf)
-- [National Instruments Application Note 041 "The Fundamentals of FFT-Based Signal Analysis and Measurement"](http://www.sjsu.edu/people/burford.furman/docs/me120/FFT_tutorial_NI.pdf)
-
-Inspired by Linear Technology (now Analog Devices) [PScope](https://www.analog.com/en/technical-articles/pscope-basics.html) tool.
-
-![pscope](pscope.png)
-Image source: [Creating an ADC Using FPGA Resources WP - Lattice](https://www.latticesemi.com/-/media/LatticeSemi/Documents/WhitePapers/AG/CreatingAnADCUsingFPGAResources.ashx?document_id=36525)
diff --git a/README.rst b/README.rst
new file mode 100644
index 0000000..4164dee
--- /dev/null
+++ b/README.rst
@@ -0,0 +1,65 @@
+python-adc-eval |Lint| |PyPi Version| |Codestyle|
+===================================================
+
+A python-based ADC evaluation tool, suitable for standalone or library-based usage
+
+Details
+--------
+
+Package based on
+`esynr3z/adc-eval <https://github.com/esynr3z/adc-eval>`__
+
+Tiny tools collection (Python
+`NumPy <https://numpy.org/>`__\ +\ `Matplotlib <https://matplotlib.org/>`__
+based) to do spectral analysis and calculate the key performance
+parameters of an ADC. Just collect some data from the ADC, specify basic
+ADC parameters and run analysis. See `example.ipynb <example.ipynb>`__
+(you will need `Jupyter Notebook <https://jupyter.org/>`__ to be
+installed).
+
+.. figure:: analyser.png
+   :alt: analyser
+
+   analyser
+
+References: - `Analog Devices MT-003 TUTORIAL “Understand SINAD, ENOB,
+SNR, THD, THD + N, and SFDR so You Don’t Get Lost in the Noise
+Floor” <https://www.analog.com/media/en/training-seminars/tutorials/MT-003.pdf>`__
+- `National Instruments Application Note 041 “The Fundamentals of
+FFT-Based Signal Analysis and
+Measurement” <http://www.sjsu.edu/people/burford.furman/docs/me120/FFT_tutorial_NI.pdf>`__
+
+Inspired by Linear Technology (now Analog Devices)
+`PScope <https://www.analog.com/en/technical-articles/pscope-basics.html>`__
+tool.
+
+
+USAGE
+=======
+
+To load the library in a module:
+
+.. code-block:: python
+
+    import adc_eval
+
+
+Given an array of values representing the output of an ADC, the spectrum can be analyzed with the following:
+
+.. code-block:: python
+
+    import adc_eval
+
+    adc_eval.spectrum.analyze(<adc list>, <adc_bits>, <adc vref>, <adc fsamp>, window='hanning', no_plot=<True/False>)
+
+
+|pscope| Image source: `Creating an ADC Using FPGA Resources WP -
+Lattice <https://www.latticesemi.com/-/media/LatticeSemi/Documents/WhitePapers/AG/CreatingAnADCUsingFPGAResources.ashx?document_id=36525>`__
+
+.. |pscope| image:: pscope.png
+.. |Lint| image:: https://github.com/fronzbot/python-adc-eval/workflows/Lint/badge.svg
+   :target: https://github.com/fronzbot/python-adc-eval/actions?query=workflow%3ALint
+.. |PyPi Version| image:: https://img.shields.io/pypi/v/spithon.svg
+   :target: https://pypi.org/project/python-adc-eval
+.. |Codestyle| image:: https://img.shields.io/badge/code%20style-black-000000.svg
+   :target: https://github.com/psf/black
diff --git a/adc_eval/__init__.py b/adc_eval/__init__.py
new file mode 100644
index 0000000..b4593a3
--- /dev/null
+++ b/adc_eval/__init__.py
@@ -0,0 +1 @@
+"""Initialization file for module."""
diff --git a/converters.py b/adc_eval/converters.py
similarity index 67%
rename from converters.py
rename to adc_eval/converters.py
index 0cf441b..bea7480 100644
--- a/converters.py
+++ b/adc_eval/converters.py
@@ -1,15 +1,13 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-"""
-Analog <-> Digital converters behavioral models
-"""
+"""Analog <-> Digital converters behavioral models."""
 
 import numpy as np
 
 
-def analog2digital(sig_f, sample_freq=1e6, sample_n=1024, sample_bits=8, vref=3.3, noisy_lsb=1):
-    sample_quants = 2 ** sample_bits
+def analog2digital(
+    sig_f, sample_freq=1e6, sample_n=1024, sample_bits=8, vref=3.3, noisy_lsb=1
+):
+    """Analog to digital converter."""
+    sample_quants = 2**sample_bits
     sample_prd = 1 / sample_freq
     t = np.arange(0, sample_n * sample_prd, sample_prd)
     dv = vref / sample_quants
@@ -25,6 +23,7 @@ def analog2digital(sig_f, sample_freq=1e6, sample_n=1024, sample_bits=8, vref=3.
 
 
 def digital2analog(samples, sample_bits=8, vref=3.3):
-    quants = 2 ** sample_bits
+    """Digital to analog converter."""
+    quants = 2**sample_bits
     dv = vref / quants
     return samples * dv
diff --git a/signals.py b/adc_eval/signals.py
similarity index 69%
rename from signals.py
rename to adc_eval/signals.py
index 1755f98..0aeb71c 100644
--- a/signals.py
+++ b/adc_eval/signals.py
@@ -1,16 +1,13 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-"""
-Some basic signal functions
-"""
+"""Basic signal functions."""
 
 import numpy as np
 
 
 def sin(t, peak=1.5, offset=1.65, freq=1e3, ph0=0):
+    """Generate a sine wave."""
     return offset + peak * np.sin(ph0 + 2 * np.pi * freq * t)
 
 
 def noise(t, mean=0, std=0.1):
+    """Generate random noise."""
     return np.random.normal(mean, std, size=len(t))
diff --git a/adc_eval/spectrum.py b/adc_eval/spectrum.py
new file mode 100644
index 0000000..22458ae
--- /dev/null
+++ b/adc_eval/spectrum.py
@@ -0,0 +1,334 @@
+"""Spectral analysis module.
+
+References:
+  - Analog Devices MT-003 TUTORIAL
+    "Understand SINAD, ENOB, SNR, THD, THD + N, and SFDR so You Don't Get Lost in the Noise Floor"
+  - National Instruments Application Note 041
+    "The Fundamentals of FFT-Based Signal Analysis and Measurement"
+
+"""
+
+import matplotlib
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+def db2amp(db):
+    """Decibels to amplitutde ratio."""
+    return 10 ** (0.05 * db)
+
+
+def amp2db(a):
+    """Amplitutde ratio to decibels."""
+    return 20 * np.log10(a)
+
+
+def db2pow(db):
+    """Decibels to power ratio."""
+    return 10 ** (0.1 * db)
+
+
+def pow2db(p):
+    """Power ratio to decibels."""
+    return 10 * np.log10(p)
+
+
+def enob(sinad):
+    """Calculate ENOB from SINAD."""
+    return (sinad - 1.76) / 6.02
+
+
+def snr_theor(n):
+    """Theoretical SNR of an ideal n-bit ADC in dB."""
+    return 6.02 * n + 1.76
+
+
+def noise_floor(snr, m):
+    """Noise floor of the m-point FFT in dB."""
+    return -snr - 10 * np.log10(m / 2)
+
+
+def harmonics(psp, fft_n, ref_pow, sample_freq, leak=20, n=5, window="hanning"):
+    """Obtain first n harmonics properties from power spectrum."""
+    # Coherence Gain and Noise Power Bandwidth for different windows
+    win_params = {
+        "uniform": {"cg": 1.0, "npb": 1.0},
+        "hanning": {"cg": 0.5, "npb": 1.5},
+        "hamming": {"cg": 0.54, "npb": 1.36},
+        "blackman": {"cg": 0.42, "npb": 1.73},
+    }[window]
+    fft_n = len(psp) * 2  # one side spectrum provided
+    df = sample_freq / fft_n
+    # calculate fundamental frequency
+    fund_bin = np.argmax(psp)
+    fund_freq = np.sum(
+        [psp[i] * i * df for i in range(fund_bin - leak, fund_bin + leak + 1)]
+    ) / np.sum(psp[fund_bin - leak : fund_bin + leak + 1])
+    if np.isinf:
+        fund_freq = fund_bin * df
+
+    # calculate harmonics info
+    h = []
+    for i in range(1, n + 1):
+        h_i = {"num": i}
+        zone_freq = (fund_freq * i) % sample_freq
+        h_i["freq"] = (
+            sample_freq - zone_freq if zone_freq >= (sample_freq / 2) else zone_freq
+        )
+        h_i["central_bin"] = int(h_i["freq"] / df)
+        h_i["bins"] = np.array(
+            range(h_i["central_bin"] - leak, h_i["central_bin"] + leak + 1)
+        )
+        h_i["pow"] = (
+            ((1 / win_params["cg"]) ** 2) * np.sum(psp[h_i["bins"]]) / win_params["npb"]
+        )
+        h_i["vrms"] = np.sqrt(h_i["pow"])
+        if i == 1:
+            h_i["db"] = "%.2f dBFS" % pow2db(h_i["pow"] / ref_pow)
+        else:
+            try:
+                h_i["db"] = "%.2f dBc" % pow2db(h_i["pow"] / h[0]["pow"])
+            except IndexError:
+                continue
+        h += [h_i]
+    return h
+
+
+def signal_noise(psp, harms):
+    """Obtain different signal+noise characteristics from spectrum."""
+    # noise + distortion power
+    nd_psp = np.copy(psp)
+    nd_psp[harms[0]["bins"]] = 0  # remove main harmonic
+    nd_psp[0] = 0  # remove dc
+    nd_pow = sum(nd_psp)
+    # noise power
+    n_psp = np.copy(psp)
+    for h in harms:
+        n_psp[h["bins"]] = 0  # remove all harmonics
+    n_psp[0] = 0  # remove dc
+    n_pow = sum(n_psp)
+    # distortion power
+    d_pow = np.sum([h["pow"] for h in harms]) - harms[0]["pow"]
+    # calculate results
+    sinad = pow2db(harms[0]["pow"] / nd_pow)
+    thd = pow2db(harms[0]["pow"] / d_pow)
+    snr = pow2db(harms[0]["pow"] / n_pow)
+    sfdr = pow2db(max(nd_psp) / harms[0]["pow"])
+    return sinad, thd, snr, sfdr
+
+
+def analyze(sig, adc_bits, adc_vref, adc_freq, window="hanning", no_plot=False):
+    """Do spectral analysis for ADC samples."""
+    # Calculate some useful parameters
+    sig_vpeak_max = adc_vref / 2
+    sig_vrms_max = sig_vpeak_max / np.sqrt(2)
+    sig_pow_max = sig_vrms_max**2
+    ref_pow = sig_pow_max
+    adc_prd = 1 / adc_freq
+    adc_quants = 2**adc_bits
+    dv = adc_vref / adc_quants
+    sig_n = len(sig)
+    dt = 1 / adc_freq
+    fft_n = sig_n
+    df = adc_freq / fft_n
+    win_coef = {"uniform": np.ones(sig_n), "hanning": np.hanning(sig_n)}[window]
+    sp_leak = 20  # spectru leak bins
+    h_n = 5  # harmonics number
+
+    # Convert samples to voltage
+    sig_v = sig * dv
+
+    # Remove DC and apply window
+    sig_dc = np.mean(sig_v)
+    sig_windowed = (sig_v - sig_dc) * win_coef
+
+    # Calculate one-side amplitude spectrum (Vrms)
+    asp = np.sqrt(2) * np.abs(np.fft.rfft(sig_windowed)) / sig_n
+
+    # Calculate one-side power spectrum (Vrms^2)
+    psp = np.power(asp, 2)
+    psp_db = pow2db(psp / ref_pow)
+
+    # Calculate harmonics
+    h = harmonics(
+        psp=psp,
+        fft_n=fft_n,
+        ref_pow=ref_pow,
+        sample_freq=adc_freq,
+        leak=sp_leak,
+        n=h_n,
+        window=window,
+    )
+
+    # Input signal parameters (based on 1st harmonic)
+    sig_pow = h[0]["pow"]
+    sig_vrms = h[0]["vrms"]
+    sig_vpeak = sig_vrms * np.sqrt(2)
+    sig_freq = h[0]["freq"]
+    sig_prd = 1 / sig_freq
+
+    # Calculate SINAD, THD, SNR, SFDR
+    adc_sinad, adc_thd, adc_snr, adc_sfdr = signal_noise(psp, h)
+
+    # Calculate ENOB
+    # sinad correction to normalize ENOB to full-scale regardless of input signal amplitude
+    adc_enob = enob(adc_sinad + pow2db(ref_pow / sig_pow))
+
+    # Calculate Noise Floor
+    adc_noise_floor = noise_floor(adc_snr, fft_n)
+    harm = {}
+    for index, h_i in enumerate(h):
+        harm[h_i["num"]] = [h_i["freq"], h_i["db"]]
+
+    result_data = {
+        "points": fft_n,
+        "fbin": df,
+        "window": window,
+        "harmonics": harm,
+        "fin": sig_freq,
+        "vpeak": sig_vpeak,
+        "offset": sig_dc,
+        "fsamp": adc_freq,
+        "tsamp": adc_prd,
+        "vref": adc_vref,
+        "bits": adc_bits,
+        "quants": adc_quants,
+        "quant": dv * 1e3,
+        "snr": adc_snr,
+        "sinad": adc_sinad,
+        "thd": adc_thd,
+        "enob": adc_enob,
+        "noise_floor": adc_noise_floor,
+    }
+
+    if not no_plot:
+        # Create plots
+        plt.figure(figsize=(14, 7))
+        gs = matplotlib.gridspec.GridSpec(2, 2, width_ratios=[3, 1])
+
+        # Time plot
+        ax_time = plt.subplot(gs[0, 0])
+        ax_time_xlim = min(sig_n, int(5 * sig_prd / dt))
+        ax_time.plot(np.arange(0, ax_time_xlim), sig[:ax_time_xlim], color="C0")
+        ax_time.set(ylabel="ADC code", ylim=[0, adc_quants])
+        ax_time.set(
+            yticks=list(range(0, adc_quants, adc_quants // 8)) + [adc_quants - 1]
+        )
+        ax_time.set(xlabel="Sample", xlim=[0, ax_time_xlim - 1])
+        ax_time.set(xticks=range(0, ax_time_xlim, max(1, ax_time_xlim // 20)))
+        ax_time.grid(True)
+        ax_time_xsec = ax_time.twiny()
+        ax_time_xsec.set(xticks=ax_time.get_xticks())
+        ax_time_xsec.set(xbound=ax_time.get_xbound())
+        ax_time_xsec.set_xticklabels(
+            ["%.02f" % (x * dt * 1e3) for x in ax_time.get_xticks()]
+        )
+        ax_time_xsec.set_xlabel("Time, ms")
+        ax_time_ysec = ax_time.twinx()
+        ax_time_ysec.set(yticks=ax_time.get_yticks())
+        ax_time_ysec.set(ybound=ax_time.get_ybound())
+        ax_time_ysec.set_yticklabels(["%.02f" % (x * dv) for x in ax_time.get_yticks()])
+        ax_time_ysec.set_ylabel("Voltage, V")
+
+        # Frequency plot
+        ax_freq = plt.subplot(gs[1, 0])
+        ax_freq.plot(
+            np.arange(0, len(psp_db)), psp_db, color="C0", zorder=0, label="Spectrum"
+        )
+        for h_i in h:
+            ax_freq.text(
+                h_i["central_bin"] + 2,
+                psp_db[h_i["central_bin"]],
+                str(h_i["num"]),
+                va="bottom",
+                ha="left",
+                weight="bold",
+            )
+            ax_freq.plot(h_i["bins"], psp_db[h_i["bins"]], color="C4")
+        ax_freq.plot(0, 0, color="C4", label="Harmonics")
+        ax_freq.set(ylabel="dB", ylim=[-150, 10])
+        ax_freq.set(xlabel="Sample", xlim=[0, fft_n / 2])
+        ax_freq.set(xticks=list(range(0, fft_n // 2, fft_n // 32)) + [fft_n // 2 - 1])
+        ax_freq.grid(True)
+        ax_freq.legend(loc="lower right", ncol=3)
+        ax_freq_sec = ax_freq.twiny()
+        ax_freq_sec.set_xticks(ax_freq.get_xticks())
+        ax_freq_sec.set_xbound(ax_freq.get_xbound())
+        ax_freq_sec.set_xticklabels(
+            ["%.02f" % (x * df * 1e-3) for x in ax_freq.get_xticks()]
+        )
+        ax_freq_sec.set_xlabel("Frequency, kHz")
+
+        # Information plot
+        ax_info = plt.subplot(gs[:, 1])
+        ax_info.set(xlim=[0, 10], xticks=[], ylim=[0, 10], yticks=[])
+        harmonics_str = "\n".join(
+            [
+                "%d%s @ %-10s : %s"
+                % (
+                    h_i["num"],
+                    ["st", "nd", "rd", "th", "th"][h_i["num"] - 1],
+                    "%0.3f kHz" % (h_i["freq"] * 1e-3),
+                    h_i["db"],
+                )
+                for h_i in h
+            ]
+        )
+        ax_info_str = """
+    ========= FFT ==========
+    Points           : {fft_n}
+    Freq. resolution : {fft_res:.4} Hz
+    Window           : {fft_window}
+
+    ======= Harmonics ======
+    {harmonics_str}
+
+    ===== Input signal =====
+    Frequency        : {sig_freq:.4} kHz
+    Amplitude (Vpeak): {sig_vpeak:.4} V
+    DC offset        : {sig_dc:.4} V
+
+    ========= ADC ==========
+    Sampling freq.   : {adc_freq:.4} kHz
+    Sampling period  : {adc_prd:.4} us
+    Reference volt.  : {adc_vref:.4} V
+    Bits             : {adc_bits} bits
+    Quants           : {adc_quants}
+    Quant            : {adc_quant:.4} mV
+    SNR              : {adc_snr:.4} dB
+    SINAD            : {adc_sinad:.4} dB
+    THD              : {adc_thd:.4} dB
+    ENOB             : {adc_enob:.4} bits
+    SFDR             : {adc_sfdr:.4} dBc
+    Noise floor      : {adc_nfloor:.4} dBFS
+    """.format(
+            fft_n=fft_n,
+            fft_res=df,
+            fft_window=window,
+            harmonics_str=harmonics_str,
+            sig_freq=sig_freq * 1e-3,
+            sig_vpeak=sig_vpeak,
+            sig_dc=sig_dc,
+            adc_freq=adc_freq * 1e-3,
+            adc_prd=adc_prd * 1e6,
+            adc_vref=adc_vref,
+            adc_bits=adc_bits,
+            adc_quants=adc_quants,
+            adc_quant=dv * 1e3,
+            adc_snr=adc_snr,
+            adc_thd=adc_thd,
+            adc_sinad=adc_sinad,
+            adc_enob=adc_enob,
+            adc_sfdr=adc_sfdr,
+            adc_nfloor=adc_noise_floor,
+        )
+        ax_info.text(1, 9.5, ax_info_str, va="top", ha="left", family="monospace")
+
+        # General plotting settings
+        plt.tight_layout()
+        plt.style.use("bmh")
+
+        # Show the result
+        plt.show()
+
+    return result_data
diff --git a/example.ipynb b/example.ipynb
deleted file mode 100644
index 510534e..0000000
--- a/example.ipynb
+++ /dev/null
@@ -1,102 +0,0 @@
-{
- "metadata": {
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.10"
-  },
-  "orig_nbformat": 2,
-  "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.8.10 64-bit"
-  },
-  "interpreter": {
-   "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2,
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from scipy import signal\n",
-    "import matplotlib\n",
-    "import matplotlib.pyplot as plt\n",
-    "import spectrum\n",
-    "import signals\n",
-    "import converters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": "<Figure size 1008x504 with 6 Axes>",
-      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"493.888125pt\" version=\"1.1\" viewBox=\"0 0 979.296847 493.888125\" width=\"979.296847pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <metadata>\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n   <cc:Work>\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n    <dc:date>2021-07-24T11:22:34.927500</dc:date>\n    <dc:format>image/svg+xml</dc:format>\n    <dc:creator>\n     <cc:Agent>\n      <dc:title>Matplotlib v3.3.4, https://matplotlib.org/</dc:title>\n     </cc:Agent>\n    </dc:creator>\n   </cc:Work>\n  </rdf:RDF>\n </metadata>\n <defs>\n  <style 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\nL 119.167295 401.841355 \nL 119.325616 404.141985 \nL 119.483936 411.582402 \nL 119.642257 403.809999 \nL 119.800577 406.070223 \nL 119.958898 404.741914 \nL 120.117218 401.646368 \nL 120.275539 405.26338 \nL 120.433859 404.950094 \nL 120.59218 412.623433 \nL 120.7505 409.766873 \nL 121.067141 401.969473 \nL 121.225462 401.670971 \nL 121.383782 404.362969 \nL 121.542102 415.225788 \nL 121.700423 403.946289 \nL 121.858743 402.429999 \nL 122.017064 409.979291 \nL 122.492025 398.69737 \nL 122.650346 401.218229 \nL 122.808666 407.801617 \nL 123.125307 399.034304 \nL 123.283628 406.559589 \nL 123.441948 395.882724 \nL 123.600269 395.500672 \nL 123.758589 411.513647 \nL 124.07523 397.106342 \nL 124.233551 396.562048 \nL 124.391871 418.151677 \nL 124.550192 401.936515 \nL 124.708512 407.992967 \nL 124.866833 403.885853 \nL 125.025153 421.589499 \nL 125.341794 397.366313 \nL 125.500114 398.318611 \nL 125.658435 400.288573 \nL 125.816755 398.398761 \nL 125.975076 438.43279 \nL 126.133396 400.263568 \nL 126.291717 402.232841 \nL 126.450037 402.253213 \nL 126.766678 410.103631 \nL 126.924999 401.157565 \nL 127.083319 402.262877 \nL 127.39996 397.567226 \nL 127.558281 393.141388 \nL 127.874922 406.380862 \nL 128.191563 399.314816 \nL 128.349883 405.30449 \nL 128.508204 402.017179 \nL 128.666524 408.010167 \nL 128.824845 399.471078 \nL 129.299806 414.495333 \nL 129.458126 410.115551 \nL 129.616447 418.692858 \nL 129.774767 404.453751 \nL 129.933088 403.401422 \nL 130.091408 401.517921 \nL 130.249729 405.672889 \nL 130.408049 414.162594 \nL 130.56637 400.925722 \nL 130.72469 409.424897 \nL 130.883011 401.316967 \nL 131.041331 399.549389 \nL 131.199652 407.796701 \nL 131.357972 409.254986 \nL 131.516293 402.594194 \nL 131.674613 408.459241 \nL 132.149575 399.441182 \nL 132.307895 409.645969 \nL 132.466216 404.885022 \nL 132.624536 404.034237 \nL 132.782857 401.149133 \nL 132.941177 402.203622 \nL 133.099497 400.078711 \nL 133.257818 420.377019 \nL 133.416138 395.044997 \nL 133.574459 398.106883 \nL 133.732779 412.386961 \nL 133.8911 404.737616 \nL 134.366061 420.364001 \nL 134.682702 398.617038 \nL 134.841023 396.893007 \nL 135.157664 411.477738 \nL 135.315984 404.880556 \nL 135.632625 434.230754 \nL 135.790946 407.503431 \nL 135.949266 410.57678 \nL 136.265907 404.353249 \nL 136.582548 398.151153 \nL 136.899189 407.162649 \nL 137.057509 408.889784 \nL 137.21583 407.739371 \nL 137.37415 396.742029 \nL 137.532471 399.311919 \nL 137.849112 422.369582 \nL 138.007432 406.145929 \nL 138.324073 403.849542 \nL 138.482394 406.055904 \nL 138.799035 398.820837 \nL 138.957355 424.728174 \nL 139.115676 404.2238 \nL 139.273996 409.422544 \nL 139.590637 405.700723 \nL 139.748958 404.562914 \nL 139.907278 399.664582 \nL 140.065599 398.478641 \nL 140.223919 400.064257 \nL 140.382239 405.636495 \nL 140.54056 400.127787 \nL 140.69888 404.542146 \nL 140.857201 404.204526 \nL 141.015521 397.137758 \nL 141.173842 396.849845 \nL 141.332162 396.224864 \nL 141.648803 396.349523 \nL 142.123765 409.523197 \nL 142.440406 398.408986 \nL 142.598726 406.740565 \nL 142.757047 406.963218 \nL 143.073688 400.726601 \nL 143.232008 401.854605 \nL 143.390329 400.128965 \nL 143.548649 401.126439 \nL 143.70697 417.20635 \nL 143.86529 404.069201 \nL 144.023611 403.001819 \nL 144.340251 405.622807 \nL 144.498572 397.172841 \nL 144.656892 399.516594 \nL 144.815213 433.425763 \nL 144.973533 410.126621 \nL 145.131854 410.410368 \nL 145.290174 397.483271 \nL 145.606815 410.835299 \nL 145.923456 400.138615 \nL 146.398418 410.837465 \nL 146.556738 407.30238 \nL 146.715059 397.326576 \nL 146.873379 400.0282 \nL 147.0317 414.903875 \nL 147.19002 402.154185 \nL 147.348341 406.603739 \nL 147.506661 414.888473 \nL 147.823302 398.968561 \nL 148.139943 414.056805 \nL 148.456584 402.019515 \nL 148.614904 405.691238 \nL 148.773225 405.53144 \nL 148.931545 399.439507 \nL 149.248186 408.02624 \nL 149.406507 406.877942 \nL 149.564827 401.023644 \nL 149.723148 400.71012 \nL 149.881468 398.434286 \nL 150.51475 414.440853 \nL 150.673071 400.636968 \nL 150.831391 403.023646 \nL 150.989712 408.527959 \nL 151.148032 405.310435 \nL 151.306353 399.546031 \nL 151.622993 409.510291 \nL 151.781314 406.228324 \nL 152.097955 395.862594 \nL 152.256275 404.833162 \nL 152.414596 400.478021 \nL 152.572916 404.223039 \nL 152.731237 425.445816 \nL 152.889557 411.613561 \nL 153.047878 412.722905 \nL 153.206198 408.926265 \nL 153.364519 409.837029 \nL 153.522839 403.861335 \nL 153.68116 408.835706 \nL 153.83948 402.544957 \nL 153.997801 405.11673 \nL 154.156121 411.973162 \nL 154.314442 401.348101 \nL 154.472762 399.139667 \nL 154.631083 401.024732 \nL 154.947724 395.079966 \nL 155.106044 398.588436 \nL 155.264365 413.230162 \nL 155.422685 416.368351 \nL 155.739326 399.313399 \nL 155.897646 402.456836 \nL 156.055967 400.121309 \nL 156.214287 393.417578 \nL 156.372608 392.630367 \nL 156.530928 401.09288 \nL 156.689249 421.836344 \nL 156.847569 404.027221 \nL 157.00589 404.754381 \nL 157.16421 402.284661 \nL 157.322531 397.876664 \nL 157.480851 399.361711 \nL 157.639172 411.452073 \nL 157.797492 399.333519 \nL 157.955813 397.598344 \nL 158.272454 406.961307 \nL 158.430774 404.014069 \nL 158.589095 405.861986 \nL 158.747415 417.084578 \nL 158.905736 399.504403 \nL 159.064056 399.107161 \nL 159.222376 409.063022 \nL 159.380697 405.014204 \nL 159.539017 406.022013 \nL 159.697338 418.571119 \nL 159.855658 422.033664 \nL 160.013979 409.130205 \nL 160.172299 406.666162 \nL 160.33062 399.575557 \nL 160.647261 409.683051 \nL 160.805581 402.950746 \nL 160.963902 404.998422 \nL 161.122222 398.225807 \nL 161.280543 396.653171 \nL 161.597184 405.952567 \nL 161.755504 405.58074 \nL 161.913825 433.842846 \nL 162.072145 400.396012 \nL 162.230466 398.716522 \nL 162.547107 415.55071 \nL 162.863748 397.235036 \nL 163.022068 394.396611 \nL 163.180388 398.11291 \nL 163.497029 414.520615 \nL 163.81367 404.185032 \nL 163.971991 405.040252 \nL 164.130311 409.271691 \nL 164.288632 402.036476 \nL 164.446952 407.028481 \nL 164.605273 405.556593 \nL 164.763593 408.146068 \nL 164.921914 400.370363 \nL 165.080234 398.453232 \nL 165.238555 401.984421 \nL 165.396875 399.33651 \nL 165.555196 398.787574 \nL 165.713516 405.385704 \nL 166.030157 403.115158 \nL 166.188478 411.7346 \nL 166.346798 401.676431 \nL 166.505119 402.137765 \nL 166.663439 405.960457 \nL 166.821759 400.304289 \nL 166.98008 404.080771 \nL 167.1384 404.080133 \nL 167.296721 405.956152 \nL 167.455041 413.582622 \nL 167.613362 414.483543 \nL 167.771682 425.430027 \nL 168.088323 400.014239 \nL 168.246644 396.198202 \nL 168.404964 403.046198 \nL 168.563285 403.469345 \nL 168.721605 407.986277 \nL 168.879926 404.836072 \nL 169.196567 397.004459 \nL 169.354887 398.796098 \nL 169.513208 406.36154 \nL 169.829849 397.745148 \nL 169.988169 398.307469 \nL 170.14649 408.240702 \nL 170.30481 427.497675 \nL 170.46313 408.829597 \nL 170.779771 405.044955 \nL 170.938092 406.22601 \nL 171.096412 404.606735 \nL 171.254733 399.641697 \nL 171.413053 398.493105 \nL 171.571374 399.38978 \nL 171.729694 402.938041 \nL 171.888015 411.815764 \nL 172.204656 397.390119 \nL 172.362976 396.76834 \nL 172.837938 410.528906 \nL 172.996258 408.602313 \nL 173.154579 400.606533 \nL 173.47122 409.885702 \nL 173.62954 404.159858 \nL 173.787861 404.985705 \nL 173.946181 402.852003 \nL 174.104502 406.032013 \nL 174.262822 401.317908 \nL 174.421142 402.177695 \nL 174.579463 410.518847 \nL 174.896104 403.871603 \nL 175.054424 399.150231 \nL 175.212745 399.699344 \nL 175.371065 402.135411 \nL 175.529386 410.025101 \nL 175.687706 427.125383 \nL 175.846027 406.296107 \nL 176.004347 400.187275 \nL 176.162668 403.69219 \nL 176.320988 403.306332 \nL 176.479309 396.581765 \nL 176.637629 398.662553 \nL 176.79595 408.008013 \nL 176.95427 404.431955 \nL 177.112591 406.575846 \nL 177.429232 399.153917 \nL 177.745873 411.615148 \nL 177.904193 407.536193 \nL 178.062513 398.872009 \nL 178.220834 413.03958 \nL 178.379154 412.995758 \nL 178.537475 405.878312 \nL 178.695795 405.944959 \nL 178.854116 411.068358 \nL 179.012436 402.537769 \nL 179.170757 400.086424 \nL 179.329077 402.182961 \nL 179.487398 408.456842 \nL 179.804039 403.298509 \nL 179.962359 402.23546 \nL 180.12068 395.13631 \nL 180.279 394.106689 \nL 180.437321 414.171641 \nL 180.595641 397.199954 \nL 180.753962 396.828582 \nL 180.912282 402.243438 \nL 181.070603 413.833545 \nL 181.387244 404.787084 \nL 181.545564 398.802736 \nL 181.703884 398.575855 \nL 182.020525 397.122956 \nL 182.178846 403.845307 \nL 182.337166 431.187854 \nL 182.495487 412.658468 \nL 182.653807 413.597502 \nL 182.812128 403.393441 \nL 182.970448 402.554921 \nL 183.128769 414.04728 \nL 183.287089 401.446658 \nL 183.44541 397.951386 \nL 183.60373 397.804998 \nL 183.762051 402.606718 \nL 183.920371 402.887883 \nL 184.078692 405.692982 \nL 184.237012 404.622149 \nL 184.395333 415.192835 \nL 184.711974 402.563255 \nL 184.870294 410.285701 \nL 185.028615 406.322823 \nL 185.186935 412.332151 \nL 185.503576 396.464967 \nL 185.661896 397.497621 \nL 185.820217 409.280487 \nL 185.978537 397.760597 \nL 186.295178 407.315531 \nL 186.453499 403.466134 \nL 186.611819 405.546918 \nL 186.92846 396.578084 \nL 187.086781 401.568607 \nL 187.403422 395.596306 \nL 187.561742 394.153858 \nL 187.720063 396.466185 \nL 187.878383 392.170047 \nL 188.036704 398.00406 \nL 188.195024 390.798435 \nL 188.353345 398.485643 \nL 188.511665 389.401785 \nL 188.669986 390.849006 \nL 188.828306 385.483374 \nL 188.986627 383.997122 \nL 189.778229 360.066017 \nL 190.09487 338.887139 \nL 190.25319 307.387695 \nL 190.411511 298.909689 \nL 190.569831 303.913419 \nL 190.886472 348.897514 \nL 191.361434 370.38985 \nL 192.153036 387.905504 \nL 192.469677 393.738276 \nL 192.627998 390.894834 \nL 192.786318 404.285584 \nL 193.102959 393.731381 \nL 193.261279 413.671681 \nL 193.57792 398.973972 \nL 193.736241 412.319922 \nL 193.894561 412.619351 \nL 194.052882 401.820327 \nL 194.211202 401.172097 \nL 194.369523 403.455166 \nL 194.527843 413.039111 \nL 194.686164 397.182124 \nL 194.844484 401.928425 \nL 195.002805 401.877013 \nL 195.161125 402.744645 \nL 195.319446 399.944773 \nL 195.477766 400.497542 \nL 195.636087 427.643772 \nL 195.794407 398.309022 \nL 195.952728 397.808391 \nL 196.269369 406.629278 \nL 196.427689 411.823519 \nL 196.74433 401.442961 \nL 197.060971 397.264989 \nL 197.219291 398.620762 \nL 197.377612 405.96181 \nL 197.694253 397.165896 \nL 197.852573 397.11047 \nL 198.010894 401.09876 \nL 198.169214 401.035099 \nL 198.327535 401.236425 \nL 198.485855 411.403941 \nL 198.644176 411.860361 \nL 199.119137 392.534014 \nL 199.435778 398.402191 \nL 199.594099 398.426265 \nL 199.752419 398.808069 \nL 200.227381 408.466145 \nL 200.385701 426.38817 \nL 200.702342 409.76138 \nL 201.018983 406.933838 \nL 201.177303 402.161343 \nL 201.335624 407.673952 \nL 201.493944 420.267481 \nL 201.652265 402.384059 \nL 201.810585 401.68839 \nL 201.968906 407.384782 \nL 202.127226 403.068675 \nL 202.443867 411.525658 \nL 202.602188 414.111197 \nL 202.918829 401.949637 \nL 203.077149 406.34689 \nL 203.23547 406.053874 \nL 203.39379 407.49043 \nL 203.710431 403.992068 \nL 203.868752 398.915732 \nL 204.343713 409.179911 \nL 204.502033 405.09444 \nL 204.818674 419.850157 \nL 204.976995 403.353761 \nL 205.135315 408.031312 \nL 205.293636 407.54757 \nL 205.451956 398.025446 \nL 205.610277 397.698184 \nL 205.768597 410.253114 \nL 205.926918 412.570807 \nL 206.085238 405.689427 \nL 206.243559 405.868339 \nL 206.401879 406.759657 \nL 206.5602 403.140486 \nL 206.71852 417.861177 \nL 206.876841 414.293409 \nL 207.193482 401.648696 \nL 207.351802 413.571533 \nL 207.510123 400.308178 \nL 207.668443 401.546618 \nL 207.826764 398.498418 \nL 207.985084 400.752892 \nL 208.301725 424.800735 \nL 208.460045 400.713097 \nL 208.618366 398.760789 \nL 208.776686 415.263406 \nL 208.935007 402.935012 \nL 209.093327 400.626909 \nL 209.251648 414.442057 \nL 209.409968 402.417921 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403.492761 \nL 217.959274 403.490455 \nL 218.117595 400.437942 \nL 218.275915 410.240184 \nL 218.592556 397.382242 \nL 218.750877 401.326458 \nL 218.909197 414.560944 \nL 219.225838 404.852589 \nL 219.384158 400.693191 \nL 219.542479 400.080569 \nL 219.85912 412.373754 \nL 220.01744 406.262086 \nL 220.175761 405.292366 \nL 220.492402 400.38847 \nL 220.650722 406.458093 \nL 220.809043 406.985854 \nL 220.967363 400.974416 \nL 221.125684 401.768544 \nL 221.284004 423.261271 \nL 221.442325 403.953618 \nL 221.600645 409.824004 \nL 221.758966 404.052356 \nL 221.917286 405.029314 \nL 222.075607 409.024837 \nL 222.392248 395.235446 \nL 222.550568 403.293346 \nL 222.708889 418.568234 \nL 222.867209 413.727043 \nL 223.02553 415.812984 \nL 223.18385 414.373971 \nL 223.34217 402.779257 \nL 223.500491 403.145605 \nL 223.658811 416.42827 \nL 223.975452 398.228621 \nL 224.133773 395.663217 \nL 224.292093 400.529018 \nL 224.450414 411.970087 \nL 224.608734 401.874122 \nL 224.767055 399.293985 \nL 224.925375 401.47421 \nL 225.083696 400.52142 \nL 225.242016 400.29799 \nL 225.400337 399.154206 \nL 225.558657 409.356278 \nL 225.716978 396.249741 \nL 225.875298 397.204506 \nL 226.033619 404.107724 \nL 226.191939 398.851596 \nL 226.35026 397.931148 \nL 226.50858 399.688492 \nL 226.666901 402.775909 \nL 226.825221 399.027316 \nL 226.983541 399.12863 \nL 227.141862 404.989923 \nL 227.458503 392.470487 \nL 227.616823 392.538391 \nL 227.775144 402.276665 \nL 227.933464 418.944978 \nL 228.091785 401.81383 \nL 228.250105 401.543547 \nL 228.408426 410.495504 \nL 228.566746 408.690993 \nL 228.725067 426.701793 \nL 228.883387 401.797014 \nL 229.041708 398.948024 \nL 229.200028 408.273078 \nL 229.358349 405.465763 \nL 229.516669 407.714314 \nL 229.83331 394.104793 \nL 229.991631 395.38912 \nL 230.149951 411.568203 \nL 230.308272 413.911979 \nL 230.466592 403.170546 \nL 230.624912 403.208177 \nL 230.783233 401.558344 \nL 230.941553 406.529542 \nL 231.099874 405.550903 \nL 231.258194 397.061307 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401.229923 \nL 239.174218 398.86403 \nL 239.332539 399.64756 \nL 239.490859 407.715995 \nL 239.8075 395.696532 \nL 239.965821 399.797805 \nL 240.124141 415.046655 \nL 240.282462 405.039836 \nL 240.599103 407.680943 \nL 240.757423 401.335261 \nL 240.915744 407.173206 \nL 241.074064 405.143261 \nL 241.232385 421.219016 \nL 241.390705 403.956003 \nL 241.707346 397.85272 \nL 242.023987 404.621974 \nL 242.182307 406.437613 \nL 242.498948 405.024759 \nL 242.657269 400.712436 \nL 242.815589 403.833177 \nL 243.13223 396.44716 \nL 243.290551 398.865997 \nL 243.448871 398.972683 \nL 243.607192 403.502486 \nL 243.765512 413.286205 \nL 243.923833 406.298688 \nL 244.082153 410.875142 \nL 244.240474 405.722689 \nL 244.398794 410.959659 \nL 244.715435 397.898188 \nL 244.873756 399.438002 \nL 245.190397 411.657594 \nL 245.348717 407.979251 \nL 245.507038 409.800436 \nL 245.665358 402.321101 \nL 245.823678 403.133825 \nL 245.981999 402.865628 \nL 246.29864 415.962443 \nL 246.45696 403.400203 \nL 246.615281 409.221803 \nL 246.931922 395.526679 \nL 247.248563 400.811552 \nL 247.406883 402.115411 \nL 247.565204 397.85514 \nL 247.723524 397.985386 \nL 247.881845 402.100573 \nL 248.040165 398.204938 \nL 248.198486 398.515828 \nL 248.356806 398.485165 \nL 248.515127 410.752188 \nL 248.673447 402.93079 \nL 248.831768 411.712352 \nL 248.990088 403.421501 \nL 249.148409 408.498205 \nL 249.306729 407.228769 \nL 249.465049 415.550325 \nL 249.62337 403.301693 \nL 249.78169 404.965206 \nL 250.098331 396.023991 \nL 250.256652 396.978104 \nL 250.573293 402.06551 \nL 250.731613 396.592667 \nL 250.889934 396.070994 \nL 251.048254 401.288289 \nL 251.206575 402.024301 \nL 251.364895 405.811134 \nL 251.523216 399.411271 \nL 251.681536 401.558927 \nL 251.998177 418.011854 \nL 252.156498 416.181806 \nL 252.314818 407.247691 \nL 252.473139 406.495455 \nL 252.631459 407.646119 \nL 252.78978 418.547615 \nL 252.9481 421.070075 \nL 253.106421 404.338299 \nL 253.264741 399.645316 \nL 253.423061 398.886248 \nL 253.581382 407.275245 \nL 253.739702 407.818348 \nL 253.898023 402.12268 \nL 254.056343 405.871376 \nL 254.214664 404.420021 \nL 254.372984 406.232664 \nL 254.689625 400.183861 \nL 254.847946 403.599409 \nL 255.164587 404.347823 \nL 255.322907 402.79197 \nL 255.481228 406.642154 \nL 255.639548 403.133344 \nL 255.797869 392.554526 \nL 255.956189 395.917874 \nL 256.11451 415.733598 \nL 256.27283 404.280111 \nL 256.431151 402.186729 \nL 256.589471 397.666227 \nL 256.747792 399.110138 \nL 256.906112 419.938845 \nL 257.064432 406.225884 \nL 257.381073 404.42172 \nL 257.539394 400.737719 \nL 257.697714 401.805981 \nL 257.856035 404.244493 \nL 258.014355 401.866399 \nL 258.330996 410.002538 \nL 258.647637 402.791975 \nL 258.805958 399.45358 \nL 258.964278 400.699881 \nL 259.122599 397.953981 \nL 259.280919 412.941347 \nL 259.43924 400.418385 \nL 259.755881 393.832687 \nL 259.914201 395.657055 \nL 260.072522 402.516446 \nL 260.230842 398.723243 \nL 260.389163 398.142751 \nL 260.547483 404.335264 \nL 260.705804 402.702317 \nL 260.864124 402.481441 \nL 261.022444 407.42993 \nL 261.180765 405.622776 \nL 261.339085 398.821855 \nL 261.655726 410.479688 \nL 261.814047 405.375864 \nL 261.972367 417.669337 \nL 262.130688 400.788214 \nL 262.289008 398.489458 \nL 262.447329 398.886799 \nL 262.605649 402.186917 \nL 262.76397 410.128129 \nL 262.92229 409.577803 \nL 263.080611 403.864762 \nL 263.238931 403.225795 \nL 263.555572 398.120488 \nL 263.872213 403.035416 \nL 264.188854 396.755008 \nL 264.505495 399.815743 \nL 264.822136 411.08626 \nL 265.138777 401.187436 \nL 265.297097 406.539362 \nL 265.455418 407.52651 \nL 265.613738 410.795025 \nL 265.772059 405.085921 \nL 265.930379 408.083194 \nL 266.0887 406.255192 \nL 266.24702 410.748618 \nL 266.405341 427.463802 \nL 266.563661 401.920643 \nL 266.721982 397.10993 \nL 266.880302 401.303648 \nL 267.038623 397.860492 \nL 267.196943 397.523891 \nL 267.355264 399.310027 \nL 267.513584 404.255509 \nL 267.671905 413.812658 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412.940132 \nL 274.638006 400.736047 \nL 274.954647 408.316974 \nL 275.112967 403.173245 \nL 275.271288 413.933481 \nL 275.587929 399.129051 \nL 275.746249 398.831403 \nL 275.904569 397.57905 \nL 276.06289 398.254825 \nL 276.22121 403.828915 \nL 276.379531 395.732224 \nL 276.537851 396.511692 \nL 276.696172 404.843834 \nL 277.012813 395.029718 \nL 277.171133 396.734473 \nL 277.329454 395.66182 \nL 277.487774 398.969927 \nL 277.646095 407.870115 \nL 277.804415 406.458698 \nL 277.962736 399.136892 \nL 278.121056 399.726064 \nL 278.279377 407.275083 \nL 278.437697 402.970205 \nL 278.596018 413.349825 \nL 278.754338 415.957394 \nL 278.912659 403.797041 \nL 279.070979 402.292503 \nL 279.2293 408.206253 \nL 279.38762 399.508143 \nL 279.54594 397.571709 \nL 279.704261 405.150348 \nL 279.862581 404.990645 \nL 280.020902 400.028242 \nL 280.179222 399.603799 \nL 280.337543 396.666547 \nL 280.495863 396.675017 \nL 280.654184 399.001864 \nL 280.812504 403.737063 \nL 280.970825 404.183217 \nL 281.129145 401.242432 \nL 281.445786 406.213301 \nL 281.604107 403.474966 \nL 281.762427 423.680226 \nL 281.920748 406.963105 \nL 282.079068 411.05341 \nL 282.237389 401.694495 \nL 282.395709 400.944956 \nL 282.55403 401.123266 \nL 282.71235 402.167165 \nL 282.870671 432.05794 \nL 283.028991 409.004781 \nL 283.187312 408.104514 \nL 283.345632 405.004936 \nL 283.503952 406.337866 \nL 283.662273 410.265968 \nL 283.820593 399.624567 \nL 283.978914 407.021878 \nL 284.137234 398.98542 \nL 284.453875 410.73054 \nL 284.770516 395.83946 \nL 285.087157 405.507541 \nL 285.245478 406.065199 \nL 285.403798 401.227891 \nL 285.562119 403.559479 \nL 285.720439 402.770074 \nL 285.87876 403.325967 \nL 286.03708 401.365384 \nL 286.195401 410.877867 \nL 286.353721 406.945218 \nL 286.512042 407.613685 \nL 286.670362 405.05966 \nL 286.828683 406.517956 \nL 286.987003 409.742113 \nL 287.145323 403.375827 \nL 287.303644 404.825474 \nL 287.620285 396.996357 \nL 287.778605 403.049106 \nL 287.936926 400.996517 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398.940249 \nL 311.843318 397.499805 \nL 312.001639 399.984167 \nL 312.4766 411.746741 \nL 312.634921 404.428773 \nL 312.793241 409.92734 \nL 312.951562 402.562541 \nL 313.109882 405.303041 \nL 313.584843 396.835697 \nL 313.743164 398.779876 \nL 313.901484 405.58589 \nL 314.059805 406.771383 \nL 314.534766 397.50782 \nL 314.693087 401.705428 \nL 314.851407 413.475433 \nL 315.009728 403.732642 \nL 315.168048 422.964224 \nL 315.326369 405.31857 \nL 315.484689 402.849449 \nL 315.64301 396.849929 \nL 315.959651 414.31199 \nL 316.117971 406.66522 \nL 316.276292 405.039652 \nL 316.434612 416.847986 \nL 316.751253 403.482106 \nL 316.909574 397.78092 \nL 317.067894 397.650685 \nL 317.226214 401.724123 \nL 317.384535 397.039258 \nL 317.542855 400.933835 \nL 317.701176 416.303741 \nL 317.859496 405.238468 \nL 318.017817 402.643622 \nL 318.334458 412.376558 \nL 318.651099 397.39984 \nL 318.809419 402.611765 \nL 318.96774 399.198733 \nL 319.12606 403.227872 \nL 319.284381 414.989254 \nL 319.442701 411.207083 \nL 319.759342 397.137269 \nL 320.075983 408.83383 \nL 320.234304 403.864398 \nL 320.392624 405.620804 \nL 320.550945 420.491818 \nL 320.709265 405.798601 \nL 321.025906 399.307763 \nL 321.184226 399.368861 \nL 321.342547 405.163163 \nL 321.500867 397.520924 \nL 321.659188 403.482371 \nL 321.817508 418.674527 \nL 322.134149 396.116494 \nL 322.29247 398.240543 \nL 322.45079 411.132145 \nL 322.609111 403.73713 \nL 322.767431 405.884486 \nL 322.925752 403.405294 \nL 323.084072 403.361707 \nL 323.242393 405.752615 \nL 323.400713 400.209629 \nL 323.559034 404.871416 \nL 323.717354 406.090961 \nL 323.875675 401.389396 \nL 324.350636 401.115364 \nL 324.508957 397.916302 \nL 324.667277 400.845046 \nL 324.825597 400.51106 \nL 324.983918 405.44692 \nL 325.142238 404.327527 \nL 325.300559 408.369235 \nL 325.458879 420.775989 \nL 325.6172 417.775433 \nL 325.77552 404.578717 \nL 325.933841 406.346901 \nL 326.092161 400.587846 \nL 326.250482 400.503462 \nL 326.408802 407.887672 \nL 326.883764 400.78303 \nL 327.042084 408.538273 \nL 327.200405 399.962962 \nL 327.517046 407.124136 \nL 327.675366 406.65792 \nL 327.833687 399.005368 \nL 328.150328 409.922412 \nL 328.308648 400.957676 \nL 328.466968 398.947241 \nL 328.625289 401.399201 \nL 328.783609 401.340629 \nL 328.94193 402.679117 \nL 329.10025 412.034903 \nL 329.258571 403.98835 \nL 329.416891 403.692066 \nL 329.733532 412.25788 \nL 329.891853 401.097384 \nL 330.050173 400.526577 \nL 330.208494 399.330755 \nL 330.366814 399.97568 \nL 330.525135 404.277484 \nL 330.841776 405.764716 \nL 331.000096 396.352824 \nL 331.158417 398.283001 \nL 331.316737 408.559053 \nL 331.791699 394.924162 \nL 331.950019 397.168951 \nL 332.26666 395.901288 \nL 332.42498 405.069341 \nL 332.583301 406.526534 \nL 332.741621 404.771725 \nL 332.899942 400.475047 \nL 333.216583 403.509197 \nL 333.374903 401.982182 \nL 333.533224 402.530313 \nL 333.691544 408.030671 \nL 333.849865 402.041107 \nL 334.166506 404.020844 \nL 334.324826 407.738627 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412.033864 \nL 341.924209 399.944038 \nL 342.08253 406.716065 \nL 342.24085 401.600593 \nL 342.399171 401.417153 \nL 342.557491 401.722692 \nL 342.715812 402.568669 \nL 342.874132 404.23598 \nL 343.032453 404.221409 \nL 343.190773 401.476757 \nL 343.349094 405.058412 \nL 343.507414 397.494134 \nL 343.665734 399.412476 \nL 343.824055 399.155907 \nL 344.140696 399.385981 \nL 344.299016 397.784167 \nL 344.773978 411.04188 \nL 345.090619 396.166886 \nL 345.248939 397.612099 \nL 345.40726 402.156451 \nL 345.56558 411.192024 \nL 345.723901 413.353955 \nL 346.040542 402.704803 \nL 346.357183 396.212738 \nL 346.673824 402.689883 \nL 346.832144 396.060256 \nL 346.990465 396.160142 \nL 347.307105 410.864089 \nL 347.623746 395.270173 \nL 347.782067 396.714098 \nL 347.940387 401.648499 \nL 348.098708 411.861202 \nL 348.415349 398.996588 \nL 348.573669 400.339856 \nL 348.89031 400.969845 \nL 349.206951 398.304507 \nL 349.365272 401.657477 \nL 349.523592 399.436423 \nL 349.681913 399.45487 \nL 349.840233 398.042967 \nL 349.998554 404.632952 \nL 350.156874 404.269373 \nL 350.315195 404.34912 \nL 350.473515 404.038468 \nL 350.790156 414.059762 \nL 350.948477 403.95464 \nL 351.423438 397.71084 \nL 351.581758 398.009444 \nL 351.740079 402.395104 \nL 351.898399 403.316289 \nL 352.373361 398.62033 \nL 352.531681 404.48639 \nL 352.690002 405.473232 \nL 353.006643 405.019527 \nL 353.164963 415.963786 \nL 353.323284 411.924515 \nL 353.481604 417.923473 \nL 353.798245 401.91479 \nL 353.956566 401.96175 \nL 354.114886 408.390527 \nL 354.273207 402.69456 \nL 354.589848 409.333816 \nL 354.748168 411.371279 \nL 354.906488 407.150583 \nL 355.064809 397.518378 \nL 355.53977 406.628126 \nL 355.698091 405.142189 \nL 355.856411 409.164719 \nL 356.014732 401.533649 \nL 356.331373 407.157087 \nL 356.489693 406.084606 \nL 356.648014 407.166487 \nL 356.806334 418.435705 \nL 356.964655 402.299077 \nL 357.122975 401.303224 \nL 357.281296 415.348828 \nL 357.439616 406.713222 \nL 357.597937 407.142803 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412.720093 \nL 365.038999 398.838104 \nL 365.19732 398.642415 \nL 365.35564 403.82313 \nL 365.513961 398.453767 \nL 365.672281 397.534478 \nL 365.830602 404.640779 \nL 365.988922 401.917208 \nL 366.147242 396.240143 \nL 366.463883 403.193296 \nL 366.622204 403.068992 \nL 366.780524 406.93765 \nL 366.938845 415.46066 \nL 367.097165 413.022762 \nL 367.255486 403.84099 \nL 367.413806 408.325689 \nL 367.730447 394.982609 \nL 368.047088 398.02051 \nL 368.363729 406.523765 \nL 368.52205 400.497558 \nL 368.68037 410.006603 \nL 368.838691 401.104052 \nL 368.997011 404.986902 \nL 369.155332 414.494836 \nL 369.313652 396.03205 \nL 369.471973 395.116556 \nL 369.630293 397.424262 \nL 369.788614 395.526137 \nL 369.946934 400.24723 \nL 370.105254 412.693335 \nL 370.263575 410.875688 \nL 370.421895 403.256989 \nL 370.580216 403.694031 \nL 370.738536 402.326422 \nL 370.896857 408.044465 \nL 371.055177 406.76511 \nL 371.213498 399.931213 \nL 371.371818 402.383592 \nL 371.688459 416.756339 \nL 372.0051 399.127828 \nL 372.163421 403.033847 \nL 372.321741 401.278062 \nL 372.480062 393.422587 \nL 372.955023 401.143678 \nL 373.113344 401.489769 \nL 373.271664 404.778255 \nL 373.429985 400.099682 \nL 373.588305 406.050509 \nL 373.746625 407.210985 \nL 373.904946 404.704712 \nL 374.063266 418.263785 \nL 374.221587 402.831811 \nL 374.379907 402.765244 \nL 374.538228 408.317407 \nL 374.696548 397.707456 \nL 374.854869 397.943862 \nL 375.013189 403.3764 \nL 375.32983 397.982299 \nL 375.488151 399.946177 \nL 375.646471 406.153095 \nL 375.804792 396.933716 \nL 375.963112 399.015606 \nL 376.121433 394.904786 \nL 376.438074 401.587103 \nL 376.596394 398.772085 \nL 376.754715 411.108318 \nL 376.913035 406.630572 \nL 377.071356 406.909683 \nL 377.229676 420.051386 \nL 377.387996 402.991898 \nL 378.021278 394.735811 \nL 378.179599 403.696899 \nL 378.337919 404.378572 \nL 378.65456 398.98084 \nL 378.812881 399.766948 \nL 378.971201 408.578704 \nL 379.129522 406.986845 \nL 379.287842 412.551757 \nL 379.446163 399.40443 \nL 379.604483 397.194534 \nL 379.762804 402.976622 \nL 379.921124 404.247331 \nL 380.079445 400.098389 \nL 380.237765 399.976577 \nL 380.396086 406.890281 \nL 380.554406 396.390006 \nL 380.712727 396.400205 \nL 380.871047 399.645144 \nL 381.029368 412.584831 \nL 381.187688 400.327177 \nL 381.346008 414.598835 \nL 381.504329 399.633227 \nL 381.662649 419.204727 \nL 381.82097 402.585879 \nL 381.97929 405.043023 \nL 382.137611 403.327176 \nL 382.295931 403.535673 \nL 382.612572 396.112503 \nL 382.770893 394.368014 \nL 383.087534 403.768288 \nL 383.245854 402.111603 \nL 383.404175 410.901156 \nL 383.562495 403.478708 \nL 383.720816 404.335374 \nL 383.879136 422.756019 \nL 384.037457 410.491134 \nL 384.195777 414.344295 \nL 384.512418 401.708286 \nL 384.829059 393.001094 \nL 385.1457 400.300973 \nL 385.462341 395.530097 \nL 385.778982 405.768259 \nL 386.095623 399.930662 \nL 386.253943 404.341394 \nL 386.412264 398.446921 \nL 386.570584 405.217607 \nL 386.728905 405.365462 \nL 386.887225 394.681963 \nL 387.045546 394.587949 \nL 387.203866 409.687575 \nL 387.362187 401.250132 \nL 387.520507 403.150081 \nL 387.678828 407.196505 \nL 387.837148 400.967941 \nL 387.995469 415.310296 \nL 388.31211 405.473272 \nL 388.47043 401.01918 \nL 388.62875 413.065088 \nL 388.787071 406.212249 \nL 388.945391 419.121946 \nL 389.103712 405.130019 \nL 389.262032 403.804537 \nL 389.420353 406.354067 \nL 389.736994 396.829192 \nL 389.895314 396.212402 \nL 390.053635 398.52988 \nL 390.370276 407.374072 \nL 390.528596 406.745599 \nL 390.686917 426.087772 \nL 390.845237 401.302272 \nL 391.161878 396.062869 \nL 391.320199 397.972434 \nL 391.478519 412.590902 \nL 391.79516 409.292554 \nL 392.111801 396.376729 \nL 392.270122 398.122406 \nL 392.586762 403.592618 \nL 392.745083 413.938891 \nL 393.061724 404.705219 \nL 393.220044 408.626387 \nL 393.536685 405.006542 \nL 393.695006 405.571731 \nL 393.853326 400.316198 \nL 394.169967 403.762706 \nL 394.328288 408.004282 \nL 394.486608 406.004714 \nL 394.644929 412.575248 \nL 394.803249 411.785738 \nL 395.11989 397.67739 \nL 395.278211 397.229204 \nL 395.594852 400.566643 \nL 395.753172 402.302863 \nL 395.911493 397.080857 \nL 396.069813 403.978127 \nL 396.386454 431.210968 \nL 396.703095 395.636286 \nL 396.861415 393.352502 \nL 397.178056 398.068049 \nL 397.494697 394.824521 \nL 397.653018 401.026362 \nL 397.811338 396.156973 \nL 398.127979 404.27933 \nL 398.2863 407.217824 \nL 398.44462 403.340717 \nL 398.602941 395.948011 \nL 398.761261 395.63453 \nL 398.919582 395.864494 \nL 399.236223 404.158272 \nL 399.394543 402.839256 \nL 399.552864 405.124952 \nL 399.711184 402.547205 \nL 399.869505 398.00884 \nL 400.027825 396.999905 \nL 400.186145 399.228165 \nL 400.344466 404.309434 \nL 400.502786 398.660309 \nL 400.819427 400.290038 \nL 401.136068 395.554896 \nL 401.294389 407.117301 \nL 401.452709 410.100754 \nL 401.61103 409.231358 \nL 401.927671 397.51526 \nL 402.085991 402.821223 \nL 402.244312 403.187444 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402.982972 \nL 410.002015 412.367769 \nL 410.318656 401.211792 \nL 410.635297 396.700209 \nL 410.793618 402.971147 \nL 410.951938 399.718665 \nL 411.110259 400.125092 \nL 411.268579 408.622213 \nL 411.426899 399.635959 \nL 411.58522 409.688959 \nL 411.74354 403.968203 \nL 411.901861 402.348019 \nL 412.060181 413.164704 \nL 412.218502 396.48277 \nL 412.376822 398.396727 \nL 412.535143 404.866939 \nL 412.693463 406.309715 \nL 412.851784 396.769082 \nL 413.010104 398.651655 \nL 413.168425 405.519216 \nL 413.326745 403.36389 \nL 413.643386 424.015525 \nL 413.801707 401.362911 \nL 413.960027 400.180936 \nL 414.118348 405.547241 \nL 414.276668 426.351648 \nL 414.593309 403.896612 \nL 414.75163 409.937048 \nL 414.90995 411.039062 \nL 415.06827 409.219329 \nL 415.226591 414.914986 \nL 415.384911 398.187125 \nL 415.543232 397.806407 \nL 415.701552 399.984302 \nL 415.859873 400.21992 \nL 416.018193 412.940693 \nL 416.176514 403.201089 \nL 416.334834 409.267412 \nL 416.493155 409.535802 \nL 416.651475 402.65096 \nL 416.809796 403.730573 \nL 416.968116 413.87615 \nL 417.126437 405.257643 \nL 417.284757 404.532713 \nL 417.443078 404.370221 \nL 417.601398 402.272282 \nL 417.759719 402.036194 \nL 417.918039 409.448951 \nL 418.23468 394.195211 \nL 418.393001 392.965623 \nL 419.501244 407.187878 \nL 419.659564 400.245425 \nL 419.817885 413.243216 \nL 419.976205 399.537212 \nL 420.134526 403.035741 \nL 420.292846 399.159479 \nL 420.451167 399.731156 \nL 420.609487 411.727256 \nL 420.767808 407.274713 \nL 420.926128 416.700187 \nL 421.242769 401.291281 \nL 421.40109 404.230539 \nL 421.55941 416.224073 \nL 421.876051 398.800778 \nL 422.034372 401.318565 \nL 422.192692 395.015562 \nL 422.351013 398.630868 \nL 422.509333 398.188074 \nL 422.667653 395.614344 \nL 422.825974 407.955467 \nL 422.984294 407.433937 \nL 423.142615 405.139652 \nL 423.300935 411.190941 \nL 423.459256 396.075465 \nL 423.617576 394.038392 \nL 423.934217 401.965035 \nL 424.092538 400.318236 \nL 424.409179 411.361836 \nL 424.72582 395.318704 \nL 425.200781 402.475117 \nL 425.359102 402.137911 \nL 425.675743 405.947091 \nL 425.834063 396.724774 \nL 425.992384 398.874742 \nL 426.309024 421.173855 \nL 426.467345 399.742051 \nL 426.942306 408.720738 \nL 427.100627 419.888656 \nL 427.575588 400.175946 \nL 427.892229 416.109887 \nL 428.05055 413.139407 \nL 428.20887 418.028005 \nL 428.683832 393.99777 \nL 429.000473 401.387042 \nL 429.158793 397.369687 \nL 429.317114 396.992812 \nL 429.475434 403.146268 \nL 429.633755 402.355864 \nL 429.950396 414.043631 \nL 430.108716 411.296725 \nL 430.267036 396.162053 \nL 430.425357 396.5604 \nL 430.583677 409.244066 \nL 430.900318 414.091117 \nL 431.058639 401.528121 \nL 431.216959 397.904625 \nL 431.37528 403.022981 \nL 431.5336 417.225177 \nL 431.850241 406.040456 \nL 432.008562 404.804654 \nL 432.166882 402.683275 \nL 432.325203 404.494057 \nL 432.483523 404.19057 \nL 432.800164 419.544897 \nL 432.958485 401.541734 \nL 433.116805 397.701323 \nL 433.275126 400.85282 \nL 433.433446 400.087767 \nL 433.591767 410.420222 \nL 433.750087 400.515581 \nL 434.066728 397.326392 \nL 434.225048 396.719353 \nL 434.85833 408.539043 \nL 435.016651 404.197823 \nL 435.174971 407.928515 \nL 435.333292 397.243462 \nL 435.491612 394.082239 \nL 435.649933 400.328008 \nL 435.808253 412.738573 \nL 436.283215 401.75061 \nL 436.441535 403.788013 \nL 436.599856 407.487077 \nL 436.916497 395.071028 \nL 437.074817 398.134372 \nL 437.233138 404.865284 \nL 437.391458 400.092591 \nL 437.708099 396.395301 \nL 437.866419 396.929158 \nL 438.18306 409.556292 \nL 438.341381 407.014385 \nL 438.499701 407.6466 \nL 438.658022 399.009691 \nL 438.816342 400.928773 \nL 438.974663 430.844047 \nL 439.132983 402.548711 \nL 439.291304 398.722182 \nL 439.449624 401.728457 \nL 439.607945 411.998435 \nL 439.924586 406.298679 \nL 440.082906 397.512038 \nL 440.241227 396.963493 \nL 440.399547 395.481249 \nL 440.557868 398.494299 \nL 440.716188 404.684913 \nL 440.874509 399.601169 \nL 441.032829 400.60346 \nL 441.19115 403.442158 \nL 441.34947 401.055728 \nL 441.50779 405.184014 \nL 441.666111 414.56934 \nL 441.982752 403.793796 \nL 442.141072 405.877856 \nL 442.457713 398.293671 \nL 442.616034 400.484442 \nL 442.774354 409.62837 \nL 442.932675 400.604061 \nL 443.090995 407.890358 \nL 443.249316 403.057457 \nL 443.407636 410.181604 \nL 443.565957 396.348275 \nL 443.882598 403.197251 \nL 444.040918 396.693359 \nL 444.199239 395.364971 \nL 444.357559 399.125415 \nL 444.51588 398.198085 \nL 444.6742 400.351259 \nL 444.990841 390.737838 \nL 445.149161 396.81677 \nL 445.307482 407.419623 \nL 445.465802 410.174524 \nL 445.782443 394.38213 \nL 445.940764 395.051891 \nL 446.257405 407.620545 \nL 446.415725 407.257458 \nL 446.732366 400.51058 \nL 446.890687 405.112776 \nL 447.207328 397.309276 \nL 447.365648 404.594524 \nL 447.523969 393.102007 \nL 447.682289 392.08277 \nL 447.84061 407.525331 \nL 447.99893 400.373386 \nL 448.157251 403.951674 \nL 448.790533 395.538432 \nL 449.107173 405.315479 \nL 449.265494 403.830457 \nL 449.423814 408.484644 \nL 449.582135 409.279116 \nL 449.898776 393.4645 \nL 450.373737 406.044529 \nL 450.532058 408.899102 \nL 450.690378 417.214846 \nL 450.848699 416.004004 \nL 451.007019 403.208629 \nL 451.16534 402.293099 \nL 451.32366 410.784158 \nL 451.640301 397.82364 \nL 451.956942 404.169752 \nL 452.115263 401.134587 \nL 452.273583 407.366968 \nL 452.431904 407.039211 \nL 452.590224 410.004957 \nL 452.748544 401.117812 \nL 452.906865 402.158489 \nL 453.065185 398.356662 \nL 453.223506 397.638562 \nL 453.381826 400.101379 \nL 453.540147 397.743081 \nL 453.698467 398.157239 \nL 453.856788 401.203035 \nL 454.015108 400.568748 \nL 454.173429 406.904582 \nL 454.331749 401.714741 \nL 454.49007 401.219458 \nL 454.64839 401.106778 \nL 454.806711 400.15044 \nL 454.965031 412.308545 \nL 455.439993 396.850319 \nL 455.756634 395.965128 \nL 455.914954 399.223197 \nL 456.073275 407.087026 \nL 456.231595 400.37493 \nL 456.389915 401.514104 \nL 456.548236 409.646486 \nL 456.864877 396.348925 \nL 457.023197 402.9308 \nL 457.181518 426.144543 \nL 457.339838 404.296417 \nL 457.498159 402.058091 \nL 457.8148 403.439791 \nL 457.97312 399.728021 \nL 458.131441 409.091621 \nL 458.289761 406.362969 \nL 458.448082 408.574935 \nL 458.606402 418.529797 \nL 458.764723 406.925379 \nL 458.923043 403.496686 \nL 459.081364 412.386518 \nL 459.239684 403.134202 \nL 459.398005 412.746308 \nL 459.556325 402.535464 \nL 459.714646 418.287422 \nL 460.031287 406.246315 \nL 460.189607 407.420341 \nL 460.347927 399.417542 \nL 460.506248 397.11005 \nL 460.664568 401.310631 \nL 460.822889 397.702533 \nL 461.13953 405.520899 \nL 461.456171 400.136795 \nL 461.614491 408.318505 \nL 461.772812 396.767588 \nL 461.931132 400.404649 \nL 462.089453 407.721311 \nL 462.247773 409.580097 \nL 462.406094 401.523866 \nL 462.564414 401.534108 \nL 462.722735 411.237305 \nL 462.881055 407.15116 \nL 463.197696 397.340165 \nL 463.356017 398.408438 \nL 463.514337 394.819038 \nL 463.672658 400.385011 \nL 463.830978 395.899858 \nL 463.989298 395.028908 \nL 464.147619 411.066493 \nL 464.305939 403.912049 \nL 464.46426 405.703462 \nL 464.62258 402.879962 \nL 464.780901 402.605333 \nL 465.097542 409.305484 \nL 465.255862 394.884884 \nL 465.414183 392.954479 \nL 465.572503 403.632177 \nL 465.730824 401.486219 \nL 465.889144 403.682548 \nL 466.205785 420.556773 \nL 466.364106 413.906968 \nL 466.522426 412.647748 \nL 466.680747 408.277048 \nL 466.839067 400.177455 \nL 466.997388 399.442792 \nL 467.155708 397.426018 \nL 467.314029 398.428004 \nL 467.472349 422.483007 \nL 467.63067 403.499579 \nL 467.78899 404.674808 \nL 467.94731 398.923568 \nL 468.105631 400.111363 \nL 468.263951 406.011738 \nL 468.422272 398.569886 \nL 468.897233 407.592565 \nL 469.055554 399.984335 \nL 469.213874 400.714548 \nL 469.530515 407.666818 \nL 469.688836 401.726702 \nL 469.847156 404.574672 \nL 470.005477 409.427308 \nL 470.163797 400.460503 \nL 470.322118 407.847778 \nL 470.797079 396.183107 \nL 471.430361 402.824027 \nL 471.588681 401.776819 \nL 471.747002 405.544443 \nL 471.905322 400.358718 \nL 472.063643 406.123492 \nL 472.380284 399.252306 \nL 472.538604 399.613375 \nL 473.013566 409.925354 \nL 473.171886 408.509158 \nL 473.330207 410.351252 \nL 473.488527 408.018728 \nL 473.805168 398.126845 \nL 473.963489 397.976152 \nL 474.121809 403.259663 \nL 474.28013 399.2901 \nL 474.596771 401.11067 \nL 474.755091 397.821451 \nL 474.913412 426.769817 \nL 475.071732 401.181092 \nL 475.230052 404.295722 \nL 475.388373 412.34147 \nL 475.546693 403.339673 \nL 475.705014 400.874887 \nL 475.863334 403.284905 \nL 476.021655 403.711665 \nL 476.179975 395.837869 \nL 476.338296 397.062139 \nL 476.654937 407.346568 \nL 476.813257 401.461407 \nL 476.971578 400.388382 \nL 477.129898 407.931871 \nL 477.446539 397.369571 \nL 477.60486 398.238738 \nL 477.76318 405.036404 \nL 477.921501 393.748041 \nL 478.079821 394.609556 \nL 478.396462 402.961985 \nL 478.554783 398.774169 \nL 478.713103 403.764654 \nL 479.029744 396.688524 \nL 479.188064 398.839109 \nL 479.346385 399.271431 \nL 479.504705 401.87881 \nL 479.663026 410.116976 \nL 479.979667 391.767332 \nL 480.137987 396.055092 \nL 480.454628 410.633573 \nL 480.612949 405.340732 \nL 480.771269 404.514271 \nL 480.92959 396.991934 \nL 481.08791 397.653699 \nL 481.246231 401.957125 \nL 481.404551 416.874135 \nL 481.721192 400.792763 \nL 481.879513 408.284058 \nL 482.037833 395.300097 \nL 482.196154 398.547942 \nL 482.354474 405.688519 \nL 482.512795 398.594522 \nL 482.671115 401.818218 \nL 482.829435 419.803207 \nL 482.987756 403.102055 \nL 483.146076 401.296498 \nL 483.304397 408.856005 \nL 483.462717 397.349565 \nL 483.621038 408.22506 \nL 483.779358 397.353782 \nL 483.937679 398.588196 \nL 484.095999 401.869223 \nL 484.25432 399.345312 \nL 484.41264 403.260348 \nL 484.570961 402.40671 \nL 484.729281 399.810913 \nL 484.887602 400.176284 \nL 485.045922 399.183466 \nL 485.204243 399.012702 \nL 485.362563 399.315563 \nL 485.520884 397.642341 \nL 485.679204 400.570523 \nL 485.837525 409.127948 \nL 485.995845 409.419623 \nL 486.154166 398.812558 \nL 486.312486 412.962942 \nL 486.470806 403.931765 \nL 486.629127 416.599807 \nL 486.945768 403.44815 \nL 487.104088 406.41129 \nL 487.262409 395.668088 \nL 487.420729 399.340094 \nL 487.57905 414.865691 \nL 487.73737 419.282098 \nL 487.895691 414.738527 \nL 488.212332 395.225743 \nL 488.528973 404.699782 \nL 488.687293 402.549964 \nL 489.003934 408.865063 \nL 489.162255 409.503671 \nL 489.320575 398.352985 \nL 489.478896 399.048193 \nL 489.795537 410.20054 \nL 490.112178 404.618123 \nL 490.270498 407.807361 \nL 490.428818 401.539362 \nL 490.587139 400.290445 \nL 490.745459 403.886093 \nL 490.90378 416.602821 \nL 491.220421 398.524397 \nL 491.378741 401.549646 \nL 491.537062 412.466388 \nL 491.695382 403.856942 \nL 492.012023 408.026706 \nL 492.170344 407.35505 \nL 492.328664 403.175522 \nL 492.486985 402.684358 \nL 492.645305 410.985956 \nL 492.803626 408.477549 \nL 493.120267 399.218379 \nL 493.278587 398.004856 \nL 493.595228 403.356836 \nL 493.753549 402.4523 \nL 493.911869 412.701424 \nL 494.070189 398.805381 \nL 494.22851 399.893416 \nL 494.38683 411.252009 \nL 494.545151 410.513909 \nL 494.703471 400.23578 \nL 494.861792 401.815363 \nL 495.020112 413.309159 \nL 495.336753 401.17972 \nL 495.495074 421.649419 \nL 495.653394 403.285973 \nL 495.811715 404.002364 \nL 495.970035 396.864075 \nL 496.128356 397.257963 \nL 496.286676 400.355813 \nL 496.444997 408.762366 \nL 496.603317 406.775425 \nL 496.761638 403.300303 \nL 496.919958 409.525596 \nL 497.078279 410.603773 \nL 497.236599 406.103408 \nL 497.39492 416.958996 \nL 497.55324 407.591772 \nL 497.711561 408.901773 \nL 497.869881 406.609383 \nL 498.028201 411.423681 \nL 498.186522 406.735221 \nL 498.344842 415.693667 \nL 498.503163 401.322178 \nL 498.661483 398.981145 \nL 498.819804 410.964131 \nL 499.136445 404.618268 \nL 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406.942413 \nL 550.43228 406.270044 \nL 550.5906 398.701932 \nL 550.907241 409.530605 \nL 551.065562 398.634208 \nL 551.223882 397.139157 \nL 551.382203 396.748022 \nL 551.540523 401.795544 \nL 551.698844 402.421522 \nL 551.857164 398.041414 \nL 552.015485 398.930817 \nL 552.332126 408.287447 \nL 552.490446 403.474382 \nL 552.807087 415.579751 \nL 553.282049 405.452481 \nL 553.440369 397.075626 \nL 553.59869 399.724493 \nL 553.75701 406.35455 \nL 553.915331 407.535759 \nL 554.231971 399.812077 \nL 554.390292 435.000509 \nL 554.548612 398.801093 \nL 554.706933 399.647168 \nL 554.865253 405.628336 \nL 555.023574 400.106907 \nL 555.181894 402.693871 \nL 555.340215 402.194063 \nL 555.498535 404.44123 \nL 555.815176 395.327643 \nL 556.131817 416.269044 \nL 556.448458 404.094239 \nL 556.606779 422.967096 \nL 556.765099 397.581686 \nL 556.92342 396.548598 \nL 557.08174 399.393677 \nL 557.240061 397.098463 \nL 557.398381 401.550314 \nL 557.556702 416.099616 \nL 557.873343 395.66007 \nL 558.031663 399.254426 \nL 558.189983 422.88431 \nL 558.348304 404.478646 \nL 558.506624 403.119462 \nL 558.664945 404.745593 \nL 558.823265 398.522531 \nL 558.981586 401.129678 \nL 559.139906 409.690522 \nL 559.298227 397.986191 \nL 559.456547 396.478272 \nL 559.614868 403.258164 \nL 559.773188 429.329778 \nL 559.931509 406.618104 \nL 560.089829 404.665765 \nL 560.40647 409.93967 \nL 560.564791 404.03627 \nL 560.723111 412.387431 \nL 561.039752 399.483152 \nL 561.198073 407.142458 \nL 561.514714 405.048799 \nL 561.673034 415.775599 \nL 561.831354 402.38043 \nL 561.989675 402.400073 \nL 562.147995 410.852994 \nL 562.306316 402.718965 \nL 562.464636 406.47747 \nL 562.622957 405.079076 \nL 562.781277 400.102986 \nL 563.256239 415.122715 \nL 563.414559 402.477254 \nL 563.57288 403.946837 \nL 563.7312 409.468453 \nL 563.889521 401.48459 \nL 564.047841 399.205137 \nL 564.206162 403.160954 \nL 564.364482 403.26138 \nL 564.522803 400.807648 \nL 564.681123 402.940608 \nL 564.839444 410.019822 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410.490461 \nL 572.280506 410.347637 \nL 572.597147 399.59902 \nL 572.913788 404.885052 \nL 573.230429 408.430106 \nL 573.388749 397.726043 \nL 573.54707 395.99416 \nL 573.863711 404.998258 \nL 574.022031 405.223937 \nL 574.180352 408.998188 \nL 574.338672 403.046478 \nL 574.496993 408.840389 \nL 574.655313 410.504327 \nL 575.130275 398.618953 \nL 575.288595 404.272186 \nL 575.446916 403.196032 \nL 575.605236 398.50096 \nL 575.921877 412.502488 \nL 576.080198 397.945732 \nL 576.71348 411.952375 \nL 577.03012 400.204184 \nL 577.188441 403.413718 \nL 577.346761 400.151825 \nL 577.663402 402.517388 \nL 577.821723 397.900864 \nL 578.296684 406.756618 \nL 578.455005 406.303401 \nL 578.771646 403.100519 \nL 578.929966 409.076662 \nL 579.088287 401.93381 \nL 579.246607 405.751896 \nL 579.404928 399.840464 \nL 579.563248 400.192497 \nL 579.721569 404.958795 \nL 580.03821 403.472392 \nL 580.19653 408.768183 \nL 580.513171 401.358383 \nL 580.829812 413.968724 \nL 580.988132 414.126298 \nL 581.146453 402.516982 \nL 581.304773 399.853982 \nL 581.463094 402.617445 \nL 581.621414 413.039821 \nL 581.779735 399.09495 \nL 581.938055 398.173679 \nL 582.096376 399.107628 \nL 582.254696 398.578863 \nL 582.413017 406.30735 \nL 582.571337 404.136736 \nL 582.729658 394.555831 \nL 582.887978 398.205542 \nL 583.046299 408.297588 \nL 583.679581 393.537421 \nL 583.837901 396.568536 \nL 583.996222 401.813044 \nL 584.154542 398.51972 \nL 584.471183 410.778799 \nL 584.629503 401.141683 \nL 584.946144 404.044243 \nL 585.104465 418.128848 \nL 585.262785 399.41803 \nL 585.421106 399.217048 \nL 585.579426 396.675318 \nL 585.737747 397.939248 \nL 585.896067 404.495621 \nL 586.054388 405.196056 \nL 586.212708 397.284372 \nL 586.371029 399.114503 \nL 586.529349 398.594487 \nL 586.68767 405.850013 \nL 586.84599 405.432565 \nL 587.004311 397.447933 \nL 587.162631 395.730081 \nL 587.320952 403.629234 \nL 587.479272 399.833014 \nL 587.637593 400.028199 \nL 587.795913 407.712912 \nL 588.112554 393.859673 \nL 588.587515 402.606406 \nL 588.745836 401.642271 \nL 588.904156 404.950792 \nL 589.220797 398.231235 \nL 589.379118 401.325809 \nL 589.537438 410.629634 \nL 589.695759 397.097469 \nL 590.0124 408.778084 \nL 590.17072 398.712104 \nL 590.487361 393.87882 \nL 590.645682 398.08703 \nL 591.120643 430.028331 \nL 591.278964 407.423319 \nL 591.437284 405.070386 \nL 591.595605 397.5493 \nL 591.753925 401.397054 \nL 591.912245 410.249166 \nL 592.070566 399.407174 \nL 592.228886 401.076952 \nL 592.387207 396.738993 \nL 592.545527 407.912215 \nL 592.703848 402.372606 \nL 592.862168 408.549308 \nL 593.020489 408.42187 \nL 593.178809 409.879533 \nL 593.33713 414.423519 \nL 593.49545 409.565177 \nL 593.653771 397.089928 \nL 593.812091 396.579651 \nL 594.128732 408.953203 \nL 594.287053 400.296802 \nL 594.445373 402.248037 \nL 594.920335 396.73047 \nL 595.078655 412.473557 \nL 595.236976 403.134046 \nL 595.395296 412.428427 \nL 595.553616 402.838802 \nL 595.711937 403.38299 \nL 595.870257 399.443081 \nL 596.028578 404.666156 \nL 596.186898 421.227481 \nL 596.345219 400.073204 \nL 596.503539 397.049967 \nL 596.82018 407.612859 \nL 596.978501 402.17035 \nL 597.136821 401.950238 \nL 597.295142 404.418755 \nL 597.453462 409.903528 \nL 597.611783 410.76283 \nL 597.770103 404.093409 \nL 597.928424 411.659238 \nL 598.245065 401.121834 \nL 598.403385 406.238056 \nL 598.561706 402.864067 \nL 598.720026 404.141559 \nL 599.036667 392.877024 \nL 599.511628 409.580766 \nL 599.669949 406.604109 \nL 599.98659 412.166835 \nL 600.14491 410.267636 \nL 600.303231 401.429946 \nL 600.461551 402.154135 \nL 600.619872 403.911989 \nL 600.778192 417.287898 \nL 601.094833 403.742038 \nL 601.253154 402.030956 \nL 601.411474 405.842146 \nL 601.569795 396.879738 \nL 601.728115 411.925326 \nL 601.886436 410.598793 \nL 602.044756 402.802142 \nL 602.361397 409.841677 \nL 602.519718 412.9167 \nL 602.836359 402.140903 \nL 602.994679 402.092418 \nL 603.152999 411.633407 \nL 603.46964 397.460932 \nL 603.627961 405.088559 \nL 603.786281 396.918659 \nL 603.944602 398.552394 \nL 604.102922 423.995463 \nL 604.261243 404.669323 \nL 604.419563 405.458602 \nL 604.736204 414.521069 \nL 604.894525 403.544811 \nL 605.052845 404.309782 \nL 605.211166 401.028528 \nL 605.369486 403.285438 \nL 605.527807 403.138096 \nL 605.686127 410.080626 \nL 605.844448 400.035015 \nL 606.002768 399.425708 \nL 606.161089 398.148263 \nL 606.319409 409.012574 \nL 606.47773 399.255047 \nL 606.794371 408.243374 \nL 607.111011 400.470701 \nL 607.269332 419.005527 \nL 607.585973 398.856567 \nL 607.744293 397.397619 \nL 607.902614 404.139968 \nL 608.219255 402.24537 \nL 608.377575 406.748811 \nL 608.535896 405.763073 \nL 608.694216 408.707234 \nL 608.852537 398.086821 \nL 609.010857 404.025541 \nL 609.169178 403.056287 \nL 609.327498 403.273843 \nL 609.644139 400.619783 \nL 609.80246 402.346745 \nL 609.96078 397.732666 \nL 610.119101 402.251901 \nL 610.277421 399.212224 \nL 610.435742 408.461358 \nL 610.594062 399.29352 \nL 610.910703 397.303722 \nL 611.069023 406.778226 \nL 611.227344 397.553655 \nL 611.385664 399.175236 \nL 611.543985 405.988705 \nL 611.702305 401.936159 \nL 611.860626 406.46152 \nL 612.018946 406.426852 \nL 612.335587 401.803588 \nL 612.652228 400.87999 \nL 612.810549 397.303477 \nL 612.968869 401.475612 \nL 613.12719 395.728157 \nL 613.28551 396.710072 \nL 613.760472 405.287405 \nL 613.918792 411.204281 \nL 614.077113 409.057273 \nL 614.235433 403.434613 \nL 614.393753 417.617852 \nL 614.552074 408.722047 \nL 614.710394 413.525823 \nL 614.868715 404.373189 \nL 615.027035 409.562959 \nL 615.185356 398.824328 \nL 615.343676 406.885468 \nL 615.501997 406.42801 \nL 615.660317 402.556064 \nL 615.818638 403.588288 \nL 615.976958 395.829422 \nL 616.135279 394.153012 \nL 616.293599 406.567886 \nL 616.45192 406.230903 \nL 616.61024 409.236915 \nL 616.768561 415.26408 \nL 616.926881 401.183857 \nL 617.243522 412.709437 \nL 617.401843 408.644558 \nL 617.718484 396.790848 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404.109673 \nL 625.001226 410.907347 \nL 625.159546 408.259758 \nL 625.317867 397.691199 \nL 625.476187 398.574008 \nL 625.792828 415.442839 \nL 626.109469 403.535926 \nL 626.42611 398.433583 \nL 626.58443 404.035091 \nL 626.742751 399.700624 \nL 627.059392 403.560654 \nL 627.217712 417.384247 \nL 627.692674 395.49479 \nL 627.850994 402.026164 \nL 628.009315 403.63383 \nL 628.167635 397.812259 \nL 628.642597 409.389738 \nL 628.800917 407.008443 \nL 628.959238 398.549961 \nL 629.117558 400.585896 \nL 629.275879 405.908855 \nL 629.592519 397.625412 \nL 629.75084 396.104417 \nL 629.90916 399.735687 \nL 630.067481 408.16393 \nL 630.225801 407.968063 \nL 630.384122 400.103916 \nL 630.542442 407.431602 \nL 630.700763 401.103757 \nL 630.859083 400.91421 \nL 631.017404 415.831405 \nL 631.492365 403.566643 \nL 631.650686 402.979283 \nL 631.809006 404.592795 \nL 631.967327 401.97543 \nL 632.125647 403.704742 \nL 632.283968 399.556208 \nL 632.600609 412.115673 \nL 632.91725 397.380703 \nL 633.07557 405.00526 \nL 633.23389 404.646505 \nL 633.392211 401.1843 \nL 633.550531 419.368976 \nL 633.708852 401.472191 \nL 633.867172 402.727427 \nL 634.025493 400.138962 \nL 634.342134 412.399695 \nL 634.500454 403.316176 \nL 634.658775 402.508328 \nL 634.817095 410.04231 \nL 634.975416 397.71724 \nL 635.133736 395.477363 \nL 635.292057 401.899544 \nL 635.450377 418.711256 \nL 635.608698 404.749142 \nL 635.767018 404.205112 \nL 635.925339 409.845904 \nL 636.083659 400.302375 \nL 636.4003 408.717797 \nL 636.716941 399.301025 \nL 636.875262 404.006878 \nL 637.033582 402.629073 \nL 637.191902 402.309057 \nL 637.350223 409.669694 \nL 637.508543 407.039887 \nL 637.666864 400.324065 \nL 637.825184 402.717313 \nL 637.983505 399.495819 \nL 638.141825 402.166336 \nL 638.300146 411.591666 \nL 638.616787 403.008613 \nL 638.775107 406.975364 \nL 638.933428 403.177435 \nL 639.091748 402.415192 \nL 639.250069 407.940394 \nL 639.408389 405.458575 \nL 639.56671 398.678807 \nL 639.72503 397.454383 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404.26591 \nL 646.849452 417.676993 \nL 647.007772 415.247042 \nL 647.166093 415.159426 \nL 647.482734 400.884109 \nL 647.641054 402.823774 \nL 647.957695 398.470142 \nL 648.116016 404.191976 \nL 648.274336 403.120029 \nL 648.432656 398.073578 \nL 648.749297 399.880262 \nL 648.907618 411.101327 \nL 649.065938 398.381817 \nL 649.224259 399.843073 \nL 649.382579 407.340816 \nL 649.69922 401.710128 \nL 649.857541 412.315917 \nL 650.015861 403.519796 \nL 650.174182 425.065121 \nL 650.332502 405.422986 \nL 650.490823 408.543162 \nL 650.649143 397.53681 \nL 650.807464 395.704694 \nL 650.965784 397.622543 \nL 651.124105 401.689332 \nL 651.282425 410.453897 \nL 651.599066 403.135459 \nL 651.757387 441.804352 \nL 651.915707 406.782981 \nL 652.074027 404.94411 \nL 652.390668 398.0019 \nL 652.548989 402.670464 \nL 652.707309 411.304043 \nL 652.86563 400.058321 \nL 653.02395 403.78263 \nL 653.182271 401.369318 \nL 653.340591 407.189549 \nL 653.498912 406.860321 \nL 653.657232 420.654239 \nL 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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "source": [
-    "# ADC parameters\n",
-    "adc_freq = 48.813e3\n",
-    "adc_buff_n = 8192\n",
-    "adc_bits = 12\n",
-    "adc_quants = 2 ** adc_bits\n",
-    "adc_vref = 3.3\n",
-    "adc_quant_v = adc_vref / adc_quants\n",
-    "\n",
-    "# input signal parameters\n",
-    "sig_points = adc_buff_n\n",
-    "sig_vpeak_max = adc_vref / 2\n",
-    "sig_vpeak = 1.5\n",
-    "assert sig_vpeak <= sig_vpeak_max  \n",
-    "sig_voffset = adc_vref / 2\n",
-    "sig_freq = 5.143e3\n",
-    "np.random.seed(42)  # for reproducible results\n",
-    "sig_ph0 = np.random.uniform(0, 2 * np.pi)\n",
-    "sig_f = lambda t: signals.sin(t, peak=sig_vpeak, offset=sig_voffset, freq=sig_freq, ph0=sig_ph0)\n",
-    "\n",
-    "# Analog to digital conversion\n",
-    "sig_sampled = converters.analog2digital(sig_f=sig_f,\n",
-    "                                        sample_freq=adc_freq,\n",
-    "                                        sample_n=sig_points,\n",
-    "                                        sample_bits=adc_bits,\n",
-    "                                        vref=adc_vref,\n",
-    "                                        noisy_lsb=2)\n",
-    "\n",
-    "# Analyze spectrum and show plots\n",
-    "spectrum.analyze(sig_sampled, adc_bits, adc_vref, adc_freq, window='hanning')"
-   ]
-  }
- ]
-}
\ No newline at end of file
diff --git a/pylintrc b/pylintrc
new file mode 100644
index 0000000..e937446
--- /dev/null
+++ b/pylintrc
@@ -0,0 +1,25 @@
+[MASTER]
+reports=no
+
+disable=
+    format,
+    invalid-name,
+    locally-disabled,
+    unused-argument,
+    duplicate-code,
+    implicit-str-concat,
+    too-many-arguments,
+    too-many-branches,
+    too-many-instance-attributes,
+    too-many-locals,
+    too-many-public-methods,
+    too-many-return-statements,
+    too-many-statements,
+    too-many-lines,
+    too-few-public-methods,
+    no-else-return,
+    unexpected-keyword-arg,
+    unnecessary-pass,
+    consider-using-f-string,
+    unused-variable,
+    consider-using-join,
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..c90c227
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,88 @@
+[build-system]
+requires = ["setuptools~=68.0", "wheel~=0.40.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "python-adc-eval"
+version = "0.1.0"
+license = {text = "MIT"}
+description = "ADC Evaluation Library"
+readme = "README.rst"
+authors = [{name = "Kevin Fronczak", email = "kfronczak@gmail.com"}]
+keywords = ["adc", "analog-to-digital", "evaluation", "eval", "spectrum"]
+classifiers = [
+    "License :: OSI Approved :: MIT License",
+    "Operating System :: OS Independent",
+    "Programming Language :: Python :: 3.8",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Topic :: Scientific/Engineering",
+]
+requires-python = ">=3.8.0"
+dependencies = [
+    "matplotlib==3.7.2",
+]
+
+[project.urls]
+"Source Code" = "https://github.com/fronzbot/python-adc-eval"
+"Bug Reports" = "https://github.com/fronzbot/python-adc-eval/issues"
+
+[tool.setuptools]
+platforms = ["any"]
+include-package-data = true
+
+[tool.setuptools.packages.find]
+include = ["adc_eval*"]
+
+[tool.ruff]
+select = [
+    "C",  # complexity
+    "D",  # docstrings
+    "E",  # pydocstyle
+    "F",  # pyflakes/autoflake
+    "G",  # flake8-logging-format
+    "I",  # isort
+    "N815",  # Varible {name} in class scope should not be mixedCase
+    "PGH004",  # Use specific rule codes when using noqa
+    "PLC",  # pylint
+    "PLE",  # pylint
+    "PLR",  # pylint
+    "PLW",  # pylint
+    "Q000",  # Double quotes found but single quotes preferred
+    "SIM118",  # Use {key} in {dict} instead of {key} in {dict}.keys()
+    "T20",  # flake8-print
+    "TRY004",  # Prefer TypeError exception for invalid type
+    "TRY200",  # Use raise from to specify exception cause
+    "UP",  # pyupgrade
+    "W",  # pycodestyle
+]
+ignore = [
+    "D202",  # No blank lines allowed after function docstring
+    "D203",  # 1 blank line required before class docstring
+    "D213",  # Multi-line docstring summary should start at the second line
+    "D406",  # Section name should end with a newline
+    "D407",  # Section name underlining
+    "E501",  # line too long
+    "E731",  # do not assign a lambda expression, use a def
+    "PLC1901", # Lots of false positives
+    # False positives https://github.com/astral-sh/ruff/issues/5386
+    "PLC0208", # Use a sequence type instead of a `set` when iterating over values
+    "PLR0911", # Too many return statements ({returns} > {max_returns})
+    "PLR0912", # Too many branches ({branches} > {max_branches})
+    "PLR0913", # Too many arguments to function call ({c_args} > {max_args})
+    "PLR0915", # Too many statements ({statements} > {max_statements})
+    "PLR2004",  # Magic value used in comparison, consider replacing {value} with a constant variable
+    "PLW2901", # Outer {outer_kind} variable {name} overwritten by inner {inner_kind} target
+    "UP006", # keep type annotation style as is
+    "UP007", # keep type annotation style as is
+    # Ignored due to performance: https://github.com/charliermarsh/ruff/issues/2923
+    "UP038", # Use `X | Y` in `isinstance` call instead of `(X, Y)`
+]
+
+line-length = 88
+
+target-version = "py39"
+
+[tool.ruff.mccabe]
+max-complexity = 10
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..3e0dfc5
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1 @@
+matplotlib==3.7.2
diff --git a/requirements_test.txt b/requirements_test.txt
new file mode 100644
index 0000000..de6f92f
--- /dev/null
+++ b/requirements_test.txt
@@ -0,0 +1,7 @@
+black==23.7.0
+coverage==7.2.7
+pylint==2.17.4
+ruff==0.0.278
+tox==4.6.4
+restructuredtext-lint==1.4.0
+pygments==2.15.1
diff --git a/spectrum.py b/spectrum.py
deleted file mode 100644
index 9df223e..0000000
--- a/spectrum.py
+++ /dev/null
@@ -1,263 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-"""
-Some basic spectral analysis.
-
-References:
-  - Analog Devices MT-003 TUTORIAL
-    "Understand SINAD, ENOB, SNR, THD, THD + N, and SFDR so You Don't Get Lost in the Noise Floor"
-  - National Instruments Application Note 041
-    "The Fundamentals of FFT-Based Signal Analysis and Measurement"
-"""
-
-import numpy as np
-import matplotlib
-import matplotlib.pyplot as plt
-
-
-def db2amp(db):
-    """Decibels to amplitutde ratio"""
-    return 10 ** (0.05 * db)
-
-
-def amp2db(a):
-    """Amplitutde ratio to decibels"""
-    return 20 * np.log10(a)
-
-
-def db2pow(db):
-    """Decibels to power ratio"""
-    return 10 ** (0.1 * db)
-
-
-def pow2db(p):
-    """Power ratio to decibels"""
-    return 10 * np.log10(p)
-
-
-def enob(sinad):
-    """Calculate ENOB from SINAD"""
-    return (sinad - 1.76) / 6.02
-
-
-def snr_theor(n):
-    """Theoretical SNR of an ideal n-bit ADC in dB"""
-    return 6.02 * n + 1.76
-
-
-def noise_floor(snr, m):
-    """Noise floor of the m-point FFT in dB"""
-    return -snr - 10 * np.log10(m / 2)
-
-
-def harmonics(psp, fft_n, ref_pow, sample_freq, leak=20, n=5, window='hanning'):
-    """Obtain first n harmonics properties from power spectrum"""
-    # Coherence Gain and Noise Power Bandwidth for different windows
-    win_params = {'uniform': {'cg': 1.0, 'npb': 1.0},
-                  'hanning': {'cg': 0.5, 'npb': 1.5},
-                  'hamming': {'cg': 0.54, 'npb': 1.36},
-                  'blackman': {'cg': 0.42, 'npb': 1.73}}[window]
-    fft_n = len(psp) * 2  # one side spectrum provided
-    df = sample_freq / fft_n
-    # calculate fundamental frequency
-    fund_bin = np.argmax(psp)
-    fund_freq = (np.sum([psp[i] * i * df for i in range(fund_bin - leak, fund_bin + leak + 1)]) /
-                 np.sum(psp[fund_bin - leak: fund_bin + leak + 1]))
-    # calculate harmonics info
-    h = []
-    for i in range(1, n + 1):
-        h_i = {'num': i}
-        zone_freq = (fund_freq * i) % sample_freq
-        h_i['freq'] = sample_freq - zone_freq if zone_freq >= (sample_freq / 2) else zone_freq
-        h_i['central_bin'] = int(h_i['freq'] / df)
-        h_i['bins'] = np.array(range(h_i['central_bin'] - leak, h_i['central_bin'] + leak + 1))
-        h_i['pow'] = ((1 / win_params['cg']) ** 2) * np.sum(psp[h_i['bins']]) / win_params['npb']
-        h_i['vrms'] = np.sqrt(h_i['pow'])
-        if i == 1:
-            h_i['db'] = '%.2f dBFS' % pow2db(h_i['pow'] / ref_pow)
-        else:
-            h_i['db'] = '%.2f dBc' % pow2db(h_i['pow'] / h[0]['pow'])
-        h += [h_i]
-    return h
-
-
-def signal_noise(psp, harmonics):
-    """Obtain different signal+noise characteristics from spectrum"""
-    # noise + distortion power
-    nd_psp = np.copy(psp)
-    nd_psp[harmonics[0]['bins']] = 0  # remove main harmonic
-    nd_psp[0] = 0  # remove dc
-    nd_pow = sum(nd_psp)
-    # noise power
-    n_psp = np.copy(psp)
-    for h in harmonics:
-        n_psp[h['bins']] = 0  # remove all harmonics
-    n_psp[0] = 0  # remove dc
-    n_pow = sum(n_psp)
-    # distortion power
-    d_pow = np.sum([h['pow'] for h in harmonics]) - harmonics[0]['pow']
-    # calculate results
-    sinad = pow2db(harmonics[0]['pow'] / nd_pow)
-    thd = pow2db(harmonics[0]['pow'] / d_pow)
-    snr = pow2db(harmonics[0]['pow'] / n_pow)
-    sfdr = pow2db(max(nd_psp) / harmonics[0]['pow'])
-    return sinad, thd, snr, sfdr
-
-
-def analyze(sig, adc_bits, adc_vref, adc_freq, window='hanning'):
-    """Do spectral analysis for ADC samples"""
-    # Calculate some useful parameters
-    sig_vpeak_max = adc_vref / 2
-    sig_vrms_max = sig_vpeak_max / np.sqrt(2)
-    sig_pow_max = sig_vrms_max ** 2
-    ref_pow = sig_pow_max
-    adc_prd = 1 / adc_freq
-    adc_quants = 2 ** adc_bits
-    dv = adc_vref / adc_quants
-    sig_n = len(sig)
-    dt = 1 / adc_freq
-    fft_n = sig_n
-    df = adc_freq / fft_n
-    win_coef = {'uniform': np.ones(sig_n),
-                'hanning': np.hanning(sig_n)}[window]
-    sp_leak = 20  # spectru leak bins
-    h_n = 5  # harmonics number
-
-    # Convert samples to voltage
-    sig_v = sig * dv
-
-    # Remove DC and apply window
-    sig_dc = np.mean(sig_v)
-    sig_windowed = (sig_v - sig_dc) * win_coef
-
-    # Calculate one-side amplitude spectrum (Vrms)
-    asp = np.sqrt(2) * np.abs(np.fft.rfft(sig_windowed)) / sig_n
-
-    # Calculate one-side power spectrum (Vrms^2)
-    psp = np.power(asp, 2)
-    psp_db = pow2db(psp / ref_pow)
-
-    # Calculate harmonics
-    h = harmonics(psp=psp, fft_n=fft_n, ref_pow=ref_pow, sample_freq=adc_freq, leak=sp_leak, n=h_n, window=window)
-
-    # Input signal parameters (based on 1st harmonic)
-    sig_pow = h[0]['pow']
-    sig_vrms = h[0]['vrms']
-    sig_vpeak = sig_vrms * np.sqrt(2)
-    sig_freq = h[0]['freq']
-    sig_prd = 1 / sig_freq
-
-    # Calculate SINAD, THD, SNR, SFDR
-    adc_sinad, adc_thd, adc_snr, adc_sfdr = signal_noise(psp, h)
-
-    # Calculate ENOB
-    # sinad correction to normalize ENOB to full-scale regardless of input signal amplitude
-    adc_enob = enob(adc_sinad + pow2db(ref_pow / sig_pow))
-
-    # Calculate Noise Floor
-    adc_noise_floor = noise_floor(adc_snr, fft_n)
-
-    # Create plots
-    fig = plt.figure(figsize=(14, 7))
-    gs = matplotlib.gridspec.GridSpec(2, 2, width_ratios=[3, 1])
-
-    # Time plot
-    ax_time = plt.subplot(gs[0, 0])
-    ax_time_xlim = min(sig_n, int(5 * sig_prd / dt))
-    ax_time.plot(np.arange(0, ax_time_xlim), sig[:ax_time_xlim], color='C0')
-    ax_time.set(ylabel='ADC code', ylim=[0, adc_quants])
-    ax_time.set(yticks=list(range(0, adc_quants, adc_quants // 8)) + [adc_quants - 1])
-    ax_time.set(xlabel='Sample', xlim=[0, ax_time_xlim - 1])
-    ax_time.set(xticks=range(0, ax_time_xlim, max(1, ax_time_xlim // 20)))
-    ax_time.grid(True)
-    ax_time_xsec = ax_time.twiny()
-    ax_time_xsec.set(xticks=ax_time.get_xticks())
-    ax_time_xsec.set(xbound=ax_time.get_xbound())
-    ax_time_xsec.set_xticklabels(['%.02f' % (x * dt * 1e3) for x in ax_time.get_xticks()])
-    ax_time_xsec.set_xlabel('Time, ms')
-    ax_time_ysec = ax_time.twinx()
-    ax_time_ysec.set(yticks=ax_time.get_yticks())
-    ax_time_ysec.set(ybound=ax_time.get_ybound())
-    ax_time_ysec.set_yticklabels(['%.02f' % (x * dv) for x in ax_time.get_yticks()])
-    ax_time_ysec.set_ylabel('Voltage, V')
-
-    # Frequency plot
-    ax_freq = plt.subplot(gs[1, 0])
-    ax_freq.plot(np.arange(0, len(psp_db)), psp_db, color='C0', zorder=0, label="Spectrum")
-    for h_i in h:
-        ax_freq.text(h_i['central_bin'] + 2, psp_db[h_i['central_bin']], str(h_i['num']),
-                     va='bottom', ha='left', weight='bold')
-        ax_freq.plot(h_i['bins'], psp_db[h_i['bins']], color='C4')
-    ax_freq.plot(0, 0, color='C4', label="Harmonics")
-    ax_freq.set(ylabel='dB', ylim=[-150, 10])
-    ax_freq.set(xlabel='Sample', xlim=[0, fft_n / 2])
-    ax_freq.set(xticks=list(range(0, fft_n // 2, fft_n // 32)) + [fft_n // 2 - 1])
-    ax_freq.grid(True)
-    ax_freq.legend(loc="lower right", ncol=3)
-    ax_freq_sec = ax_freq.twiny()
-    ax_freq_sec.set_xticks(ax_freq.get_xticks())
-    ax_freq_sec.set_xbound(ax_freq.get_xbound())
-    ax_freq_sec.set_xticklabels(['%.02f' % (x * df * 1e-3) for x in ax_freq.get_xticks()])
-    ax_freq_sec.set_xlabel('Frequency, kHz')
-
-    # Information plot
-    ax_info = plt.subplot(gs[:, 1])
-    ax_info.set(xlim=[0, 10], xticks=[], ylim=[0, 10], yticks=[])
-    harmonics_str = '\n'.join(['%d%s @ %-10s : %s' % (h_i['num'], ['st', 'nd', 'rd', 'th', 'th'][h_i['num'] - 1],
-                                                      '%0.3f kHz' % (h_i['freq'] * 1e-3),
-                                                      h_i['db']) for h_i in h])
-    ax_info_str = """
-========= FFT ==========
-Points           : {fft_n}
-Freq. resolution : {fft_res:.4} Hz
-Window           : {fft_window}
-
-======= Harmonics ======
-{harmonics_str}
-
-===== Input signal =====
-Frequency        : {sig_freq:.4} kHz
-Amplitude (Vpeak): {sig_vpeak:.4} V
-DC offset        : {sig_dc:.4} V
-
-========= ADC ==========
-Sampling freq.   : {adc_freq:.4} kHz
-Sampling period  : {adc_prd:.4} us
-Reference volt.  : {adc_vref:.4} V
-Bits             : {adc_bits} bits
-Quants           : {adc_quants}
-Quant            : {adc_quant:.4} mV
-SNR              : {adc_snr:.4} dB
-SINAD            : {adc_sinad:.4} dB
-THD              : {adc_thd:.4} dB
-ENOB             : {adc_enob:.4} bits
-SFDR             : {adc_sfdr:.4} dBc
-Noise floor      : {adc_nfloor:.4} dBFS
-""".format(fft_n=fft_n,
-           fft_res=df,
-           fft_window=window,
-           harmonics_str=harmonics_str,
-           sig_freq=sig_freq * 1e-3,
-           sig_vpeak=sig_vpeak,
-           sig_dc=sig_dc,
-           adc_freq=adc_freq * 1e-3,
-           adc_prd=adc_prd * 1e6,
-           adc_vref=adc_vref,
-           adc_bits=adc_bits,
-           adc_quants=adc_quants,
-           adc_quant=dv * 1e3,
-           adc_snr=adc_snr,
-           adc_thd=adc_thd,
-           adc_sinad=adc_sinad,
-           adc_enob=adc_enob,
-           adc_sfdr=adc_sfdr,
-           adc_nfloor=adc_noise_floor)
-    ax_info.text(1, 9.5, ax_info_str, va='top', ha='left', family='monospace')
-
-    # General plotting settings
-    plt.tight_layout()
-    plt.style.use('bmh')
-
-    # Show the result
-    plt.show()
diff --git a/tox.ini b/tox.ini
new file mode 100644
index 0000000..3e99ad3
--- /dev/null
+++ b/tox.ini
@@ -0,0 +1,28 @@
+[tox]
+envlist = lint,build
+
+[testenv]
+setenv = 
+    LANG=en_US.UTF-8
+    PYTHONPATH = {toxinidir}
+deps =
+    -r{toxinidir}/requirements.txt
+    -r{toxinidir}/requirements_test.txt
+
+[testenv:lint]
+deps = 
+    -r{toxinidir}/requirements.txt
+    -r{toxinidir}/requirements_test.txt
+basepython = python3
+ignore_errors = True
+commands =
+    pylint --rcfile={toxinidir}/pylintrc adc_eval
+    ruff check adc_eval
+    black --check --diff adc_eval
+    rst-lint README.rst
+
+[testenv:build]
+basepython = python3
+ignore_errors = True
+commands = 
+    pip install .