From c0361da0d6c84fdc4ef67ac07fbe80bb7ff8c593 Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Thu, 25 Jan 2024 22:43:52 -0500 Subject: [PATCH 01/37] added ability to set a peak intensity threshold for fitting initial peak parameters --- docs/quickstart.md | 2 +- peakipy/cli/fit.py | 65 ++++++++++++++++++++++++++++++++++++++++----- peakipy/cli/main.py | 5 ++++ peakipy/core.py | 51 ++++++++++++++++++++++------------- pyproject.toml | 2 +- 5 files changed, 99 insertions(+), 26 deletions(-) diff --git a/docs/quickstart.md b/docs/quickstart.md index a3a44dd3..972f372c 100644 --- a/docs/quickstart.md +++ b/docs/quickstart.md @@ -95,7 +95,7 @@ Requirements === "Latest" - The latest version (1.1.2) of `peakipy` requires Python 3.10 or above (see `pyproject.toml` for details). + The latest version (1.1.3) of `peakipy` requires Python 3.10 or above (see `pyproject.toml` for details). === "0.2.0" diff --git a/peakipy/cli/fit.py b/peakipy/cli/fit.py index 1d0c229b..36baa26b 100644 --- a/peakipy/cli/fit.py +++ b/peakipy/cli/fit.py @@ -2,6 +2,7 @@ """Fit and deconvolute NMR peaks: Functions used for running peakipy fit """ from pathlib import Path +from typing import Optional, List from multiprocessing import cpu_count, Pool import numpy as np @@ -88,12 +89,55 @@ def split_peaklist(peaklist, n_cpu, tmp_path=tmp_path): class FitPeaksInput: """input data for the fit_peaks function""" - def __init__(self, args: dict, data: np.array, config: dict, plane_numbers: list): - + def __init__( + self, + args: dict, + data: np.array, + config: dict, + plane_numbers: list, + planes_for_initial_fit: Optional[List[int]] = None, + use_only_planes_above_threshold: Optional[float] = None, + ): self._data = data self._args = args self._config = config self._plane_numbers = plane_numbers + self._planes_for_initial_fit = planes_for_initial_fit + self._use_only_planes_above_threshold = use_only_planes_above_threshold + + def check_integer_list(self): + if hasattr(self._planes_for_initial_fit, "append"): + pass + else: + return False + if all([(type(i) == int) for i in self._planes_for_initial_fit]): + pass + else: + return False + if all([((i - 1) > self._data.shape[0]) for i in self._planes_for_initial_fit]): + return True + else: + return False + + def sum_planes_for_initial_fit(self): + if ( + self._planes_for_initial_fit + == self._use_only_planes_above_threshold + == None + ): + return self._data.sum(axis=0) + + elif self.check_integer_list(): + return self._data[self._planes_for_initial_fit].sum(axis=0) + + elif type(self._use_only_planes_above_threshold) == float: + # very crude at the moment + return self._data[ + self._data.max(axis=1).max(axis=1) + > self._use_only_planes_above_threshold + ] + else: + return self._data.sum(axis=0) @property def data(self): @@ -111,12 +155,15 @@ def config(self): def plane_numbers(self): return self._plane_numbers + @property + def summed_planes_for_initial_fit(self): + return self.sum_planes_for_initial_fit() + class FitPeaksResult: """Result of fitting a set of peaks""" def __init__(self, df: pd.DataFrame, log: str): - self._df = df self._log = log @@ -151,6 +198,7 @@ def fit_peaks(peaks: pd.DataFrame, fit_input: FitPeaksInput) -> FitPeaksResult: # console.print(to_fix, style="red bold") noise = fit_input.args.get("noise") verb = fit_input.args.get("verb") + initial_fit_threshold = fit_input.args.get("initial_fit_threshold") lineshape = fit_input.args.get("lineshape") xy_bounds = fit_input.args.get("xy_bounds") vclist = fit_input.args.get("vclist") @@ -225,7 +273,7 @@ def fit_peaks(peaks: pd.DataFrame, fit_input: FitPeaksInput) -> FitPeaksResult: # fits sum of all planes first fit_result = fit_first_plane( group, - summed_planes, + fit_input.data, # norm(summed_planes), uc_dics, lineshape=lineshape, @@ -233,6 +281,7 @@ def fit_peaks(peaks: pd.DataFrame, fit_input: FitPeaksInput) -> FitPeaksResult: verbose=verb, noise=noise, fit_method=fit_input.config.get("fit_method", "leastsq"), + threshold=initial_fit_threshold, ) fit_result.plot( plot_path=fit_input.args.get("plot"), @@ -304,8 +353,12 @@ def fit_peaks(peaks: pd.DataFrame, fit_input: FitPeaksInput) -> FitPeaksResult: # deal with lineshape specific parameters match lineshape: case lineshape.PV_PV: - frac_x, frac_err_x, name = get_params(first.params, "fraction_x") - frac_y, frac_err_y, name = get_params(first.params, "fraction_y") + frac_x, frac_err_x, name = get_params( + first.params, "fraction_x" + ) + frac_y, frac_err_y, name = get_params( + first.params, "fraction_y" + ) fractions_x.extend(frac_x) fractions_y.extend(frac_y) case lineshape.V: diff --git a/peakipy/cli/main.py b/peakipy/cli/main.py index 9842105d..09d0dbaf 100644 --- a/peakipy/cli/main.py +++ b/peakipy/cli/main.py @@ -373,6 +373,7 @@ def fit( vclist: Optional[Path] = None, plane: Optional[List[int]] = None, exclude_plane: Optional[List[int]] = None, + initial_fit_threshold: Optional[float] = None, mp: bool = True, plot: Optional[Path] = None, show: bool = False, @@ -409,6 +410,9 @@ def fit( exclude_plane : Optional[List[int]] Specific plane(s) to fit [default: None] eg. [1,4,5] will exclude planes 1, 4 and 5 + initial_fit_threshold: Optional[float] + threshold used to select planes for fitting of initial lineshape parameters. Only planes with + intensities above this threshold will be included in the intial fit of summed planes. mp : bool Use multiprocessing [default: True] plot : Optional[Path] @@ -472,6 +476,7 @@ def fit( args["verb"] = verb args["show"] = show args["mp"] = mp + args["initial_fit_threshold"] = initial_fit_threshold # read vclist if vclist is None: diff --git a/peakipy/core.py b/peakipy/core.py index c48fd044..5a8b2dce 100644 --- a/peakipy/core.py +++ b/peakipy/core.py @@ -24,7 +24,7 @@ import json from datetime import datetime from pathlib import Path -from typing import List +from typing import List, Optional from enum import Enum import numpy as np @@ -496,7 +496,6 @@ def make_param_dict(peaks, data, lineshape: Lineshape = Lineshape.PV): param_dict = {} for index, peak in peaks.iterrows(): - str_form = lambda x: "%s%s" % (to_prefix(peak.ASS), x) # using exact value of points (i.e decimal) param_dict[str_form("center_x")] = peak.X_AXISf @@ -668,10 +667,10 @@ def update_params( params[k].min = v - y_bound params[k].max = v + y_bound # pass - #print( + # print( # "setting limit of %s, min = %.3e, max = %.3e" # % (k, params[k].min, params[k].max) - #) + # ) elif "sigma" in k: params[k].min = 0.0 params[k].max = 1e4 @@ -746,6 +745,23 @@ def df_to_rich_table(df, title: str, columns: List[str], styles: str): return table +def make_mask_from_peak_cluster(group, data): + mask = np.zeros(data.shape, dtype=bool) + for index, peak in group.iterrows(): + mask += make_mask( + data, peak.X_AXISf, peak.Y_AXISf, peak.X_RADIUS, peak.Y_RADIUS + ) + return mask, peak + + +def select_planes_above_threshold_from_masked_data(data, threshold=None): + if threshold == None: + data = data + else: + data = data[data.max(axis=1) > threshold] + return data + + def fit_first_plane( group, data, @@ -756,6 +772,7 @@ def fit_first_plane( log=None, noise=1.0, fit_method="leastsq", + threshold: Optional[float] = None, ): """Deconvolute group of peaks @@ -796,8 +813,6 @@ def fit_first_plane( :rtype: FitResult """ - shape = data.shape - mask = np.zeros(shape, dtype=bool) match lineshape: case lineshape.PV | lineshape.G | lineshape.L: lineshape_function = pvoigt2d @@ -821,10 +836,8 @@ def fit_first_plane( cen_x = [p_guess[k].value for k in p_guess if "center_x" in k] cen_y = [p_guess[k].value for k in p_guess if "center_y" in k] - for index, peak in group.iterrows(): - mask += make_mask( - data, peak.X_AXISf, peak.Y_AXISf, peak.X_RADIUS, peak.Y_RADIUS - ) + first_plane_data = data[0] + mask, peak = make_mask_from_peak_cluster(group, first_plane_data) x_radius = group.X_RADIUS.max() y_radius = group.Y_RADIUS.max() @@ -839,6 +852,7 @@ def fit_first_plane( ) #  deal with peaks on the edge of spectrum + shape = data.shape if min_y < 0: min_y = 0 @@ -851,7 +865,14 @@ def fit_first_plane( if max_x > shape[-1]: max_x = shape[-1] - peak_slices = data.copy()[mask] + # print("DATA",data.shape, mask.shape) + peak_slices = np.array([d[mask] for d in data]) + # print("Peak slices", peak_slices.shape) + peak_slices = select_planes_above_threshold_from_masked_data(peak_slices, threshold) + # print(peak_slices.shape) + peak_slices = peak_slices.sum(axis=0) + # print(peak_slices.shape) + # must be a better way to make the meshgrid x = np.arange(shape[-1]) y = np.arange(shape[-2]) @@ -871,7 +892,7 @@ def fit_first_plane( z_sim = mod.eval(XY=XY, params=out.params) z_sim[~mask] = np.nan - z_plot = data.copy() + z_plot = first_plane_data.copy() z_plot[~mask] = np.nan #  also if peak position changed significantly from start then add warning @@ -1435,7 +1456,6 @@ def __init__( posF2="Position F1", verbose=False, ): - dic, data = ng.pipe.read(data_path) Pseudo3D.__init__(self, dic, data, dims) self.fmt = fmt @@ -1649,7 +1669,6 @@ def add_fix_bound_columns(self): pass def _read_analysis(self): - df = pd.read_csv(self.peaklist_path, delimiter="\t") new_columns = [self.analysis_to_pipe_dic.get(i, i) for i in df.columns] pipe_columns = dict(zip(df.columns, new_columns)) @@ -1658,7 +1677,6 @@ def _read_analysis(self): return df def _read_assign(self): - df = pd.read_csv(self.peaklist_path, delimiter="\t") new_columns = [self.assign_to_pipe_dic.get(i, i) for i in df.columns] pipe_columns = dict(zip(df.columns, new_columns)) @@ -1667,7 +1685,6 @@ def _read_assign(self): return df def _read_sparky(self): - df = pd.read_csv( self.peaklist_path, skiprows=1, @@ -1988,7 +2005,6 @@ class LoadData(Peaklist): """ def read_peaklist(self): - if self.peaklist_path.suffix == ".csv": self.df = pd.read_csv(self.peaklist_path) # , comment="#") @@ -2003,7 +2019,6 @@ def read_peaklist(self): return self.df def check_data_frame(self): - # make diameter columns if "X_DIAMETER_PPM" in self.df.columns: pass diff --git a/pyproject.toml b/pyproject.toml index 18a39c91..938d56ee 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "peakipy" -version = "1.1.2" +version = "1.1.3" description = "Deconvolute overlapping NMR peaks" authors = ["Jacob Brady "] From 2fb83f0766b0d242a3389021b360392baabfe39e Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Sun, 28 Jan 2024 23:28:12 -0500 Subject: [PATCH 02/37] now using bokeh contours --- peakipy/cli/edit.py | 148 ++++++++++++++++++++++++-------------------- 1 file changed, 80 insertions(+), 68 deletions(-) diff --git a/peakipy/cli/edit.py b/peakipy/cli/edit.py index ccb9e56a..70b0f0bd 100644 --- a/peakipy/cli/edit.py +++ b/peakipy/cli/edit.py @@ -13,6 +13,7 @@ import nmrglue as ng import numpy as np import matplotlib.pyplot as plt +import pandas as pd from matplotlib.cm import magma, autumn, viridis from skimage.filters import threshold_otsu from rich import print @@ -37,7 +38,8 @@ Div, ) from bokeh.plotting import figure -from bokeh.palettes import PuBuGn9, Category20 +from bokeh.plotting.contour import contour_data +from bokeh.palettes import PuBuGn9, Category20, Viridis256, RdGy11, Reds256 from peakipy.core import LoadData, read_config, StrucEl @@ -46,17 +48,14 @@ class BokehScript: - def __init__( - self, peaklist_path: Path, data_path: Path - ): - + def __init__(self, peaklist_path: Path, data_path: Path): self._path = peaklist_path self._data_path = data_path args, config = read_config({}) - #self.args = args - #self.config = config - self._dims = config.get("dims", [0,1,2]) - self.thres = config.get("thres",1e6) + # self.args = args + # self.config = config + self._dims = config.get("dims", [0, 1, 2]) + self.thres = config.get("thres", 1e6) self._peakipy_data = LoadData( self._path, self._data_path, dims=self._dims, verbose=True ) @@ -192,21 +191,27 @@ def setup_plot(self): self.peakipy_data.f1_ppm_0, self.peakipy_data.f1_ppm_1, ) - self.spec_source = get_contour_data( - self.peakipy_data.data[0], cl, extent=self.extent, cmap=viridis - ) - #  negative contours - self.spec_source_neg = get_contour_data( - self.peakipy_data.data[0] * -1.0, cl, extent=self.extent, cmap=autumn + + self.x_ppm_mesh, self.y_ppm_mesh = np.meshgrid( + self.peakipy_data.f2_ppm_scale, self.peakipy_data.f1_ppm_scale ) - self.p.multi_line( - xs="xs", ys="ys", line_color="line_color", source=self.spec_source + self.positive_contour_renderer = self.p.contour( + self.x_ppm_mesh, + self.y_ppm_mesh, + self.peakipy_data.data[0], + cl, + fill_color=Viridis256, + line_color="black", ) - self.p.multi_line( - xs="xs", ys="ys", line_color="line_color", source=self.spec_source_neg + self.negative_contour_renderer = self.p.contour( + self.x_ppm_mesh, + self.y_ppm_mesh, + self.peakipy_data.data[0] * -1.0, + cl, + fill_color=Reds256, + line_color="black", ) - # contour_num = Slider(title="contour number", value=20, start=1, end=50,step=1) - # contour_start = Slider(title="contour start", value=100000, start=1000, end=10000000,step=1000) + self.contour_start = TextInput( value="%.2e" % self.thres, title="Contour level:", width=100 ) @@ -223,7 +228,7 @@ def setup_plot(self): height="Y_DIAMETER_PPM", source=self.source, fill_color="color", - fill_alpha=0.1, + fill_alpha=0.25, line_dash="dotted", line_color="red", ) @@ -425,7 +430,8 @@ def setup_plot(self): row( column(column(self.ls_div), column(self.radio_button_group)), column(column(self.select_plane), column(self.checkbox_group)), - ), max_width=400, + ), + max_width=400, ) # reclustering tab @@ -448,12 +454,22 @@ def setup_plot(self): fitting_layout = fitting_controls log_layout = self.fit_reports_div recluster_layout = column( - row(self.clust_div,), - row(column( - self.contour_start, self.struct_el, self.struct_el_size, self.recluster - )), max_width=400, + row( + self.clust_div, + ), + row( + column( + self.contour_start, + self.struct_el, + self.struct_el_size, + self.recluster, + ) + ), + max_width=400, + ) + save_layout = column( + self.savefilename, self.button, self.exit_button, max_width=400 ) - save_layout = column(self.savefilename, self.button, self.exit_button, max_width=400) fitting_tab = TabPanel(child=fitting_layout, title="Peak fitting") log_tab = TabPanel(child=log_layout, title="Log") @@ -487,7 +503,6 @@ def recluster_peaks(self, event): return self.peakipy_data.df def update_memcnt(self): - for ind, group in self.peakipy_data.df.groupby("CLUSTID"): self.peakipy_data.df.loc[group.index, "MEMCNT"] = len(group) @@ -504,7 +519,6 @@ def update_memcnt(self): return self.peakipy_data.df def fit_selected(self, event): - selectionIndex = self.source.selected.indices current = self.peakipy_data.df.iloc[selectionIndex] @@ -550,7 +564,6 @@ def fit_selected(self, event): os.system(plot_command) def save_peaks(self, event): - if self.savefilename.value: to_save = Path(self.savefilename.value) else: @@ -630,11 +643,14 @@ def peak_pick_callback(self, event): "include": "yes", "color": "black", } - self.peakipy_data.df = self.peakipy_data.df.append(new_peak, ignore_index=True) + new_peak = {k: [v] for k, v in new_peak.items()} + new_peak = pd.DataFrame(new_peak) + self.peakipy_data.df = pd.concat( + [self.peakipy_data.df, new_peak], ignore_index=True + ) self.update_memcnt() def slider_callback_x(self, attrname, old, new): - selectionIndex = self.source.selected.indices current = self.peakipy_data.df.iloc[selectionIndex] self.peakipy_data.df.loc[selectionIndex, "X_RADIUS"] = ( @@ -657,7 +673,6 @@ def slider_callback_x(self, attrname, old, new): self.source.data = ColumnDataSource.from_df(self.peakipy_data.df) def slider_callback_y(self, attrname, old, new): - selectionIndex = self.source.selected.indices current = self.peakipy_data.df.iloc[selectionIndex] self.peakipy_data.df.loc[selectionIndex, "Y_RADIUS"] = ( @@ -713,7 +728,6 @@ def slider_callback_y(self, attrname, old, new): # self.slider_callback(attrname, old, new, dim="Y") def update_contour(self, attrname, old, new): - new_cs = eval(self.contour_start.value) cl = new_cs * self.contour_factor ** np.arange(self.contour_num) if len(cl) > 1 and np.min(np.diff(cl)) <= 0.0: @@ -723,61 +737,59 @@ def update_contour(self, attrname, old, new): pos_neg = self.pos_neg_contour_dic[self.pos_neg_contour_radiobutton.active] if pos_neg == "pos/neg": - self.spec_source.data = dict( - get_contour_data( + self.positive_contour_renderer.set_data( + contour_data( + self.x_ppm_mesh, + self.y_ppm_mesh, self.peakipy_data.data[plane_index], cl, - extent=self.extent, - cmap=viridis, - ).data + ) ) - self.spec_source_neg.data = dict( - get_contour_data( + self.negative_contour_renderer.set_data( + contour_data( + self.x_ppm_mesh, + self.y_ppm_mesh, self.peakipy_data.data[plane_index] * -1.0, cl, - extent=self.extent, - cmap=autumn, - ).data + ) ) elif pos_neg == "pos": - self.spec_source.data = dict( - get_contour_data( + self.positive_contour_renderer.set_data( + contour_data( + self.x_ppm_mesh, + self.y_ppm_mesh, self.peakipy_data.data[plane_index], cl, - extent=self.extent, - cmap=viridis, - ).data + ) ) - self.spec_source_neg.data = dict( - get_contour_data( - self.peakipy_data.data[plane_index] * 0.0, + self.negative_contour_renderer.set_data( + contour_data( + self.x_ppm_mesh, + self.y_ppm_mesh, + self.peakipy_data.data[plane_index] * 0, cl, - extent=self.extent, - cmap=autumn, - ).data + ) ) elif pos_neg == "neg": - self.spec_source.data = dict( - get_contour_data( + self.positive_contour_renderer.set_data( + contour_data( + self.x_ppm_mesh, + self.y_ppm_mesh, self.peakipy_data.data[plane_index] * 0.0, cl, - extent=self.extent, - cmap=viridis, - ).data + ) ) - self.spec_source_neg.data = dict( - get_contour_data( + self.negative_contour_renderer.set_data( + contour_data( + self.x_ppm_mesh, + self.y_ppm_mesh, self.peakipy_data.data[plane_index] * -1.0, cl, - extent=self.extent, - cmap=autumn, - ).data + ) ) - # print("Value of checkbox",checkbox_group.active) - def exit_edit_peaks(self, event): sys.exit() From 2a6cb33c9c9727fa71852bfccd6b6cf884809176 Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Sun, 28 Jan 2024 23:29:06 -0500 Subject: [PATCH 03/37] removed bug in which pdf.savefig() call breaks interactive 3D plot --- peakipy/cli/main.py | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/peakipy/cli/main.py b/peakipy/cli/main.py index 09d0dbaf..590f7efc 100644 --- a/peakipy/cli/main.py +++ b/peakipy/cli/main.py @@ -37,7 +37,7 @@ import matplotlib import matplotlib.pyplot as plt -from mpl_toolkits.mplot3d import Axes3D +from mpl_toolkits.mplot3d import axes3d from matplotlib import cm from matplotlib.backends.backend_pdf import PdfPages from matplotlib.widgets import Button @@ -1044,8 +1044,8 @@ def check( plt = matplotlib.pyplot fig = plt.figure(figsize=(10, 6)) - ax = fig.add_subplot(111, projection="3d") - # slice out plot area + ax = fig.add_subplot(projection="3d") + ## slice out plot area x_plot = pseudo3D.uc_f2.ppm(X[min_y:max_y, min_x:max_x]) y_plot = pseudo3D.uc_f1.ppm(Y[min_y:max_y, min_x:max_x]) masked_data = masked_data[min_y:max_y, min_x:max_x] @@ -1066,7 +1066,8 @@ def check( ) ) plt.close() - # print(Fore.RED + "Maybe your F1/F2 radii for fitting were too small...") + + #print(Fore.RED + "Maybe your F1/F2 radii for fitting were too small...") elif masked_data.shape[0] == 0 or masked_data.shape[1] == 0: print( f"[red]Nothing to plot for cluster {int(plane.clustid)}[/red]" @@ -1125,7 +1126,6 @@ def check( ) for c, z_single in zip(single_colors, sim_data_singles) ] - ax.plot_wireframe( x_plot, y_plot, @@ -1184,10 +1184,8 @@ def check( ) ax.legend() - pdf.savefig() if show: - def exit_program(event): exit() @@ -1204,6 +1202,9 @@ def next_plot(event): plt.show(windowTitle="", size=(1000, 500)) else: plt.show() + else: + pdf.savefig() + plt.close() From e27cb4b26db46289df3247d792b72c19bc5b237d Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Sun, 28 Jan 2024 23:29:38 -0500 Subject: [PATCH 04/37] updates --- poetry.lock | 1366 +++++++++++++++++++++++------------------------- pyproject.toml | 4 +- 2 files changed, 661 insertions(+), 709 deletions(-) diff --git a/poetry.lock b/poetry.lock index c50b296a..98af3b22 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,15 +1,4 @@ -# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. - 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{file = "wcwidth-0.2.8-py2.py3-none-any.whl", hash = "sha256:77f719e01648ed600dfa5402c347481c0992263b81a027344f3e1ba25493a704"}, - {file = "wcwidth-0.2.8.tar.gz", hash = "sha256:8705c569999ffbb4f6a87c6d1b80f324bd6db952f5eb0b95bc07517f4c1813d4"}, + {file = "wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859"}, + {file = "wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"}, ] [[package]] name = "xyzservices" -version = "2023.7.0" +version = "2023.10.1" description = "Source of XYZ tiles providers" optional = false python-versions = ">=3.8" files = [ - {file = "xyzservices-2023.7.0-py3-none-any.whl", hash = "sha256:88e9cbf22b31a2f9c1b242e2b18690f5c705f0e539c9bfd37a10399e1037731b"}, - {file = "xyzservices-2023.7.0.tar.gz", hash = "sha256:0ec928742227d6f5d4367ea7b457fcfed943429f4de2949b5b02a82cdf5569d6"}, + {file = "xyzservices-2023.10.1-py3-none-any.whl", hash = "sha256:6a4c38d3a9f89d3e77153eff9414b36a8ee0850c9e8b85796fd1b2a85b8dfd68"}, + {file = "xyzservices-2023.10.1.tar.gz", hash = "sha256:091229269043bc8258042edbedad4fcb44684b0473ede027b5672ad40dc9fa02"}, ] [metadata] lock-version = "2.0" python-versions = ">=3.10,<3.13" -content-hash = "d141a9b72da94c48b398cca5e29f9ebff8971418ce8faf06a92856897e3575da" +content-hash = "34645291ffe1814c1c60ed845fda663bedfdea83baa6abec223b5b6f3d39031f" diff --git a/pyproject.toml b/pyproject.toml index 938d56ee..8ef1013e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -24,13 +24,13 @@ license="GNU GPLv3" python = ">=3.10,<3.13" pandas = "^2.1.0" numpy = "^1.25.2" -matplotlib = "^3.7.2" +matplotlib = "^3.8.2" PyYAML = "^6.0.1" nmrglue = "^0.9" scipy = "^1.11.2" lmfit = "^1.2.2" scikit-image = "^0.21.0" -bokeh = "^3.2.2" +bokeh = "^3.3.4" numdifftools = "^0.9.39" Jinja2 = "^3.1.2" jedi = "^0.19.0" From 577ee5046d01e276f75752720884c672d82d6797 Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Mon, 29 Jan 2024 22:40:37 -0500 Subject: [PATCH 05/37] changed styles --- peakipy/cli/edit.py | 43 +++++++++++++++++++++++++++++++++++-------- 1 file changed, 35 insertions(+), 8 deletions(-) diff --git a/peakipy/cli/edit.py b/peakipy/cli/edit.py index 70b0f0bd..977b456f 100644 --- a/peakipy/cli/edit.py +++ b/peakipy/cli/edit.py @@ -18,9 +18,10 @@ from skimage.filters import threshold_otsu from rich import print +from bokeh.io import curdoc from bokeh.events import ButtonClick, DoubleTap from bokeh.layouts import row, column -from bokeh.models import ColumnDataSource, Tabs, TabPanel +from bokeh.models import ColumnDataSource, Tabs, TabPanel, InlineStyleSheet from bokeh.models.tools import HoverTool from bokeh.models.widgets import ( Slider, @@ -39,7 +40,7 @@ ) from bokeh.plotting import figure from bokeh.plotting.contour import contour_data -from bokeh.palettes import PuBuGn9, Category20, Viridis256, RdGy11, Reds256 +from bokeh.palettes import PuBuGn9, Category20, Viridis256, RdGy11, Reds256, YlOrRd9 from peakipy.core import LoadData, read_config, StrucEl @@ -81,6 +82,7 @@ def init(self, doc): ) ) doc.title = "peakipy: Edit Fits" + doc.theme = "dark_minimal" @property def args(self): @@ -200,7 +202,7 @@ def setup_plot(self): self.y_ppm_mesh, self.peakipy_data.data[0], cl, - fill_color=Viridis256, + fill_color=YlOrRd9, line_color="black", ) self.negative_contour_renderer = self.p.contour( @@ -271,8 +273,8 @@ def setup_plot(self): self.p.line( spec_border_x, spec_border_y, - line_width=1, - line_color="black", + line_width=2, + line_color="red", line_dash="dotted", line_alpha=0.5, ) @@ -361,7 +363,7 @@ def setup_plot(self): self.fit_button.on_event(ButtonClick, self.fit_selected) columns = [ - TableColumn(field="ASS", title="Assignment"), + TableColumn(field="ASS", title="Assignment", width=500), TableColumn(field="CLUSTID", title="Cluster", editor=IntEditor()), TableColumn( field="X_PPM", @@ -405,14 +407,39 @@ def setup_plot(self): TableColumn( field="include", title="Include", + width=7, editor=SelectEditor(options=["yes", "no"]), ), TableColumn(field="MEMCNT", title="MEMCNT", editor=IntEditor()), ] self.data_table = DataTable( - source=self.source, columns=columns, editable=True, fit_columns=True - ) + source=self.source, columns=columns, editable=True, width=800, + ) + self.table_style = InlineStyleSheet( + css=""" + .slick-header-columns { + background-color: #2B303A !important; + font-family: arial; + font-weight: bold; + font-size: 12pt; + color: #FFFFFF; + text-align: right; + } + .slick-row { + font-size: 12pt; + font-family: arial; + text-align: left; + } + .slick-row:hover{ + background: none repeat scroll 0 0 #7c7c7c; + } + + + """ + ) + + self.data_table.stylesheets = [self.table_style] # callback for adding # source.selected.on_change('indices', callback) From 94b70f4f59fa9136cead42baff15facf86b7dcb1 Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Mon, 29 Jan 2024 22:42:04 -0500 Subject: [PATCH 06/37] black --- peakipy/cli/edit_fits_app/main.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/peakipy/cli/edit_fits_app/main.py b/peakipy/cli/edit_fits_app/main.py index b0749a26..de0d67fe 100755 --- a/peakipy/cli/edit_fits_app/main.py +++ b/peakipy/cli/edit_fits_app/main.py @@ -144,7 +144,6 @@ def clusters( def recluster_peaks(event): - struc_size = tuple([int(i) for i in struct_el_size.value.split(",")]) print(struc_size) @@ -164,7 +163,6 @@ def recluster_peaks(event): def update_memcnt(df): - for ind, group in df.groupby("CLUSTID"): df.loc[group.index, "MEMCNT"] = len(group) @@ -179,7 +177,6 @@ def update_memcnt(df): def fit_selected(event): - selectionIndex = source.selected.indices current = df.iloc[selectionIndex] @@ -234,7 +231,6 @@ def peak_pick_callback(event): def slider_callback(attrname, old, new): - selectionIndex = source.selected.indices current = df.iloc[selectionIndex] From 2ea36321535cd1e5c6109cd2b9e669b4093f40c2 Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Mon, 29 Jan 2024 22:44:58 -0500 Subject: [PATCH 07/37] added --reference-plane-index for selecting planes for initial lineshape fitting, fixed threshold to work with negative data --- peakipy/cli/fit.py | 6 ++++-- peakipy/cli/main.py | 39 +++++++++++++++++++++++++++++---------- peakipy/core.py | 25 ++++++++++++++++++++++++- 3 files changed, 57 insertions(+), 13 deletions(-) diff --git a/peakipy/cli/fit.py b/peakipy/cli/fit.py index 36baa26b..86544149 100644 --- a/peakipy/cli/fit.py +++ b/peakipy/cli/fit.py @@ -95,14 +95,14 @@ def __init__( data: np.array, config: dict, plane_numbers: list, - planes_for_initial_fit: Optional[List[int]] = None, + reference_planes_for_initial_fit: List[int] = [], use_only_planes_above_threshold: Optional[float] = None, ): self._data = data self._args = args self._config = config self._plane_numbers = plane_numbers - self._planes_for_initial_fit = planes_for_initial_fit + self._planes_for_initial_fit = reference_planes_for_initial_fit self._use_only_planes_above_threshold = use_only_planes_above_threshold def check_integer_list(self): @@ -199,6 +199,7 @@ def fit_peaks(peaks: pd.DataFrame, fit_input: FitPeaksInput) -> FitPeaksResult: noise = fit_input.args.get("noise") verb = fit_input.args.get("verb") initial_fit_threshold = fit_input.args.get("initial_fit_threshold") + reference_plane_indices = fit_input.args.get("reference_plane_indices") lineshape = fit_input.args.get("lineshape") xy_bounds = fit_input.args.get("xy_bounds") vclist = fit_input.args.get("vclist") @@ -281,6 +282,7 @@ def fit_peaks(peaks: pd.DataFrame, fit_input: FitPeaksInput) -> FitPeaksResult: verbose=verb, noise=noise, fit_method=fit_input.config.get("fit_method", "leastsq"), + reference_plane_indices=reference_plane_indices, threshold=initial_fit_threshold, ) fit_result.plot( diff --git a/peakipy/cli/main.py b/peakipy/cli/main.py index 590f7efc..26bd3e07 100644 --- a/peakipy/cli/main.py +++ b/peakipy/cli/main.py @@ -102,15 +102,18 @@ y_radius_ppm_help = "Y radius in ppm of the elliptical fitting mask for each peak" dims_help = "Dimension order of your data" + @app.command(help="Read NMRPipe/Analysis peaklist into pandas dataframe") def read( peaklist_path: Annotated[Path, typer.Argument(help=peaklist_path_help)], data_path: Annotated[Path, typer.Argument(help=data_path_help)], - peaklist_format: Annotated[PeaklistFormat, typer.Argument(help=peaklist_format_help)], - thres: Annotated[Optional[float],typer.Option(help=thres_help)] = None, + peaklist_format: Annotated[ + PeaklistFormat, typer.Argument(help=peaklist_format_help) + ], + thres: Annotated[Optional[float], typer.Option(help=thres_help)] = None, struc_el: StrucEl = StrucEl.disk, struc_size: Tuple[int, int] = (3, None), # Tuple[int, Optional[int]] = (3, None), - x_radius_ppm: Annotated[float,typer.Option(help=x_radius_ppm_help)] = 0.04, + x_radius_ppm: Annotated[float, typer.Option(help=x_radius_ppm_help)] = 0.04, y_radius_ppm: Annotated[float, typer.Option(help=y_radius_ppm_help)] = 0.4, x_ppm_column_name: str = "Position F1", y_ppm_column_name: str = "Position F2", @@ -352,13 +355,20 @@ def read( out.write(yaml) os.system("peakipy spec show_clusters.yml") - print(f"""[green] + print( + f"""[green] ✨✨ Finished reading and clustering peaks! ✨✨ Use {outname} to run peakipy edit or fit.[/green] - """) + """ + ) + + +reference_plane_index_help = ( + "Select planes to use for initial estimation of lineshape parameters" +) @app.command(help="Fit NMR data to lineshape models and deconvolute overlapping peaks") @@ -373,6 +383,9 @@ def fit( vclist: Optional[Path] = None, plane: Optional[List[int]] = None, exclude_plane: Optional[List[int]] = None, + reference_plane_index: Annotated[ + List[int], typer.Option(help=reference_plane_index_help) + ] = [], initial_fit_threshold: Optional[float] = None, mp: bool = True, plot: Optional[Path] = None, @@ -477,6 +490,7 @@ def fit( args["show"] = show args["mp"] = mp args["initial_fit_threshold"] = initial_fit_threshold + args["reference_plane_indices"] = reference_plane_index # read vclist if vclist is None: @@ -584,12 +598,17 @@ def fit( nclusters = peakipy_data.df.CLUSTID.nunique() npeaks = peakipy_data.df.shape[0] if (nclusters >= n_cpu) and mp: - print(f"[green]Using multiprocessing to fit {npeaks} peaks in {nclusters} clusters [/green]"+"\n") + print( + f"[green]Using multiprocessing to fit {npeaks} peaks in {nclusters} clusters [/green]" + + "\n" + ) # split peak lists # tmp_dir = split_peaklist(peakipy_data.df, n_cpu) fit_peaks_args = FitPeaksInput(args, peakipy_data.data, config, plane_numbers) with Pool(processes=n_cpu) as pool, tqdm( - total=len(peakipy_data.df.CLUSTID.unique()), ascii="▱▰",colour="green", + total=len(peakipy_data.df.CLUSTID.unique()), + ascii="▱▰", + colour="green", ) as pbar: # result = pool.map(fit_peaks, peaklists) # result = pool.starmap(fit_peaks, zip(peaklists, args_list)) @@ -1066,8 +1085,8 @@ def check( ) ) plt.close() - - #print(Fore.RED + "Maybe your F1/F2 radii for fitting were too small...") + + # print(Fore.RED + "Maybe your F1/F2 radii for fitting were too small...") elif masked_data.shape[0] == 0 or masked_data.shape[1] == 0: print( f"[red]Nothing to plot for cluster {int(plane.clustid)}[/red]" @@ -1186,6 +1205,7 @@ def check( ax.legend() if show: + def exit_program(event): exit() @@ -1204,7 +1224,6 @@ def next_plot(event): plt.show() else: pdf.savefig() - plt.close() diff --git a/peakipy/core.py b/peakipy/core.py index 5a8b2dce..1b43a94d 100644 --- a/peakipy/core.py +++ b/peakipy/core.py @@ -754,11 +754,32 @@ def make_mask_from_peak_cluster(group, data): return mask, peak +def select_reference_planes_using_indices(data, indices: List[int]): + n_planes = data.shape[0] + if indices == []: + return data + + max_index = max(indices) + min_index = min(indices) + + if max_index >= n_planes: + raise IndexError( + f"Your data has {n_planes}. You selected plane {max_index} (allowed indices between 0 and {n_planes-1})" + ) + elif min_index < (-1 * n_planes): + raise IndexError( + f"Your data has {n_planes}. You selected plane {min_index} (allowed indices between -{n_planes} and {n_planes-1})" + ) + else: + data = data[indices] + return data + + def select_planes_above_threshold_from_masked_data(data, threshold=None): if threshold == None: data = data else: - data = data[data.max(axis=1) > threshold] + data = data[np.abs(data).max(axis=1) > threshold] return data @@ -772,6 +793,7 @@ def fit_first_plane( log=None, noise=1.0, fit_method="leastsq", + reference_plane_indices: List[int] = [], threshold: Optional[float] = None, ): """Deconvolute group of peaks @@ -868,6 +890,7 @@ def fit_first_plane( # print("DATA",data.shape, mask.shape) peak_slices = np.array([d[mask] for d in data]) # print("Peak slices", peak_slices.shape) + peak_slices = select_reference_planes_using_indices(peak_slices, reference_plane_indices) peak_slices = select_planes_above_threshold_from_masked_data(peak_slices, threshold) # print(peak_slices.shape) peak_slices = peak_slices.sum(axis=0) From ac42030c48ffe75e1348a27a7c9b01f301845721 Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Thu, 1 Feb 2024 22:40:04 -0500 Subject: [PATCH 08/37] added all peakipy fit options to bokeh interface --- peakipy/cli/edit.py | 210 +++++++++++++++++++++++++++++++------------- peakipy/cli/main.py | 106 ++++++++++++---------- peakipy/core.py | 4 +- test/test_cli.py | 5 +- 4 files changed, 212 insertions(+), 113 deletions(-) diff --git a/peakipy/cli/edit.py b/peakipy/cli/edit.py index 977b456f..bb45c5eb 100644 --- a/peakipy/cli/edit.py +++ b/peakipy/cli/edit.py @@ -5,22 +5,18 @@ import sys import shutil -from pathlib import Path from subprocess import check_output from pathlib import Path -import nmrglue as ng import numpy as np -import matplotlib.pyplot as plt import pandas as pd -from matplotlib.cm import magma, autumn, viridis from skimage.filters import threshold_otsu from rich import print from bokeh.io import curdoc from bokeh.events import ButtonClick, DoubleTap -from bokeh.layouts import row, column +from bokeh.layouts import row, column, grid from bokeh.models import ColumnDataSource, Tabs, TabPanel, InlineStyleSheet from bokeh.models.tools import HoverTool from bokeh.models.widgets import ( @@ -64,10 +60,13 @@ def __init__(self, peaklist_path: Path, data_path: Path): self.peakipy_data.check_data_frame() # make temporary paths self.make_temp_files() - self.make_data_source() self.setup_radii_sliders() self.setup_save_buttons() + self.setup_set_fixed_parameters() + self.setup_xybounds() + self.setup_set_reference_planes() + self.setup_initial_fit_threshold() self.setup_quit_button() self.setup_plot() @@ -151,6 +150,78 @@ def setup_save_buttons(self): self.button = Button(label="Save", button_type="success") self.button.on_event(ButtonClick, self.save_peaks) + def setup_set_fixed_parameters(self): + self.select_fixed_parameters_help = Div( + text="Select parameters to fix after initial lineshape parameters have been fitted" + ) + self.select_fixed_parameters = TextInput( + value="fraction sigma center", width=200 + ) + + def setup_xybounds(self): + self.set_xybounds_help = Div( + text="If floating the peak centers you can bound the fits in the x and y dimensions. Units of ppm." + ) + self.set_xybounds = TextInput(placeholder="e.g. 0.01 0.1") + + def get_xybounds(self): + try: + x_bound, y_bound = self.set_xybounds.value.split(" ") + x_bound = float(x_bound) + y_bound = float(y_bound) + xy_bounds = x_bound, y_bound + except: + xy_bounds = None, None + return xy_bounds + + def make_xybound_command(self, x_bound, y_bound): + if (x_bound != None) and (y_bound != None): + xy_bounds_command = f" --xy-bounds {x_bound} {y_bound}" + else: + xy_bounds_command = "" + return xy_bounds_command + + def setup_set_reference_planes(self): + self.select_reference_planes_help = Div( + text="Select reference planes (index starts at 0)" + ) + self.select_reference_planes = TextInput(placeholder="0 1 2 3") + + def get_reference_planes(self): + if self.select_reference_planes.value: + print("You have selected1") + return self.select_reference_planes.value.split(" ") + else: + return [] + + def make_reference_planes_command(self, reference_plane_list): + reference_plane_command = "" + for plane in reference_plane_list: + reference_plane_command += f" --reference-plane-index {plane}" + return reference_plane_command + + def setup_initial_fit_threshold(self): + self.set_initial_fit_threshold_help = Div( + text="Set an intensity threshold for selection of planes for initial estimation of lineshape parameters" + ) + self.set_initial_fit_threshold = TextInput(placeholder="e.g. 1e7") + + def get_initial_fit_threshold(self): + try: + initial_fit_threshold = float(self.set_initial_fit_threshold.value) + except ValueError: + initial_fit_threshold = None + return initial_fit_threshold + + def make_initial_fit_threshold_command(self, initial_fit_threshold): + if initial_fit_threshold is not None: + initial_fit_threshold_command = ( + f" --initial-fit-threshold {initial_fit_threshold}" + ) + else: + initial_fit_threshold_command = "" + return initial_fit_threshold_command + def setup_quit_button(self): # Quit button self.exit_button = Button(label="Quit", button_type="warning") @@ -204,6 +275,7 @@ def setup_plot(self): cl, fill_color=YlOrRd9, line_color="black", + line_width=0.25, ) self.negative_contour_renderer = self.p.contour( self.x_ppm_mesh, @@ -212,6 +284,7 @@ def setup_plot(self): cl, fill_color=Reds256, line_color="black", + line_width=0.25, ) self.contour_start = TextInput( @@ -313,12 +386,12 @@ def setup_plot(self): # 6: "PV_G", # 7: "G_L", } - self.radio_button_group = RadioButtonGroup( + self.select_lineshape_radiobuttons = RadioButtonGroup( labels=[self.lineshapes[i] for i in self.lineshapes.keys()], active=0 ) - self.ls_div = Div( + self.select_lineshape_radiobuttons_help = Div( text="""Choose lineshape you wish to fit. This can be Voigt (V), pseudo-Voigt (PV), Gaussian (G), Lorentzian (L). - PV_PV fits a PV lineshape with independent "fraction" parameters for the direct and indirect dimensions""" + PV_PV fits a PV lineshape with independent "fraction" parameters for the direct and indirect dimensions""", ) self.clust_div = Div( text="""If you want to adjust how the peaks are automatically clustered then try changing the @@ -414,7 +487,10 @@ def setup_plot(self): ] self.data_table = DataTable( - source=self.source, columns=columns, editable=True, width=800, + source=self.source, + columns=columns, + editable=True, + width=800, ) self.table_style = InlineStyleSheet( css=""" @@ -445,20 +521,48 @@ def setup_plot(self): # source.selected.on_change('indices', callback) self.source.selected.on_change("indices", self.select_callback) - # Document layout - fitting_controls = column( - row( - column(self.slider_X_RADIUS, self.slider_Y_RADIUS), - column( - row(column(self.contour_start, self.pos_neg_contour_radiobutton)), - column(self.fit_button), - ), + # # Document layout + # fitting_controls = column( + # row( + # column(self.slider_X_RADIUS, self.slider_Y_RADIUS), + # column( + # row(column(self.contour_start, self.pos_neg_contour_radiobutton)), + # column(self.fit_button), + # ), + # ), + # row( + # column(column(self.select_lineshape_radiobuttons_help), column(self.select_lineshape_radiobuttons)), + # column(column(self.select_plane), column(self.checkbox_group)), + # column(self.select_fixed_parameters_help, self.select_fixed_parameters), + # column(self.select_reference_planes) + # ), + # max_width=400, + # ) + fitting_controls = row( + column( + self.slider_X_RADIUS, + self.slider_Y_RADIUS, + self.contour_start, + self.pos_neg_contour_radiobutton, + self.select_lineshape_radiobuttons_help, + self.select_lineshape_radiobuttons, + max_width=400, ), - row( - column(column(self.ls_div), column(self.radio_button_group)), - column(column(self.select_plane), column(self.checkbox_group)), + column( + self.select_plane, + self.checkbox_group, + self.select_fixed_parameters_help, + self.select_fixed_parameters, + self.set_xybounds_help, + self.set_xybounds, + self.select_reference_planes_help, + self.select_reference_planes, + self.set_initial_fit_threshold_help, + self.set_initial_fit_threshold, + self.fit_button, + max_width=400, ), - max_width=400, + max_width=800, ) # reclustering tab @@ -545,6 +649,15 @@ def update_memcnt(self): self.source.data = ColumnDataSource.from_df(self.peakipy_data.df) return self.peakipy_data.df + def unpack_parameters_to_fix(self): + return self.select_fixed_parameters.value.strip().split(" ") + + def make_fix_command_from_parameters(self, parameters): + command = "" + for parameter in parameters: + command += f" --fix {parameter}" + return command + def fit_selected(self, event): selectionIndex = self.source.selected.indices current = self.peakipy_data.df.iloc[selectionIndex] @@ -568,16 +681,26 @@ def fit_selected(self, event): ] selected_df.to_csv(self.TEMP_INPUT_CSV) + fix_command = self.make_fix_command_from_parameters( + self.unpack_parameters_to_fix() + ) + xy_bounds_command = self.make_xybound_command(*self.get_xybounds()) + reference_planes_command = self.make_reference_planes_command( + self.get_reference_planes() + ) + initial_fit_threshold_command = self.make_initial_fit_threshold_command( + self.get_initial_fit_threshold() + ) - lineshape = self.lineshapes[self.radio_button_group.active] + lineshape = self.lineshapes[self.select_lineshape_radiobuttons.active] print(f"[yellow]Using LS = {lineshape}[/yellow]") if self.checkbox_group.active == []: - fit_command = f"peakipy fit {self.TEMP_INPUT_CSV} {self.data_path} {self.TEMP_OUT_CSV} --lineshape {lineshape}" + fit_command = f"peakipy fit {self.TEMP_INPUT_CSV} {self.data_path} {self.TEMP_OUT_CSV} --lineshape {lineshape}{fix_command}{reference_planes_command}{initial_fit_threshold_command}{xy_bounds_command}" plot_command = f"peakipy check {self.TEMP_OUT_CSV} {self.data_path} --label --individual --show --outname {self.TEMP_OUT_PLOT / Path('tmp.pdf')}" else: plane_index = self.select_plane.value print(f"[yellow]Only fitting plane {plane_index}[/yellow]") - fit_command = f"peakipy fit {self.TEMP_INPUT_CSV} {self.data_path} {self.TEMP_OUT_CSV} --lineshape {lineshape} --plane {plane_index}" + fit_command = f"peakipy fit {self.TEMP_INPUT_CSV} {self.data_path} {self.TEMP_OUT_CSV} --lineshape {lineshape} --plane {plane_index}{fix_command}{reference_planes_command}{initial_fit_threshold_command}{xy_bounds_command}" plot_command = f"peakipy check {self.TEMP_OUT_CSV} {self.data_path} --label --individual --show --outname {self.TEMP_OUT_PLOT / Path('tmp.pdf')} --plane {plane_index}" print(f"[blue]{fit_command}[/blue]") @@ -819,40 +942,3 @@ def update_contour(self, attrname, old, new): def exit_edit_peaks(self, event): sys.exit() - - -def get_contour_data(data, levels, **kwargs): - cs = plt.contour(data, levels, **kwargs) - xs = [] - ys = [] - xt = [] - yt = [] - col = [] - text = [] - isolevelid = 0 - for isolevel in cs.collections: - isocol = isolevel.get_edgecolor()[0] - thecol = 3 * [None] - theiso = str(cs.get_array()[isolevelid]) - isolevelid += 1 - for i in range(3): - thecol[i] = int(255 * isocol[i]) - thecol = "#%02x%02x%02x" % (thecol[0], thecol[1], thecol[2]) - - for path in isolevel.get_paths(): - v = path.vertices - x = v[:, 0] - y = v[:, 1] - xs.append(x.tolist()) - ys.append(y.tolist()) - indx = int(len(x) / 2) - indy = int(len(y) / 2) - xt.append(x[indx]) - yt.append(y[indy]) - text.append(theiso) - col.append(thecol) - - source = ColumnDataSource( - data={"xs": xs, "ys": ys, "line_color": col, "xt": xt, "yt": yt, "text": text} - ) - return source diff --git a/peakipy/cli/main.py b/peakipy/cli/main.py index 26bd3e07..e5272532 100644 --- a/peakipy/cli/main.py +++ b/peakipy/cli/main.py @@ -801,6 +801,57 @@ def fit( run_log() +def validate_plane_selection(plane, pseudo3D): + if plane > pseudo3D.n_planes: + raise ValueError( + f"[red]There are {pseudo3D.n_planes} planes in your data you selected --plane {plane}...[red]" + f"plane numbering starts from 0." + ) + elif plane < 0: + raise ValueError( + f"[red]Plane number can not be negative; you selected --plane {plane}...[/red]" + ) + else: + return plane + + +def validate_ccount(ccount): + if type(ccount) == int: + ccount = ccount + else: + raise TypeError("ccount should be an integer") + return ccount + + +def validate_rcount(rcount): + if type(rcount) == int: + rcount = rcount + else: + raise TypeError("rcount should be an integer") + return rcount + + +def unpack_plotting_colors(colors): + match colors: + case (data_color, fit_color): + data_color, fit_color = colors + case _: + data_color, fit_color = "green", "blue" + return data_color, fit_color + + +def get_fit_data_for_selected_peak_clusters(fits, clusters): + match clusters: + case None | []: + pass + case _: + # only use these clusters + fits = fits[fits.clustid.isin(clusters)] + if len(fits) < 1: + exit(f"Are you sure clusters {clusters} exist?") + return fits + + @app.command(help="Interactive plots for checking fits") def check( fits: Path, @@ -830,7 +881,7 @@ def check( e.g. clusters=[2,4,6,7] plane : int Plot selected plane [default: 0] - e.g. plane=2 will plot second plane only + e.g. --plane 2 will plot second plane only outname : Path Plot name [default: Path("plots.pdf")] first : bool @@ -887,52 +938,11 @@ def check( else: plane = plane - if plane > pseudo3D.n_planes: - raise ValueError( - f"[red]There are {pseudo3D.n_planes} planes in your data you selected --plane {plane}...[red]" - f"plane numbering starts from 0." - ) - elif plane < 0: - raise ValueError( - f"[red]Plane number can not be negative; you selected --plane {plane}...[/red]" - ) - # in case first plane is chosen - elif plane == 0: - selected_plane = plane - # plane numbers start from 1 so adjust for indexing - else: - selected_plane = plane - # fits = fits[fits["plane"] == plane] - # print(fits) - - if type(ccount) == int: - ccount = ccount - else: - raise TypeError("ccount should be an integer") - - if type(rcount) == int: - rcount = rcount - else: - raise TypeError("rcount should be an integer") - - match colors: - case (data_color, fit_color): - data_color, fit_color = colors - case _: - data_color, fit_color = "green", "blue" - - # raise TypeError( - # "colors should be valid pair for matplotlib. i.e. g,b or green,blue" - # ) - - match clusters: - case None | []: - pass - case _: - # only use these clusters - fits = fits[fits.clustid.isin(clusters)] - if len(fits) < 1: - exit(f"Are you sure clusters {clusters} exist?") + selected_plane = validate_plane_selection(plane, pseudo3D) + ccount = validate_ccount(ccount) + rcount = validate_rcount(rcount) + data_color, fit_color = unpack_plotting_colors(colors) + fits = get_fit_data_for_selected_peak_clusters(fits, clusters) groups = fits.groupby("clustid") @@ -943,7 +953,7 @@ def check( X, Y = XY with PdfPages(outname) as pdf: - for name, group in groups: + for _, group in groups: table = df_to_rich_table( group, title="", diff --git a/peakipy/core.py b/peakipy/core.py index 1b43a94d..d404db3d 100644 --- a/peakipy/core.py +++ b/peakipy/core.py @@ -890,7 +890,9 @@ def fit_first_plane( # print("DATA",data.shape, mask.shape) peak_slices = np.array([d[mask] for d in data]) # print("Peak slices", peak_slices.shape) - peak_slices = select_reference_planes_using_indices(peak_slices, reference_plane_indices) + peak_slices = select_reference_planes_using_indices( + peak_slices, reference_plane_indices + ) peak_slices = select_planes_above_threshold_from_masked_data(peak_slices, threshold) # print(peak_slices.shape) peak_slices = peak_slices.sum(axis=0) diff --git a/test/test_cli.py b/test/test_cli.py index 8badf4ca..a6a4e653 100644 --- a/test/test_cli.py +++ b/test/test_cli.py @@ -132,10 +132,11 @@ def test_check_main_with_voigt(protein_L): ) peakipy.cli.main.check(**args) + def test_edit_with_default(protein_L): args = dict( - peaklist_path=protein_L/Path("peaks.csv"), - data_path=protein_L/Path("test1.ft2"), + peaklist_path=protein_L / Path("peaks.csv"), + data_path=protein_L / Path("test1.ft2"), test=True, ) peakipy.cli.main.edit(**args) From 0c022f9f1db267f695f76a501f5609b1259f4286 Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Sat, 10 Feb 2024 22:25:09 -0500 Subject: [PATCH 09/37] some refactoring, added some tests, updated bokeh app --- .pre-commit-config.yaml | 14 +++ ccpn_macros/selected_to_peakipy.py | 2 +- peakipy/cli/edit.py | 51 +++++--- peakipy/core.py | 188 ++++++++++++++++------------- poetry.lock | 122 ++++++++++++++++++- pyproject.toml | 1 + test/test_core.py | 87 ++++++++++++- 7 files changed, 358 insertions(+), 107 deletions(-) create mode 100644 .pre-commit-config.yaml diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 00000000..d4585edb --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,14 @@ +# See https://pre-commit.com for more information +# See https://pre-commit.com/hooks.html for more hooks +repos: +- repo: https://github.com/pre-commit/pre-commit-hooks + rev: v3.2.0 + hooks: + - id: trailing-whitespace + - id: end-of-file-fixer + - id: check-yaml + - id: check-added-large-files +- repo: https://github.com/psf/black + rev: 24.1.1 + hooks: + - id: black diff --git a/ccpn_macros/selected_to_peakipy.py b/ccpn_macros/selected_to_peakipy.py index b88e317c..1fb2ff0e 100644 --- a/ccpn_macros/selected_to_peakipy.py +++ b/ccpn_macros/selected_to_peakipy.py @@ -1,4 +1,5 @@ """ Export selected peaks to TSV file """ + import os from pathlib import Path import pandas as pd @@ -84,7 +85,6 @@ def peakipy_check(): if __name__ == "__main__": - peaks = current.peaks df = PeaksToDataFrame(peaks) column_order = [ diff --git a/peakipy/cli/edit.py b/peakipy/cli/edit.py index bb45c5eb..6f5a5859 100644 --- a/peakipy/cli/edit.py +++ b/peakipy/cli/edit.py @@ -401,12 +401,12 @@ def setup_plot(self): any manual edits will be lost.""" ) self.intro_div = Div( - text="""

peakipy - interactive fit adjustment

+ text="""

peakipy - interactive fit adjustment

""" ) self.doc_link = Div( - text="

ℹ️ click here for documentation

" + text="

ℹ️ click here for documentation

" ) self.fit_reports = "" self.fit_reports_div = Div(text="", height=400, styles={"overflow": "scroll"}) @@ -490,18 +490,21 @@ def setup_plot(self): source=self.source, columns=columns, editable=True, - width=800, + width=1200, ) self.table_style = InlineStyleSheet( css=""" .slick-header-columns { - background-color: #2B303A !important; + background-color: #00296b !important; font-family: arial; font-weight: bold; font-size: 12pt; color: #FFFFFF; text-align: right; } + .slick-header-column:hover { + background: none repeat scroll 0 0 #fdc500; + } .slick-row { font-size: 12pt; font-family: arial; @@ -510,8 +513,22 @@ def setup_plot(self): .slick-row:hover{ background: none repeat scroll 0 0 #7c7c7c; } + .slick-cell { + header-font-weight: 500; + border-width: 1px 1px 1px 1px; + border-color: #d4d4d4; + background-color: #00509D; + color: #FFFFFF; + } + .slick-cell.selected { + header-font-weight: 500; + border-width: 1px 1px 1px 1px; + border-color: #00509D; + background-color: #FDC500; + color: black; + } + - """ ) @@ -662,12 +679,12 @@ def fit_selected(self, event): selectionIndex = self.source.selected.indices current = self.peakipy_data.df.iloc[selectionIndex] - self.peakipy_data.df.loc[ - selectionIndex, "X_RADIUS_PPM" - ] = self.slider_X_RADIUS.value - self.peakipy_data.df.loc[ - selectionIndex, "Y_RADIUS_PPM" - ] = self.slider_Y_RADIUS.value + self.peakipy_data.df.loc[selectionIndex, "X_RADIUS_PPM"] = ( + self.slider_X_RADIUS.value + ) + self.peakipy_data.df.loc[selectionIndex, "Y_RADIUS_PPM"] = ( + self.slider_Y_RADIUS.value + ) self.peakipy_data.df.loc[selectionIndex, "X_DIAMETER_PPM"] = ( current["X_RADIUS_PPM"] * 2.0 @@ -806,9 +823,9 @@ def slider_callback_x(self, attrname, old, new): self.peakipy_data.df.loc[selectionIndex, "X_RADIUS"] = ( self.slider_X_RADIUS.value * self.peakipy_data.pt_per_ppm_f2 ) - self.peakipy_data.df.loc[ - selectionIndex, "X_RADIUS_PPM" - ] = self.slider_X_RADIUS.value + self.peakipy_data.df.loc[selectionIndex, "X_RADIUS_PPM"] = ( + self.slider_X_RADIUS.value + ) self.peakipy_data.df.loc[selectionIndex, "X_DIAMETER_PPM"] = ( current["X_RADIUS_PPM"] * 2.0 @@ -828,9 +845,9 @@ def slider_callback_y(self, attrname, old, new): self.peakipy_data.df.loc[selectionIndex, "Y_RADIUS"] = ( self.slider_Y_RADIUS.value * self.peakipy_data.pt_per_ppm_f1 ) - self.peakipy_data.df.loc[ - selectionIndex, "Y_RADIUS_PPM" - ] = self.slider_Y_RADIUS.value + self.peakipy_data.df.loc[selectionIndex, "Y_RADIUS_PPM"] = ( + self.slider_Y_RADIUS.value + ) self.peakipy_data.df.loc[selectionIndex, "Y_DIAMETER_PPM"] = ( current["Y_RADIUS_PPM"] * 2.0 diff --git a/peakipy/core.py b/peakipy/core.py index d404db3d..114cc854 100644 --- a/peakipy/core.py +++ b/peakipy/core.py @@ -1,4 +1,4 @@ -""" +""" peakipy - deconvolute overlapping NMR peaks Copyright (C) 2019 Jacob Peter Brady @@ -19,7 +19,6 @@ """ - import sys import json from datetime import datetime @@ -91,7 +90,7 @@ class Lineshape(str, Enum): def gaussian(x, center=0.0, sigma=1.0): - """1-dimensional Gaussian function. + r"""1-dimensional Gaussian function. gaussian(x, center, sigma) = (1/(s2pi*sigma)) * exp(-(1.0*x-center)**2 / (2*sigma**2)) @@ -115,7 +114,7 @@ def gaussian(x, center=0.0, sigma=1.0): def lorentzian(x, center=0.0, sigma=1.0): - """1-dimensional Lorentzian function. + r"""1-dimensional Lorentzian function. lorentzian(x, center, sigma) = (1/(1 + ((1.0*x-center)/sigma)**2)) / (pi*sigma) @@ -139,7 +138,7 @@ def lorentzian(x, center=0.0, sigma=1.0): def voigt(x, center=0.0, sigma=1.0, gamma=None): - """Return a 1-dimensional Voigt function. + r"""Return a 1-dimensional Voigt function. voigt(x, center, sigma, gamma) = amplitude*wofz(z).real / (sigma*sqrt(2.0 * π)) @@ -174,7 +173,7 @@ def voigt(x, center=0.0, sigma=1.0, gamma=None): def pseudo_voigt(x, center=0.0, sigma=1.0, fraction=0.5): - """1-dimensional Pseudo-voigt function + r"""1-dimensional Pseudo-voigt function Superposition of Gaussian and Lorentzian function @@ -212,7 +211,7 @@ def pvoigt2d( sigma_y=1.0, fraction=0.5, ): - """2D pseudo-voigt model + r"""2D pseudo-voigt model :math:`(1-fraction) G(x,center,\sigma_{gx}) + (fraction) L(x, center, \sigma_x) * (1-fraction) G(y,center,\sigma_{gy}) + (fraction) L(y, center, \sigma_y)` @@ -241,21 +240,9 @@ def pvoigt2d( :rtype: numpy.array """ - x, y = XY - # sigma_gx = sigma_x / sqrt(2 * log2) - # sigma_gy = sigma_y / sqrt(2 * log2) - # fraction same for both dimensions - # super position of gaussian and lorentzian - # then convoluted for x y - # pv_x = (1 - fraction) * gaussian(x, center_x, sigma_gx) + fraction * lorentzian( - # x, center_x, sigma_x - # ) pv_x = pseudo_voigt(x, center_x, sigma_x, fraction) pv_y = pseudo_voigt(y, center_y, sigma_y, fraction) - # pv_y = (1 - fraction) * gaussian(y, center_y, sigma_gy) + fraction * lorentzian( - # y, center_y, sigma_y - # ) return amplitude * pv_x * pv_y @@ -495,7 +482,7 @@ def make_param_dict(peaks, data, lineshape: Lineshape = Lineshape.PV): param_dict = {} - for index, peak in peaks.iterrows(): + for _, peak in peaks.iterrows(): str_form = lambda x: "%s%s" % (to_prefix(peak.ASS), x) # using exact value of points (i.e decimal) param_dict[str_form("center_x")] = peak.X_AXISf @@ -614,7 +601,7 @@ def make_models( # make model for first peak first_peak, *remaining_peaks = peaks.iterrows() mod = Model(model, prefix="%s" % to_prefix(first_peak[1].ASS)) - for index, peak in remaining_peaks: + for _, peak in remaining_peaks: mod += Model(model, prefix="%s" % to_prefix(peak.ASS)) param_dict = make_param_dict(peaks, data, lineshape=lineshape) @@ -726,7 +713,7 @@ def df_to_rich_table(df, title: str, columns: List[str], styles: str): table = Table(title=title) for col, style in zip(columns, styles): table.add_column(col, style=style) - for ind, row in df.iterrows(): + for _, row in df.iterrows(): row = row[columns].values str_row = [] for i in row: @@ -747,7 +734,7 @@ def df_to_rich_table(df, title: str, columns: List[str], styles: str): def make_mask_from_peak_cluster(group, data): mask = np.zeros(data.shape, dtype=bool) - for index, peak in group.iterrows(): + for _, peak in group.iterrows(): mask += make_mask( data, peak.X_AXISf, peak.Y_AXISf, peak.X_RADIUS, peak.Y_RADIUS ) @@ -776,11 +763,76 @@ def select_reference_planes_using_indices(data, indices: List[int]): def select_planes_above_threshold_from_masked_data(data, threshold=None): + """This function returns planes with data above the threshold. + + It currently uses absolute intensity values. + Negative thresholds just result in return of the orignal data. + + """ if threshold == None: - data = data + selected_data = data else: - data = data[np.abs(data).max(axis=1) > threshold] - return data + selected_data = data[np.abs(data).max(axis=1) > threshold] + + if selected_data.shape[0] == 0: + selected_data = data + + return selected_data + + +def get_lineshape_function(lineshape: Lineshape): + match lineshape: + case lineshape.PV | lineshape.G | lineshape.L: + lineshape_function = pvoigt2d + case lineshape.V: + lineshape_function = voigt2d + case lineshape.PV_PV: + lineshape_function = pv_pv + case lineshape.G_L: + lineshape_function = gaussian_lorentzian + case lineshape.PV_G: + lineshape_function = pv_g + case lineshape.PV_L: + lineshape_function = pv_l + case _: + raise Exception("No lineshape was selected!") + return lineshape_function + + +def slice_peaks_from_data_using_mask(data, mask): + peak_slices = np.array([d[mask] for d in data]) + return peak_slices + + +def get_limits_for_axis_in_points(group_axis_points, mask_radius_in_points): + max_point, min_point = ( + int(np.ceil(max(group_axis_points) + mask_radius_in_points + 1)), + int(np.floor(min(group_axis_points) - mask_radius_in_points)), + ) + return max_point, min_point + + +def deal_with_peaks_on_edge_of_spectrum(data_shape, max_x, min_x, max_y, min_y): + if min_y < 0: + min_y = 0 + + if min_x < 0: + min_x = 0 + + if max_y > data_shape[-2]: + max_y = data_shape[-2] + + if max_x > data_shape[-1]: + max_x = data_shape[-1] + return max_x, min_x, max_y, min_y + + +def make_meshgrid(data_shape): + # must be a better way to make the meshgrid + x = np.arange(data_shape[-1]) + y = np.arange(data_shape[-2]) + XY = np.meshgrid(x, y) + return XY def fit_first_plane( @@ -796,7 +848,7 @@ def fit_first_plane( reference_plane_indices: List[int] = [], threshold: Optional[float] = None, ): - """Deconvolute group of peaks + r"""Deconvolute group of peaks :param group: pandas data from containing group of peaks using groupby("CLUSTID") :type group: pandas.core.groupby.generic.DataFrameGroupBy @@ -835,28 +887,10 @@ def fit_first_plane( :rtype: FitResult """ - match lineshape: - case lineshape.PV | lineshape.G | lineshape.L: - lineshape_function = pvoigt2d - case lineshape.V: - lineshape_function = voigt2d - case lineshape.PV_PV: - lineshape_function = pv_pv - case lineshape.G_L: - lineshape_function = gaussian_lorentzian - case lineshape.PV_G: - lineshape_function = pv_g - case lineshape.PV_L: - lineshape_function = pv_l - case _: - raise Exception("No lineshape was selected!") - + lineshape_function = get_lineshape_function(lineshape) mod, p_guess = make_models( lineshape_function, group, data, lineshape=lineshape, xy_bounds=xy_bounds ) - # get initial peak centers - cen_x = [p_guess[k].value for k in p_guess if "center_x" in k] - cen_y = [p_guess[k].value for k in p_guess if "center_y" in k] first_plane_data = data[0] mask, peak = make_mask_from_peak_cluster(group, first_plane_data) @@ -864,48 +898,28 @@ def fit_first_plane( x_radius = group.X_RADIUS.max() y_radius = group.Y_RADIUS.max() - max_x, min_x = ( - int(np.ceil(max(group.X_AXISf) + x_radius + 1)), - int(np.floor(min(group.X_AXISf) - x_radius)), + max_x, min_x = get_limits_for_axis_in_points( + group_axis_points=group.X_AXISf, mask_radius_in_points=x_radius ) - max_y, min_y = ( - int(np.ceil(max(group.Y_AXISf) + y_radius + 1)), - int(np.floor(min(group.Y_AXISf) - y_radius)), + max_y, min_y = get_limits_for_axis_in_points( + group_axis_points=group.Y_AXISf, mask_radius_in_points=y_radius + ) + max_x, min_x, max_y, min_y = deal_with_peaks_on_edge_of_spectrum( + data.shape, max_x, min_x, max_y, min_y ) - #  deal with peaks on the edge of spectrum - shape = data.shape - if min_y < 0: - min_y = 0 - - if min_x < 0: - min_x = 0 - - if max_y > shape[-2]: - max_y = shape[-2] - - if max_x > shape[-1]: - max_x = shape[-1] - - # print("DATA",data.shape, mask.shape) - peak_slices = np.array([d[mask] for d in data]) - # print("Peak slices", peak_slices.shape) + peak_slices = slice_peaks_from_data_using_mask(data, mask) peak_slices = select_reference_planes_using_indices( peak_slices, reference_plane_indices ) peak_slices = select_planes_above_threshold_from_masked_data(peak_slices, threshold) - # print(peak_slices.shape) peak_slices = peak_slices.sum(axis=0) - # print(peak_slices.shape) - # must be a better way to make the meshgrid - x = np.arange(shape[-1]) - y = np.arange(shape[-2]) - XY = np.meshgrid(x, y) + XY = make_meshgrid(data.shape) X, Y = XY XY_slices = np.array([X.copy()[mask], Y.copy()[mask]]) - # print("XY_slices", XY_slices) + weights = 1.0 / np.array([noise] * len(np.ravel(peak_slices))) out = mod.fit( @@ -1104,9 +1118,9 @@ def plot(self, plot_path=None, show=False, mp=False): z_sim = self.Z_sim[self.min_y : self.max_y, self.min_x : self.max_x] ax.set_title( - "$\chi^2$=" + r"$\chi^2$=" + f"{self.out.chisqr:.3f}, " - + "$\chi_{red}^2$=" + + r"$\chi_{red}^2$=" + f"{self.out.redchi:.4f}" ) @@ -1635,10 +1649,10 @@ def update_df(self): self.df["X_AXISf"] = self.df.X_PPM.apply(lambda x: self.uc_f2.f(x, "ppm")) self.df["Y_AXISf"] = self.df.Y_PPM.apply(lambda x: self.uc_f1.f(x, "ppm")) # in case of missing values (should estimate though) - self.df.XW_HZ.replace("None", "20.0", inplace=True) - self.df.YW_HZ.replace("None", "20.0", inplace=True) - self.df.XW_HZ.replace(np.NaN, "20.0", inplace=True) - self.df.YW_HZ.replace(np.NaN, "20.0", inplace=True) + self.df["XW_HZ"] = self.df.XW_HZ.replace("None", "20.0") + self.df["YW_HZ"] = self.df.YW_HZ.replace("None", "20.0") + self.df["XW_HZ"] = self.df.XW_HZ.replace(np.NaN, "20.0") + self.df["YW_HZ"] = self.df.YW_HZ.replace(np.NaN, "20.0") # convert linewidths to float self.df["XW_HZ"] = self.df.XW_HZ.apply(lambda x: float(x)) self.df["YW_HZ"] = self.df.YW_HZ.apply(lambda x: float(x)) @@ -1713,7 +1727,7 @@ def _read_sparky(self): df = pd.read_csv( self.peaklist_path, skiprows=1, - delim_whitespace=True, + sep=r"\s+", names=["ASS", "Y_PPM", "X_PPM", "VOLUME", "HEIGHT", "YW_HZ", "XW_HZ"], ) df["INDEX"] = df.index @@ -1732,7 +1746,7 @@ def _read_pipe(self): else: to_skip += 1 df = pd.read_csv( - self.peaklist_path, skiprows=to_skip, names=columns, delim_whitespace=True + self.peaklist_path, skiprows=to_skip, names=columns, sep=r"\s+" ) return df @@ -1764,7 +1778,7 @@ def check_assignments(self): textwrap.dedent( """ Creating dummy assignments for duplicates - + """ ) ) @@ -1782,7 +1796,7 @@ def check_peak_bounds(self): f"""[red] ################################################################################# - Excluding the following peaks as they are not within the spectrum which has shape + Excluding the following peaks as they are not within the spectrum which has shape {self.data.shape} [/red]""" @@ -1967,7 +1981,7 @@ def to_fuda(self, fname="params.fuda"): fuda_file = textwrap.dedent( f"""\ - + # Read peaklist and spectrum info PEAKLIST=peaks.fuda SPECFILE={self.data_path} diff --git a/poetry.lock b/poetry.lock index 98af3b22..40676c6e 100644 --- a/poetry.lock +++ b/poetry.lock @@ -114,6 +114,17 @@ files = [ {file = "certifi-2023.11.17.tar.gz", hash = "sha256:9b469f3a900bf28dc19b8cfbf8019bf47f7fdd1a65a1d4ffb98fc14166beb4d1"}, ] +[[package]] +name = "cfgv" +version = "3.4.0" +description = "Validate configuration and produce human readable error messages." +optional = false +python-versions = ">=3.8" +files = [ + {file = "cfgv-3.4.0-py2.py3-none-any.whl", hash = "sha256:b7265b1f29fd3316bfcd2b330d63d024f2bfd8bcb8b0272f8e19a504856c48f9"}, + {file = "cfgv-3.4.0.tar.gz", hash = "sha256:e52591d4c5f5dead8e0f673fb16db7949d2cfb3f7da4582893288f0ded8fe560"}, +] + [[package]] name = "charset-normalizer" version = "3.3.2" @@ -341,6 +352,17 @@ files = [ {file = "decorator-5.1.1.tar.gz", hash = "sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330"}, ] +[[package]] +name = "distlib" +version = "0.3.8" +description = "Distribution utilities" +optional = false +python-versions = "*" +files = [ + {file = "distlib-0.3.8-py2.py3-none-any.whl", hash = "sha256:034db59a0b96f8ca18035f36290806a9a6e6bd9d1ff91e45a7f172eb17e51784"}, + {file = "distlib-0.3.8.tar.gz", hash = "sha256:1530ea13e350031b6312d8580ddb6b27a104275a31106523b8f123787f494f64"}, +] + [[package]] name = "exceptiongroup" version = "1.2.0" @@ -369,6 +391,22 @@ files = [ [package.extras] tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipython", "littleutils", "pytest", "rich"] +[[package]] +name = "filelock" +version = "3.13.1" +description = "A platform independent file lock." +optional = false +python-versions = ">=3.8" +files = [ + {file = "filelock-3.13.1-py3-none-any.whl", hash = "sha256:57dbda9b35157b05fb3e58ee91448612eb674172fab98ee235ccb0b5bee19a1c"}, + {file = "filelock-3.13.1.tar.gz", hash = "sha256:521f5f56c50f8426f5e03ad3b281b490a87ef15bc6c526f168290f0c7148d44e"}, +] + +[package.extras] +docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.24)"] +testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)"] +typing = ["typing-extensions (>=4.8)"] + [[package]] name = "fonttools" version = "4.47.2" @@ -461,6 +499,20 @@ python-dateutil = ">=2.8.1" [package.extras] dev = ["flake8", "markdown", "twine", "wheel"] +[[package]] +name = "identify" +version = "2.5.33" +description = "File identification library for Python" +optional = false +python-versions = ">=3.8" +files = [ + {file = "identify-2.5.33-py2.py3-none-any.whl", hash = "sha256:d40ce5fcd762817627670da8a7d8d8e65f24342d14539c59488dc603bf662e34"}, + {file = "identify-2.5.33.tar.gz", hash = "sha256:161558f9fe4559e1557e1bff323e8631f6a0e4837f7497767c1782832f16b62d"}, +] + +[package.extras] +license = ["ukkonen"] + [[package]] name = "idna" version = "3.6" @@ -1003,6 +1055,20 @@ scipy = "*" [package.extras] docs = ["numpydoc", "sphinx", "sphinx-rtd-theme"] +[[package]] +name = "nodeenv" +version = "1.8.0" +description = "Node.js virtual environment builder" +optional = false +python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*" +files = [ + {file = "nodeenv-1.8.0-py2.py3-none-any.whl", hash = "sha256:df865724bb3c3adc86b3876fa209771517b0cfe596beff01a92700e0e8be4cec"}, + {file = "nodeenv-1.8.0.tar.gz", hash = "sha256:d51e0c37e64fbf47d017feac3145cdbb58836d7eee8c6f6d3b6880c5456227d2"}, +] + +[package.dependencies] +setuptools = "*" + [[package]] name = "numdifftools" version = "0.9.41" @@ -1322,6 +1388,24 @@ files = [ dev = ["pre-commit", "tox"] testing = ["pytest", "pytest-benchmark"] +[[package]] +name = "pre-commit" +version = "3.6.0" +description = "A framework for managing and maintaining multi-language pre-commit hooks." +optional = false +python-versions = ">=3.9" +files = [ + {file = "pre_commit-3.6.0-py2.py3-none-any.whl", hash = "sha256:c255039ef399049a5544b6ce13d135caba8f2c28c3b4033277a788f434308376"}, + {file = "pre_commit-3.6.0.tar.gz", hash = "sha256:d30bad9abf165f7785c15a21a1f46da7d0677cb00ee7ff4c579fd38922efe15d"}, +] + +[package.dependencies] +cfgv = ">=2.0.0" +identify = ">=1.0.0" +nodeenv = ">=0.11.1" +pyyaml = ">=5.1" +virtualenv = ">=20.10.0" + [[package]] name = "prompt-toolkit" version = "3.0.43" @@ -1696,6 +1780,22 @@ dev = ["click", "cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyl doc = ["jupytext", "matplotlib (>2)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"] test = ["asv", "gmpy2", "hypothesis", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"] +[[package]] +name = "setuptools" +version = "69.0.3" +description = "Easily download, build, install, upgrade, and uninstall Python packages" +optional = false +python-versions = ">=3.8" +files = [ + {file = "setuptools-69.0.3-py3-none-any.whl", hash = "sha256:385eb4edd9c9d5c17540511303e39a147ce2fc04bc55289c322b9e5904fe2c05"}, + {file = "setuptools-69.0.3.tar.gz", hash = "sha256:be1af57fc409f93647f2e8e4573a142ed38724b8cdd389706a867bb4efcf1e78"}, +] + +[package.extras] +docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"] +testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"] +testing-integration = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.1)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"] + [[package]] name = "six" version = "1.16.0" @@ -1937,6 +2037,26 @@ brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"] socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"] zstd = ["zstandard (>=0.18.0)"] +[[package]] +name = "virtualenv" +version = "20.25.0" +description = "Virtual Python Environment builder" +optional = false +python-versions = ">=3.7" +files = [ + {file = "virtualenv-20.25.0-py3-none-any.whl", hash = "sha256:4238949c5ffe6876362d9c0180fc6c3a824a7b12b80604eeb8085f2ed7460de3"}, + {file = "virtualenv-20.25.0.tar.gz", hash = "sha256:bf51c0d9c7dd63ea8e44086fa1e4fb1093a31e963b86959257378aef020e1f1b"}, +] + +[package.dependencies] +distlib = ">=0.3.7,<1" +filelock = ">=3.12.2,<4" +platformdirs = ">=3.9.1,<5" + +[package.extras] +docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"] +test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.4)", "pytest-env (>=0.8.2)", "pytest-freezer (>=0.4.8)", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10)"] + [[package]] name = "watchdog" version = "3.0.0" @@ -2001,4 +2121,4 @@ files = [ [metadata] lock-version = "2.0" python-versions = ">=3.10,<3.13" -content-hash = "34645291ffe1814c1c60ed845fda663bedfdea83baa6abec223b5b6f3d39031f" +content-hash = "06f1d10780fb94ccd26fa372524e161efbc5a75fac8ce7812912e3f70d6985a7" diff --git a/pyproject.toml b/pyproject.toml index 8ef1013e..61f3ec61 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -47,6 +47,7 @@ black = "^23.7.0" pytest = "^7.4.2" mkdocs-material = "^8.5.7" ipython = "^8.15.0" +pre-commit = "^3.6.0" [build-system] requires = ["poetry>=0.12"] diff --git a/test/test_core.py b/test/test_core.py index 39384105..dd8a02a7 100644 --- a/test/test_core.py +++ b/test/test_core.py @@ -2,10 +2,12 @@ from unittest.mock import patch import numpy as np +from numpy.testing import assert_array_equal import pandas as pd import nmrglue as ng from lmfit import Model + from peakipy.core import ( make_mask, fix_params, @@ -19,9 +21,92 @@ Peaklist, Lineshape, PeaklistFormat, + select_reference_planes_using_indices, + select_planes_above_threshold_from_masked_data, + slice_peaks_from_data_using_mask, ) +def test_select_reference_planes_using_indices(): + data = np.zeros((6, 100, 200)) + indices = [] + np.testing.assert_array_equal( + select_reference_planes_using_indices(data, indices), data + ) + indices = [1] + assert select_reference_planes_using_indices(data, indices).shape == (1, 100, 200) + indices = [1, -1] + assert select_reference_planes_using_indices(data, indices).shape == (2, 100, 200) + + +def test_slice_peaks_from_data_using_mask(): + data = np.array( + [ + np.array( + [ + [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 1, 1, 0, 0, 0, 0], + [0, 0, 0, 1, 2, 2, 1, 0, 0, 0], + [0, 0, 1, 2, 3, 3, 2, 1, 0, 0], + [0, 1, 2, 3, 4, 4, 3, 2, 1, 0], + [1, 2, 3, 4, 5, 5, 4, 3, 2, 1], + [0, 1, 2, 3, 4, 4, 3, 2, 1, 0], + [0, 0, 1, 2, 3, 3, 2, 1, 0, 0], + [0, 0, 0, 1, 2, 2, 1, 0, 0, 0], + [0, 0, 0, 0, 1, 1, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], + ] + ) + for i in range(5) + ] + ) + mask = data[0] > 0 + assert data.shape == (5, 11, 10) + assert mask.shape == (11, 10) + peak_slices = slice_peaks_from_data_using_mask(data, mask) + # array is flattened by application of mask + assert peak_slices.shape == (5, 50) + + +def test_select_planes_above_threshold_from_masked_data(): + peak_slices = np.array( + [ + [1, 1, 1, 1, 1, 1], + [2, 2, 2, 2, 2, 2], + [-1, -1, -1, -1, -1, -1], + [-2, -2, -2, -2, -2, -2], + ] + ) + assert peak_slices.shape == (4, 6) + threshold = -1 + assert select_planes_above_threshold_from_masked_data( + peak_slices, threshold + ).shape == ( + 4, + 6, + ) + threshold = 2 + assert_array_equal( + select_planes_above_threshold_from_masked_data(peak_slices, threshold), + peak_slices, + ) + threshold = 1 + assert select_planes_above_threshold_from_masked_data( + peak_slices, threshold + ).shape == (2, 6) + + threshold = None + assert_array_equal( + select_planes_above_threshold_from_masked_data(peak_slices, threshold), + peak_slices, + ) + threshold = 10 + assert_array_equal( + select_planes_above_threshold_from_masked_data(peak_slices, threshold), + peak_slices, + ) + + class TestCoreFunctions(unittest.TestCase): def test_make_mask(self): data = np.ones((10, 10)) @@ -140,7 +225,7 @@ def test_to_prefix(self): (" one", "_one_"), (" one/two", "_oneortwo_"), (" one?two", "_onemaybetwo_"), - (" [{one?two\}][", "___onemaybetwo____"), + (r" [{one?two\}][", "___onemaybetwo____"), ] for test, expect in names: prefix = to_prefix(test) From 202c68bb20bf156a89f532e89299df450236d668 Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Sun, 11 Feb 2024 23:13:17 -0500 Subject: [PATCH 10/37] refactored check code and added plotly 3D surface and wireframe --- peakipy/cli/main.py | 700 +++++++++++++++++++++++++++----------------- poetry.lock | 225 +++++++++++++- pyproject.toml | 2 + 3 files changed, 652 insertions(+), 275 deletions(-) diff --git a/peakipy/cli/main.py b/peakipy/cli/main.py index e5272532..88feb731 100644 --- a/peakipy/cli/main.py +++ b/peakipy/cli/main.py @@ -22,6 +22,7 @@ import json import shutil from pathlib import Path +from dataclasses import dataclass, field from enum import Enum from typing import Optional, Tuple, List, Annotated from multiprocessing import Pool @@ -41,7 +42,10 @@ from matplotlib import cm from matplotlib.backends.backend_pdf import PdfPages from matplotlib.widgets import Button + import yaml +import plotly.graph_objects as go +import panel as pn from peakipy.core import ( Peaklist, @@ -61,6 +65,8 @@ PeaklistFormat, Lineshape, OutFmt, + get_limits_for_axis_in_points, + deal_with_peaks_on_edge_of_spectrum, ) from .fit import ( cpu_count, @@ -357,11 +363,11 @@ def read( print( f"""[green] - + ✨✨ Finished reading and clustering peaks! ✨✨ - + Use {outname} to run peakipy edit or fit.[/green] - + """ ) @@ -605,11 +611,14 @@ def fit( # split peak lists # tmp_dir = split_peaklist(peakipy_data.df, n_cpu) fit_peaks_args = FitPeaksInput(args, peakipy_data.data, config, plane_numbers) - with Pool(processes=n_cpu) as pool, tqdm( - total=len(peakipy_data.df.CLUSTID.unique()), - ascii="▱▰", - colour="green", - ) as pbar: + with ( + Pool(processes=n_cpu) as pool, + tqdm( + total=len(peakipy_data.df.CLUSTID.unique()), + ascii="▱▰", + colour="green", + ) as pbar, + ): # result = pool.map(fit_peaks, peaklists) # result = pool.starmap(fit_peaks, zip(peaklists, args_list)) result = [ @@ -795,7 +804,7 @@ def fit( print( """[green] 🍾 ✨ Finished! ✨ 🍾 - [/green] + [/green] """ ) run_log() @@ -852,6 +861,351 @@ def get_fit_data_for_selected_peak_clusters(fits, clusters): return fits +def make_masks_from_plane_data(empty_mask_array, plane_data): + # make masks + individual_masks = [] + for cx, cy, rx, ry, name in zip( + plane_data.center_x, + plane_data.center_y, + plane_data.x_radius, + plane_data.y_radius, + plane_data.assignment, + ): + tmp_mask = make_mask(empty_mask_array, cx, cy, rx, ry) + empty_mask_array += tmp_mask + individual_masks.append(tmp_mask) + filled_mask_array = empty_mask_array + return individual_masks, filled_mask_array + + +def simulate_pv_pv_lineshapes_from_fitted_peak_parameters( + peak_parameters, XY, sim_data, sim_data_singles +): + for amp, c_x, c_y, s_x, s_y, frac_x, frac_y, ls in zip( + peak_parameters.amp, + peak_parameters.center_x, + peak_parameters.center_y, + peak_parameters.sigma_x, + peak_parameters.sigma_y, + peak_parameters.fraction_x, + peak_parameters.fraction_y, + peak_parameters.lineshape, + ): + sim_data_i = pv_pv(XY, amp, c_x, c_y, s_x, s_y, frac_x, frac_y).reshape( + sim_data.shape + ) + sim_data += sim_data_i + sim_data_singles.append(sim_data_i) + return sim_data, sim_data_singles + + +def simulate_lineshapes_from_fitted_peak_parameters( + peak_parameters, XY, sim_data, sim_data_singles +): + shape = sim_data.shape + for amp, c_x, c_y, s_x, s_y, frac, lineshape in zip( + peak_parameters.amp, + peak_parameters.center_x, + peak_parameters.center_y, + peak_parameters.sigma_x, + peak_parameters.sigma_y, + peak_parameters.fraction, + peak_parameters.lineshape, + ): + # print(amp) + match lineshape: + case "G" | "L" | "PV": + sim_data_i = pvoigt2d(XY, amp, c_x, c_y, s_x, s_y, frac).reshape(shape) + case "PV_L": + sim_data_i = pv_l(XY, amp, c_x, c_y, s_x, s_y, frac).reshape(shape) + + case "PV_G": + sim_data_i = pv_g(XY, amp, c_x, c_y, s_x, s_y, frac).reshape(shape) + + case "G_L": + sim_data_i = gaussian_lorentzian( + XY, amp, c_x, c_y, s_x, s_y, frac + ).reshape(shape) + + case "V": + sim_data_i = voigt2d(XY, amp, c_x, c_y, s_x, s_y, frac).reshape(shape) + sim_data += sim_data_i + sim_data_singles.append(sim_data_i) + return sim_data, sim_data_singles + + +@dataclass +class PlottingDataForPlane: + pseudo3D: Pseudo3D + plane_id: int + plane: pd.DataFrame + X: np.array + Y: np.array + mask: np.array + individual_masks: List[np.array] + sim_data: np.array + sim_data_singles: List[np.array] + min_x: int + max_x: int + min_y: int + max_y: int + fit_color: str + data_color: str + rcount: int + ccount: int + + x_plot: np.array = field(init=False) + y_plot: np.array = field(init=False) + masked_data: np.array = field(init=False) + masked_sim_data: np.array = field(init=False) + residual: np.array = field(init=False) + single_colors: List = field(init=False) + + def __post_init__(self): + self.plane_data = self.pseudo3D.data[self.plane_id] + self.masked_data = self.plane_data.copy() + self.masked_sim_data = self.sim_data.copy() + self.masked_data[~self.mask] = np.nan + self.masked_sim_data[~self.mask] = np.nan + + self.x_plot = self.pseudo3D.uc_f2.ppm( + self.X[self.min_y : self.max_y, self.min_x : self.max_x] + ) + self.y_plot = self.pseudo3D.uc_f1.ppm( + self.Y[self.min_y : self.max_y, self.min_x : self.max_x] + ) + self.masked_data = self.masked_data[ + self.min_y : self.max_y, self.min_x : self.max_x + ] + self.sim_plot = self.masked_sim_data[ + self.min_y : self.max_y, self.min_x : self.max_x + ] + self.residual = self.masked_data - self.sim_plot + + for single_mask, single in zip(self.individual_masks, self.sim_data_singles): + single[~single_mask] = np.nan + self.sim_data_singles = [ + sim_data_single[self.min_y : self.max_y, self.min_x : self.max_x] + for sim_data_single in self.sim_data_singles + ] + self.single_colors = [ + cm.viridis(i) for i in np.linspace(0, 1, len(self.sim_data_singles)) + ] + + +def plot_data_is_valid(plot_data: PlottingDataForPlane) -> bool: + if len(plot_data.x_plot) < 1 or len(plot_data.y_plot) < 1: + print(f"[red]Nothing to plot for cluster {int(plot_data.plane.clustid)}[/red]") + print(f"[red]x={plot_data.x_plot},y={plot_data.y_plot}[/red]") + print( + df_to_rich_table( + plot_data.plane, + title="", + columns=bad_column_selection, + styles=bad_color_selection, + ) + ) + plt.close() + validated = False + # print(Fore.RED + "Maybe your F1/F2 radii for fitting were too small...") + elif plot_data.masked_data.shape[0] == 0 or plot_data.masked_data.shape[1] == 0: + print(f"[red]Nothing to plot for cluster {int(plot_data.plane.clustid)}[/red]") + print( + df_to_rich_table( + plot_data.plane, + title="Bad plane", + columns=bad_column_selection, + styles=bad_color_selection, + ) + ) + spec_lim_f1 = " - ".join( + ["%8.3f" % i for i in plot_data.pseudo3D.f1_ppm_limits] + ) + spec_lim_f2 = " - ".join( + ["%8.3f" % i for i in plot_data.pseudo3D.f2_ppm_limits] + ) + print(f"Spectrum limits are {plot_data.pseudo3D.f2_label:4s}:{spec_lim_f2} ppm") + print(f" {plot_data.pseudo3D.f1_label:4s}:{spec_lim_f1} ppm") + plt.close() + validated = False + else: + validated = True + return validated + + +def create_matplotlib_figure( + plot_data: PlottingDataForPlane, + pdf: PdfPages, + individual=False, + label=False, + ccpn_flag=False, + show=True, +): + fig = plt.figure(figsize=(10, 6)) + ax = fig.add_subplot(projection="3d") + if plot_data_is_valid(plot_data): + cset = ax.contourf( + plot_data.x_plot, + plot_data.y_plot, + plot_data.residual, + zdir="z", + offset=np.nanmin(plot_data.masked_data) * 1.1, + alpha=0.5, + cmap=cm.coolwarm, + ) + cbl = fig.colorbar(cset, ax=ax, shrink=0.5, format="%.2e") + cbl.ax.set_title("Residual", pad=20) + + if individual: + #  for plotting single fit surfaces + single_colors = [ + cm.viridis(i) + for i in np.linspace(0, 1, len(plot_data.sim_data_singles)) + ] + [ + ax.plot_surface( + plot_data.x_plot, + plot_data.y_plot, + z_single, + color=c, + alpha=0.5, + ) + for c, z_single in zip(single_colors, plot_data.sim_data_singles) + ] + ax.plot_wireframe( + plot_data.x_plot, + plot_data.y_plot, + plot_data.sim_plot, + # colors=[cm.coolwarm(i) for i in np.ravel(residual)], + colors=plot_data.fit_color, + linestyle="--", + label="fit", + rcount=plot_data.rcount, + ccount=plot_data.ccount, + ) + ax.plot_wireframe( + plot_data.x_plot, + plot_data.y_plot, + plot_data.masked_data, + colors=plot_data.data_color, + linestyle="-", + label="data", + rcount=plot_data.rcount, + ccount=plot_data.ccount, + ) + ax.set_ylabel(plot_data.pseudo3D.f1_label) + ax.set_xlabel(plot_data.pseudo3D.f2_label) + + # axes will appear inverted + ax.view_init(30, 120) + + # names = ",".join(plane.assignment) + title = f"Plane={plot_data.plane_id},Cluster={plot_data.plane.clustid.iloc[0]}" + plt.title(title) + print(f"[green]Plotting: {title}[/green]") + out_str = "Volumes (Heights)\n===========\n" + # chi2s = [] + for _, row in plot_data.plane.iterrows(): + out_str += f"{row.assignment} = {row.amp:.3e} ({row.height:.3e})\n" + if label: + ax.text( + row.center_x_ppm, + row.center_y_ppm, + row.height * 1.2, + row.assignment, + (1, 1, 1), + ) + + ax.text2D( + -0.5, + 1.0, + out_str, + transform=ax.transAxes, + fontsize=10, + fontfamily="sans-serif", + va="top", + bbox=dict(boxstyle="round", ec="k", fc="k", alpha=0.5), + ) + + ax.legend() + + if show: + + def exit_program(event): + exit() + + def next_plot(event): + plt.close() + + axexit = plt.axes([0.81, 0.05, 0.1, 0.075]) + bnexit = Button(axexit, "Exit") + bnexit.on_clicked(exit_program) + axnext = plt.axes([0.71, 0.05, 0.1, 0.075]) + bnnext = Button(axnext, "Next") + bnnext.on_clicked(next_plot) + if ccpn_flag: + plt.show(windowTitle="", size=(1000, 500)) + else: + plt.show() + else: + pdf.savefig() + + plt.close() + + +def create_plotly_wireframe_lines(plot_data: PlottingDataForPlane): + lines = [] + show_legend = False + # make simulated data wireframe + line_marker = dict(color=plot_data.fit_color, width=4) + for i, j, k in zip(plot_data.x_plot, plot_data.y_plot, plot_data.sim_plot): + lines.append(go.Scatter3d(x=i, y=j, z=k, mode="lines", line=line_marker)) + for i, j, k in zip(plot_data.x_plot.T, plot_data.y_plot.T, plot_data.sim_plot.T): + lines.append(go.Scatter3d(x=i, y=j, z=k, mode="lines", line=line_marker)) + # make experimental data wireframe + line_marker = dict(color=plot_data.data_color, width=4) + for i, j, k in zip(plot_data.x_plot, plot_data.y_plot, plot_data.masked_data): + lines.append(go.Scatter3d(x=i, y=j, z=k, mode="lines", line=line_marker)) + for i, j, k in zip(plot_data.x_plot.T, plot_data.y_plot.T, plot_data.masked_data.T): + lines.append(go.Scatter3d(x=i, y=j, z=k, mode="lines", line=line_marker)) + + return lines + + +def create_plotly_surfaces(plot_data: PlottingDataForPlane): + data = [] + color_scale_values = np.linspace(0, 1, len(plot_data.single_colors)) + color_scale = [ + [val, f"rgb({', '.join('%d'%(i*255) for i in c[0:3])})"] + for val, c in zip(color_scale_values, plot_data.single_colors) + ] + for val, individual_peak in zip(color_scale_values, plot_data.sim_data_singles): + colors = np.zeros(shape=individual_peak.shape) + val + data.append( + go.Surface( + z=individual_peak, + x=plot_data.x_plot, + y=plot_data.y_plot, + opacity=0.5, + surfacecolor=colors, + colorscale=color_scale, + showscale=False, + cmin=0, + cmax=1, + ) + ) + return data + + +def create_plotly_figure(plot_data: PlottingDataForPlane): + lines = create_plotly_wireframe_lines(plot_data) + surfaces = create_plotly_surfaces(plot_data) + fig = go.Figure(data=lines + surfaces) + layout = go.Layout(showlegend=False) + fig.update_layout(layout) + return fig + + @app.command(help="Interactive plots for checking fits") def check( fits: Path, @@ -944,7 +1298,7 @@ def check( data_color, fit_color = unpack_plotting_colors(colors) fits = get_fit_data_for_selected_peak_clusters(fits, clusters) - groups = fits.groupby("clustid") + peak_clusters = fits.groupby("clustid") # make plotting meshes x = np.arange(pseudo3D.f2_size) @@ -953,292 +1307,90 @@ def check( X, Y = XY with PdfPages(outname) as pdf: - for _, group in groups: + for _, peak_cluster in peak_clusters: table = df_to_rich_table( - group, + peak_cluster, title="", columns=columns_to_print, styles=["blue" for _ in columns_to_print], ) print(table) - mask = np.zeros((pseudo3D.f1_size, pseudo3D.f2_size), dtype=bool) - - first_plane = group[group.plane == selected_plane] - - x_radius = group.x_radius.max() - y_radius = group.y_radius.max() - max_x, min_x = ( - int(np.ceil(max(group.center_x) + x_radius + 1)), - int(np.floor(min(group.center_x) - x_radius)), + x_radius = peak_cluster.x_radius.max() + y_radius = peak_cluster.y_radius.max() + max_x, min_x = get_limits_for_axis_in_points( + group_axis_points=peak_cluster.center_x, mask_radius_in_points=x_radius ) - max_y, min_y = ( - int(np.ceil(max(group.center_y) + y_radius + 1)), - int(np.floor(min(group.center_y) - y_radius)), + max_y, min_y = get_limits_for_axis_in_points( + group_axis_points=peak_cluster.center_y, mask_radius_in_points=y_radius + ) + max_x, min_x, max_y, min_y = deal_with_peaks_on_edge_of_spectrum( + pseudo3D.data.shape, max_x, min_x, max_y, min_y ) - #  deal with peaks on the edge of spectrum - if min_y < 0: - min_y = 0 - - if min_x < 0: - min_x = 0 - - if max_y > pseudo3D.f1_size: - max_y = pseudo3D.f1_size - - if max_x > pseudo3D.f2_size: - max_x = pseudo3D.f2_size - - masks = [] - # make masks - for cx, cy, rx, ry, name in zip( - first_plane.center_x, - first_plane.center_y, - first_plane.x_radius, - first_plane.y_radius, - first_plane.assignment, - ): - tmp_mask = make_mask(mask, cx, cy, rx, ry) - mask += tmp_mask - masks.append(tmp_mask) + empty_mask_array = np.zeros( + (pseudo3D.f1_size, pseudo3D.f2_size), dtype=bool + ) + first_plane = peak_cluster[peak_cluster.plane == selected_plane] + individual_masks, mask = make_masks_from_plane_data( + empty_mask_array, first_plane + ) # generate simulated data - for plane_id, plane in group.groupby("plane"): + for plane_id, plane in peak_cluster.groupby("plane"): sim_data_singles = [] sim_data = np.zeros((pseudo3D.f1_size, pseudo3D.f2_size)) - shape = sim_data.shape try: - for amp, c_x, c_y, s_x, s_y, frac_x, frac_y, ls in zip( - plane.amp, - plane.center_x, - plane.center_y, - plane.sigma_x, - plane.sigma_y, - plane.fraction_x, - plane.fraction_y, - plane.lineshape, - ): - sim_data_i = pv_pv( - XY, amp, c_x, c_y, s_x, s_y, frac_x, frac_y - ).reshape(shape) - sim_data += sim_data_i - sim_data_singles.append(sim_data_i) + ( + sim_data, + sim_data_singles, + ) = simulate_pv_pv_lineshapes_from_fitted_peak_parameters( + plane, XY, sim_data, sim_data_singles + ) except: - for amp, c_x, c_y, s_x, s_y, frac, ls in zip( - plane.amp, - plane.center_x, - plane.center_y, - plane.sigma_x, - plane.sigma_y, - plane.fraction, - plane.lineshape, - ): - # print(amp) - match ls: - case "G" | "L" | "PV": - sim_data_i = pvoigt2d( - XY, amp, c_x, c_y, s_x, s_y, frac - ).reshape(shape) - case "PV_L": - sim_data_i = pv_l( - XY, amp, c_x, c_y, s_x, s_y, frac - ).reshape(shape) - - case "PV_G": - sim_data_i = pv_g( - XY, amp, c_x, c_y, s_x, s_y, frac - ).reshape(shape) - - case "G_L": - sim_data_i = gaussian_lorentzian( - XY, amp, c_x, c_y, s_x, s_y, frac - ).reshape(shape) - - case "V": - sim_data_i = voigt2d( - XY, amp, c_x, c_y, s_x, s_y, frac - ).reshape(shape) - sim_data += sim_data_i - sim_data_singles.append(sim_data_i) - - masked_data = pseudo3D.data[plane_id].copy() - masked_sim_data = sim_data.copy() - masked_data[~mask] = np.nan - masked_sim_data[~mask] = np.nan + ( + sim_data, + sim_data_singles, + ) = simulate_lineshapes_from_fitted_peak_parameters( + plane, XY, sim_data, sim_data_singles + ) + + plot_data = PlottingDataForPlane( + pseudo3D, + plane_id, + plane, + X, + Y, + mask, + individual_masks, + sim_data, + sim_data_singles, + min_x, + max_x, + min_y, + max_y, + fit_color, + data_color, + rcount, + ccount, + ) if ccpn_flag: plt = PlotterWidget() else: plt = matplotlib.pyplot + create_matplotlib_figure( + plot_data, pdf, individual, label, ccpn_flag, show + ) + # fig = create_plotly_figure(plot_data) + fig = create_plotly_figure(plot_data) + fig.show() + # surf = pn.pane.plotly.Plotly(fig) + # app = pn.Column(surf) + # app.show(threaded=True) + if first: + break - fig = plt.figure(figsize=(10, 6)) - ax = fig.add_subplot(projection="3d") - ## slice out plot area - x_plot = pseudo3D.uc_f2.ppm(X[min_y:max_y, min_x:max_x]) - y_plot = pseudo3D.uc_f1.ppm(Y[min_y:max_y, min_x:max_x]) - masked_data = masked_data[min_y:max_y, min_x:max_x] - sim_plot = masked_sim_data[min_y:max_y, min_x:max_x] - # or len(masked_data)<1 or len(sim_plot)<1 - - if len(x_plot) < 1 or len(y_plot) < 1: - print( - f"[red]Nothing to plot for cluster {int(plane.clustid)}[/red]" - ) - print(f"[red]x={x_plot},y={y_plot}[/red]") - print( - df_to_rich_table( - plane, - title="", - columns=bad_column_selection, - styles=bad_color_selection, - ) - ) - plt.close() - - # print(Fore.RED + "Maybe your F1/F2 radii for fitting were too small...") - elif masked_data.shape[0] == 0 or masked_data.shape[1] == 0: - print( - f"[red]Nothing to plot for cluster {int(plane.clustid)}[/red]" - ) - print( - df_to_rich_table( - plane, - title="Bad plane", - columns=bad_column_selection, - styles=bad_color_selection, - ) - ) - spec_lim_f1 = " - ".join( - ["%8.3f" % i for i in pseudo3D.f1_ppm_limits] - ) - spec_lim_f2 = " - ".join( - ["%8.3f" % i for i in pseudo3D.f2_ppm_limits] - ) - print( - f"Spectrum limits are {pseudo3D.f2_label:4s}:{spec_lim_f2} ppm" - ) - print( - f" {pseudo3D.f1_label:4s}:{spec_lim_f1} ppm" - ) - plt.close() - else: - residual = masked_data - sim_plot - cset = ax.contourf( - x_plot, - y_plot, - residual, - zdir="z", - offset=np.nanmin(masked_data) * 1.1, - alpha=0.5, - cmap=cm.coolwarm, - ) - cbl = fig.colorbar(cset, ax=ax, shrink=0.5, format="%.2e") - cbl.ax.set_title("Residual", pad=20) - - if individual: - # for making colored masks - for single_mask, single in zip(masks, sim_data_singles): - single[~single_mask] = np.nan - sim_data_singles = [ - sim_data_single[min_y:max_y, min_x:max_x] - for sim_data_single in sim_data_singles - ] - #  for plotting single fit surfaces - single_colors = [ - cm.viridis(i) - for i in np.linspace(0, 1, len(sim_data_singles)) - ] - [ - ax.plot_surface( - x_plot, y_plot, z_single, color=c, alpha=0.5 - ) - for c, z_single in zip(single_colors, sim_data_singles) - ] - ax.plot_wireframe( - x_plot, - y_plot, - sim_plot, - # colors=[cm.coolwarm(i) for i in np.ravel(residual)], - colors=fit_color, - linestyle="--", - label="fit", - rcount=rcount, - ccount=ccount, - ) - ax.plot_wireframe( - x_plot, - y_plot, - masked_data, - colors=data_color, - linestyle="-", - label="data", - rcount=rcount, - ccount=ccount, - ) - ax.set_ylabel(pseudo3D.f1_label) - ax.set_xlabel(pseudo3D.f2_label) - - # axes will appear inverted - ax.view_init(30, 120) - - # names = ",".join(plane.assignment) - title = f"Plane={plane_id},Cluster={plane.clustid.iloc[0]}" - plt.title(title) - print(f"[green]Plotting: {title}[/green]") - out_str = "Volumes (Heights)\n===========\n" - # chi2s = [] - for ind, row in plane.iterrows(): - out_str += ( - f"{row.assignment} = {row.amp:.3e} ({row.height:.3e})\n" - ) - if label: - ax.text( - row.center_x_ppm, - row.center_y_ppm, - row.height * 1.2, - row.assignment, - (1, 1, 1), - ) - - ax.text2D( - -0.5, - 1.0, - out_str, - transform=ax.transAxes, - fontsize=10, - fontfamily="sans-serif", - va="top", - bbox=dict(boxstyle="round", ec="k", fc="k", alpha=0.5), - ) - - ax.legend() - - if show: - - def exit_program(event): - exit() - - def next_plot(event): - plt.close() - - axexit = plt.axes([0.81, 0.05, 0.1, 0.075]) - bnexit = Button(axexit, "Exit") - bnexit.on_clicked(exit_program) - axnext = plt.axes([0.71, 0.05, 0.1, 0.075]) - bnnext = Button(axnext, "Next") - bnnext.on_clicked(next_plot) - if ccpn_flag: - plt.show(windowTitle="", size=(1000, 500)) - else: - plt.show() - else: - pdf.savefig() - - plt.close() - - if first: - break run_log() diff --git a/poetry.lock b/poetry.lock index 40676c6e..5ae425a4 100644 --- a/poetry.lock +++ b/poetry.lock @@ -81,6 +81,24 @@ d = ["aiohttp (>=3.7.4)", "aiohttp (>=3.7.4,!=3.9.0)"] jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"] uvloop = ["uvloop (>=0.15.2)"] +[[package]] +name = "bleach" +version = "6.1.0" +description = "An easy safelist-based HTML-sanitizing tool." +optional = false +python-versions = ">=3.8" +files = [ + {file = "bleach-6.1.0-py3-none-any.whl", hash = "sha256:3225f354cfc436b9789c66c4ee030194bee0568fbf9cbdad3bc8b5c26c5f12b6"}, + {file = "bleach-6.1.0.tar.gz", hash = "sha256:0a31f1837963c41d46bbf1331b8778e1308ea0791db03cc4e7357b97cf42a8fe"}, +] + +[package.dependencies] +six = ">=1.9.0" +webencodings = "*" + +[package.extras] +css = ["tinycss2 (>=1.1.0,<1.3)"] + [[package]] name = "bokeh" version = "3.3.4" @@ -767,6 +785,26 @@ files = [ lint = ["pre-commit (>=3.3)"] test = ["pytest (>=7.4)", "pytest-cov (>=4.1)"] +[[package]] +name = "linkify-it-py" +version = "2.0.3" +description = "Links recognition library with FULL unicode support." +optional = false +python-versions = ">=3.7" +files = [ + {file = "linkify-it-py-2.0.3.tar.gz", hash = "sha256:68cda27e162e9215c17d786649d1da0021a451bdc436ef9e0fa0ba5234b9b048"}, + {file = "linkify_it_py-2.0.3-py3-none-any.whl", hash = "sha256:6bcbc417b0ac14323382aef5c5192c0075bf8a9d6b41820a2b66371eac6b6d79"}, +] + +[package.dependencies] +uc-micro-py = "*" + +[package.extras] +benchmark = ["pytest", "pytest-benchmark"] +dev = ["black", "flake8", "isort", "pre-commit", "pyproject-flake8"] +doc = ["myst-parser", "sphinx", "sphinx-book-theme"] +test = ["coverage", "pytest", "pytest-cov"] + [[package]] name = "lmfit" version = "1.2.2" @@ -805,6 +843,30 @@ files = [ docs = ["mdx-gh-links (>=0.2)", "mkdocs (>=1.5)", "mkdocs-gen-files", "mkdocs-literate-nav", "mkdocs-nature (>=0.6)", "mkdocs-section-index", "mkdocstrings[python]"] testing = ["coverage", "pyyaml"] +[[package]] +name = "markdown-it-py" +version = "3.0.0" +description = "Python port of markdown-it. Markdown parsing, done right!" +optional = false +python-versions = ">=3.8" +files = [ + {file = "markdown-it-py-3.0.0.tar.gz", hash = "sha256:e3f60a94fa066dc52ec76661e37c851cb232d92f9886b15cb560aaada2df8feb"}, + {file = "markdown_it_py-3.0.0-py3-none-any.whl", hash = "sha256:355216845c60bd96232cd8d8c40e8f9765cc86f46880e43a8fd22dc1a1a8cab1"}, +] + +[package.dependencies] +mdurl = ">=0.1,<1.0" + +[package.extras] +benchmarking = ["psutil", "pytest", "pytest-benchmark"] +code-style = ["pre-commit (>=3.0,<4.0)"] +compare = ["commonmark (>=0.9,<1.0)", "markdown (>=3.4,<4.0)", "mistletoe (>=1.0,<2.0)", "mistune (>=2.0,<3.0)", "panflute (>=2.3,<3.0)"] +linkify = ["linkify-it-py (>=1,<3)"] +plugins = ["mdit-py-plugins"] +profiling = ["gprof2dot"] +rtd = ["jupyter_sphinx", "mdit-py-plugins", "myst-parser", "pyyaml", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinx_book_theme"] +testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"] + [[package]] name = "markupsafe" version = "2.1.4" @@ -936,6 +998,36 @@ files = [ [package.dependencies] traitlets = "*" +[[package]] +name = "mdit-py-plugins" +version = "0.4.0" +description = "Collection of plugins for markdown-it-py" +optional = false +python-versions = ">=3.8" +files = [ + {file = "mdit_py_plugins-0.4.0-py3-none-any.whl", hash = "sha256:b51b3bb70691f57f974e257e367107857a93b36f322a9e6d44ca5bf28ec2def9"}, + {file = "mdit_py_plugins-0.4.0.tar.gz", hash = "sha256:d8ab27e9aed6c38aa716819fedfde15ca275715955f8a185a8e1cf90fb1d2c1b"}, +] + +[package.dependencies] +markdown-it-py = ">=1.0.0,<4.0.0" + +[package.extras] +code-style = ["pre-commit"] +rtd = ["myst-parser", "sphinx-book-theme"] +testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"] + +[[package]] +name = "mdurl" +version = "0.1.2" +description = "Markdown URL utilities" +optional = false +python-versions = ">=3.7" +files = [ + {file = "mdurl-0.1.2-py3-none-any.whl", hash = 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+markdown-it-py = "*" +mdit-py-plugins = "*" +pandas = ">=1.2" +param = ">=2.0.0,<3.0" +pyviz-comms = ">=2.0.0" +requests = "*" +tqdm = ">=4.48.0" +typing-extensions = "*" +xyzservices = ">=2021.09.1" + +[package.extras] +all = ["aiohttp", "altair", "anywidget", "channels", "croniter", "datashader", "diskcache", "django (<4)", "fastparquet", "flake8", "folium", "graphviz", "holoviews (>=1.16.0)", "hvplot", "ipyleaflet", "ipympl", "ipython (>=7.0)", "ipyvolume", "ipyvuetify", "ipywidgets", "ipywidgets-bokeh", "jupyter-bokeh (>=3.0.7)", "jupyter-server", "jupyterlab", "lxml", "matplotlib", "nbsite (>=0.8.4)", "nbval", "networkx (>=2.5)", "numba (<0.58)", "numpy", "pandas (<2.1.0)", "pandas (>=1.3)", "parameterized", "pillow", "playwright", "plotly", "plotly (>=4.0)", "pre-commit", "psutil", "pydeck", "pygraphviz", "pyinstrument (>=4.0)", "pytest", "pytest-asyncio (<0.22)", "pytest-cov", "pytest-playwright", "pytest-rerunfailures", "pytest-xdist", "python-graphviz", "pyvista", "reacton", "scikit-image", "scikit-learn", "scipy", "seaborn", "streamz", "twine", "vega-datasets", "vtk", "xarray", "xgboost"] +all-pip = ["aiohttp", "altair", "anywidget", "channels", "croniter", "datashader", "diskcache", "django (<4)", "fastparquet", "flake8", "folium", "graphviz", "holoviews (>=1.16.0)", "hvplot", "ipyleaflet", "ipympl", "ipython (>=7.0)", "ipyvolume", "ipyvuetify", "ipywidgets", "ipywidgets-bokeh", "jupyter-bokeh (>=3.0.7)", "jupyter-server", "jupyterlab", "lxml", "matplotlib", "nbsite (>=0.8.4)", "nbval", "networkx (>=2.5)", "numba (<0.58)", "numpy", "pandas (<2.1.0)", "pandas (>=1.3)", "parameterized", "pillow", "playwright", "plotly", "plotly (>=4.0)", "pre-commit", "psutil", "pydeck", "pyinstrument (>=4.0)", "pytest", "pytest-asyncio (<0.22)", "pytest-cov", "pytest-playwright", "pytest-rerunfailures", "pytest-xdist", "pyvista", "reacton", "scikit-image", "scikit-learn", "scipy", "seaborn", "streamz", "twine", "vega-datasets", "vtk", "xarray", "xgboost"] +build = ["bleach", "bokeh (>=3.3.0,<3.4.0)", "cryptography (<39)", "markdown", "packaging", "param (>=2.0.0)", "pyviz-comms (>=2.0.0)", "requests", "setuptools (>=42)", "tqdm (>=4.48.0)", "urllib3 (<2.0)"] +doc = ["holoviews (>=1.16.0)", "jupyterlab", "lxml", "matplotlib", "nbsite (>=0.8.4)", "pandas (<2.1.0)", "pillow", "plotly"] +examples = ["aiohttp", "altair", "channels", "croniter", "datashader", "django (<4)", "fastparquet", "folium", "graphviz", "holoviews (>=1.16.0)", "hvplot", "ipyleaflet", "ipympl", "ipyvolume", "ipyvuetify", "ipywidgets", "ipywidgets-bokeh", "jupyter-bokeh (>=3.0.7)", "networkx (>=2.5)", "plotly (>=4.0)", "pydeck", "pygraphviz", "pyinstrument (>=4.0)", "python-graphviz", "pyvista", "reacton", "scikit-image", "scikit-learn", "seaborn", "streamz", "vega-datasets", "vtk", "xarray", "xgboost"] +recommended = ["holoviews (>=1.16.0)", "jupyterlab", "matplotlib", "pillow", "plotly"] +tests = ["altair", "anywidget", "diskcache", "flake8", "folium", "holoviews (>=1.16.0)", "ipympl", "ipython (>=7.0)", "ipyvuetify", "ipywidgets-bokeh", "nbval", "numba (<0.58)", "numpy", "pandas (>=1.3)", "parameterized", "pre-commit", "psutil", "pytest", "pytest-asyncio (<0.22)", "pytest-cov", "pytest-rerunfailures", "pytest-xdist", "reacton", "scipy", "twine"] +tests-core = ["altair", "anywidget", "diskcache", "flake8", "folium", "holoviews (>=1.16.0)", "ipython (>=7.0)", "nbval", "numpy", "pandas (>=1.3)", "parameterized", "pre-commit", "psutil", "pytest", "pytest-asyncio (<0.22)", "pytest-cov", "pytest-rerunfailures", "pytest-xdist", "scipy"] +ui = ["jupyter-server", "playwright", "pytest-playwright"] + +[[package]] +name = "param" +version = "2.0.2" +description = "Make your Python code clearer and more reliable by declaring Parameters." +optional = false +python-versions = ">=3.8" +files = [ + {file = "param-2.0.2-py3-none-any.whl", hash = "sha256:b269fd7397886ec609e544f81035fa52e1950da0e76d20080bfeca3d7a0317ca"}, + {file = "param-2.0.2.tar.gz", hash = "sha256:785845a727a588eb94c7666d80551c7e2bb97d4309d3507beab66f95e57f7527"}, +] + +[package.extras] +all = ["param[doc]", "param[lint]", "param[tests-full]"] +doc = ["nbsite (==0.8.4)", "param[examples]", "sphinx-remove-toctrees"] +examples = ["aiohttp", "pandas", "panel"] +lint = ["flake8", "pre-commit"] +tests = ["coverage[toml]", "pytest", "pytest-asyncio"] +tests-deser = ["odfpy", "openpyxl", "pyarrow", "tables", "xlrd"] +tests-examples = ["nbval", "param[examples]", "pytest", "pytest-asyncio", "pytest-xdist"] +tests-full = ["cloudpickle", "gmpy", "ipython", "jsonschema", "nest-asyncio", "numpy", "pandas", "param[tests-deser]", "param[tests-examples]", "param[tests]"] + [[package]] name = "parso" version = "0.8.3" @@ -1373,6 +1523,21 @@ files = [ docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.1)", "sphinx-autodoc-typehints (>=1.24)"] test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4)", "pytest-cov (>=4.1)", "pytest-mock (>=3.11.1)"] +[[package]] +name = "plotly" +version = "5.18.0" +description = "An open-source, interactive data visualization library for Python" +optional = false +python-versions = ">=3.6" +files = [ + {file = "plotly-5.18.0-py3-none-any.whl", hash = "sha256:23aa8ea2f4fb364a20d34ad38235524bd9d691bf5299e800bca608c31e8db8de"}, + {file = "plotly-5.18.0.tar.gz", hash = "sha256:360a31e6fbb49d12b007036eb6929521343d6bee2236f8459915821baefa2cbb"}, +] + +[package.dependencies] +packaging = "*" +tenacity = ">=6.2.0" + [[package]] name = "pluggy" version = "1.4.0" @@ -1539,6 +1704,25 @@ files = [ {file = "pytz-2023.4.tar.gz", hash = "sha256:31d4583c4ed539cd037956140d695e42c033a19e984bfce9964a3f7d59bc2b40"}, ] +[[package]] +name = "pyviz-comms" +version = "3.0.1" +description = "A JupyterLab extension for rendering HoloViz content." +optional = false +python-versions = ">=3.8" +files = [ + {file = "pyviz_comms-3.0.1-py3-none-any.whl", hash = "sha256:0130e952b942906a0eb5fcbcc750262a8e4f565a9b06b3c0d8d631f33b61b78e"}, + {file = "pyviz_comms-3.0.1.tar.gz", hash = "sha256:427c33a5a81780db9b9e757f0675f65ea2292d9a642a2d291cfb5cae6cd46991"}, +] + +[package.dependencies] +param = "*" + +[package.extras] +all = ["pyviz-comms[build]", "pyviz-comms[tests]"] +build = ["jupyterlab (>=4.0,<5.0)", "keyring", "rfc3986", "setuptools (>=40.8.0)", "twine"] +tests = ["flake8", "pytest"] + [[package]] name = "pywavelets" version = "1.5.0" @@ -1875,6 +2059,20 @@ build = ["cython (>=0.29.32)"] develop = ["Jinja2", "colorama", "cython (>=0.29.32)", "cython (>=0.29.32,<3.0.0)", "flake8", "isort", "joblib", "matplotlib (>=3)", "oldest-supported-numpy (>=2022.4.18)", "pytest (>=7.0.1,<7.1.0)", "pytest-randomly", "pytest-xdist", "pywinpty", "setuptools-scm[toml] (>=7.0.0,<7.1.0)"] docs = ["ipykernel", "jupyter-client", "matplotlib", "nbconvert", "nbformat", "numpydoc", "pandas-datareader", "sphinx"] +[[package]] +name = "tenacity" +version = "8.2.3" +description = "Retry code until it succeeds" +optional = false +python-versions = ">=3.7" +files = [ + {file = "tenacity-8.2.3-py3-none-any.whl", hash = "sha256:ce510e327a630c9e1beaf17d42e6ffacc88185044ad85cf74c0a8887c6a0f88c"}, + {file = "tenacity-8.2.3.tar.gz", hash = "sha256:5398ef0d78e63f40007c1fb4c0bff96e1911394d2fa8d194f77619c05ff6cc8a"}, +] + +[package.extras] +doc = ["reno", "sphinx", "tornado (>=4.5)"] + [[package]] name = "tifffile" version = "2023.12.9" @@ -2001,6 +2199,20 @@ files = [ {file = "tzdata-2023.4.tar.gz", hash = "sha256:dd54c94f294765522c77399649b4fefd95522479a664a0cec87f41bebc6148c9"}, ] +[[package]] +name = "uc-micro-py" +version = "1.0.3" +description = "Micro subset of unicode data files for linkify-it-py projects." +optional = false +python-versions = ">=3.7" +files = [ + {file = "uc-micro-py-1.0.3.tar.gz", hash = "sha256:d321b92cff673ec58027c04015fcaa8bb1e005478643ff4a500882eaab88c48a"}, + {file = "uc_micro_py-1.0.3-py3-none-any.whl", hash = "sha256:db1dffff340817673d7b466ec86114a9dc0e9d4d9b5ba229d9d60e5c12600cd5"}, +] + +[package.extras] +test = ["coverage", "pytest", "pytest-cov"] + [[package]] name = "uncertainties" version = "3.1.7" @@ -2107,6 +2319,17 @@ files = [ {file = "wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"}, ] +[[package]] +name = "webencodings" +version = "0.5.1" +description = "Character encoding aliases for legacy web content" +optional = false +python-versions = "*" +files = [ + {file = "webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78"}, + {file = "webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"}, +] + [[package]] name = "xyzservices" version = "2023.10.1" @@ -2121,4 +2344,4 @@ files = [ [metadata] lock-version = "2.0" python-versions = ">=3.10,<3.13" -content-hash = "06f1d10780fb94ccd26fa372524e161efbc5a75fac8ce7812912e3f70d6985a7" +content-hash = "0daddfd00e2dc4271dc14874220aa261c3dcf7dba02a9e399ed57e5a6e16b815" diff --git a/pyproject.toml b/pyproject.toml index 61f3ec61..de28bf8f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -38,6 +38,8 @@ typer = "^0.9.0" statsmodels = "^0.13.2" rich = "^12.5.1" tqdm = "^4.65.0" +panel = "^1.3.8" +plotly = "^5.18.0" [tool.poetry.scripts] peakipy = 'peakipy.cli.main:app' From ae0924f5898d1d6b336ca52fd217c3c14db9af29 Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Tue, 13 Feb 2024 08:18:14 -0500 Subject: [PATCH 11/37] addded data validation for peakipy check and plotly flag --- peakipy/cli/main.py | 43 ++++++++++++++-- poetry.lock | 123 +++++++++++++++++++++++++++++++++++++++++++- pyproject.toml | 1 + 3 files changed, 163 insertions(+), 4 deletions(-) diff --git a/peakipy/cli/main.py b/peakipy/cli/main.py index 88feb731..613546cd 100644 --- a/peakipy/cli/main.py +++ b/peakipy/cli/main.py @@ -35,6 +35,7 @@ from tqdm import tqdm from rich import print from skimage.filters import threshold_otsu +from pydantic import BaseModel import matplotlib import matplotlib.pyplot as plt @@ -1206,6 +1207,40 @@ def create_plotly_figure(plot_data: PlottingDataForPlane): return fig +class FitDataModel(BaseModel): + plane: int + clustid: int + assignment: str + memcnt: int + amp: float + height: float + center_x_ppm: float + center_y_ppm: float + fwhm_x_hz: float + fwhm_y_hz: float + lineshape: str + x_radius: float + y_radius: float + center_x: float + center_y: float + sigma_x: float + sigma_y: float + fraction: float + + +def validate_fit_data(dict): + fit_data = FitDataModel(**dict) + return fit_data.model_dump() + + +def validate_fit_dataframe(df): + validated_fit_data = [] + for _, row in df.iterrows(): + fit_data = validate_fit_data(row.to_dict()) + validated_fit_data.append(fit_data) + return pd.DataFrame(validated_fit_data) + + @app.command(help="Interactive plots for checking fits") def check( fits: Path, @@ -1222,6 +1257,7 @@ def check( ccount: int = 50, colors: Tuple[str, str] = ("#5e3c99", "#e66101"), verb: bool = False, + plotly: bool = False, ): """Interactive plots for checking fits @@ -1271,7 +1307,7 @@ def check( "fwhm_y_hz", "lineshape", ] - fits = pd.read_csv(fits) + fits = validate_fit_dataframe(pd.read_csv(fits)) args = {} # get dims from config file config_path = Path("peakipy.config") @@ -1383,8 +1419,9 @@ def check( plot_data, pdf, individual, label, ccpn_flag, show ) # fig = create_plotly_figure(plot_data) - fig = create_plotly_figure(plot_data) - fig.show() + if plotly: + fig = create_plotly_figure(plot_data) + fig.show() # surf = pn.pane.plotly.Plotly(fig) # app = pn.Column(surf) # app.show(threaded=True) diff --git a/poetry.lock b/poetry.lock index 5ae425a4..451b681a 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,5 +1,16 @@ # This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +[[package]] +name = "annotated-types" +version = "0.6.0" +description = "Reusable constraint types to use with typing.Annotated" +optional = false +python-versions = ">=3.8" +files = [ + {file = "annotated_types-0.6.0-py3-none-any.whl", hash = "sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43"}, + {file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"}, +] + [[package]] name = "asteval" version = "0.9.31" @@ -1610,6 +1621,116 @@ files = [ [package.extras] tests = ["pytest"] +[[package]] +name = "pydantic" +version = "2.6.1" +description = "Data validation using Python type hints" +optional = false +python-versions = ">=3.8" +files = [ + {file = "pydantic-2.6.1-py3-none-any.whl", hash = "sha256:0b6a909df3192245cb736509a92ff69e4fef76116feffec68e93a567347bae6f"}, + {file = "pydantic-2.6.1.tar.gz", hash = "sha256:4fd5c182a2488dc63e6d32737ff19937888001e2a6d86e94b3f233104a5d1fa9"}, +] + +[package.dependencies] +annotated-types = ">=0.4.0" +pydantic-core = "2.16.2" +typing-extensions = ">=4.6.1" + 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plotly = "^5.18.0" +pydantic = "^2.6.1" [tool.poetry.scripts] peakipy = 'peakipy.cli.main:app' From 8acd68ebe37de52ddf5d67fd3aa3672e8610b8c5 Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Tue, 13 Feb 2024 23:37:14 -0500 Subject: [PATCH 12/37] very beta check panel --- peakipy/cli/check_panel.py | 50 ++++++++++++++++++++++++++++++++++++++ peakipy/cli/main.py | 4 +-- 2 files changed, 52 insertions(+), 2 deletions(-) create mode 100644 peakipy/cli/check_panel.py diff --git a/peakipy/cli/check_panel.py b/peakipy/cli/check_panel.py new file mode 100644 index 00000000..51fb8fcc --- /dev/null +++ b/peakipy/cli/check_panel.py @@ -0,0 +1,50 @@ +from pathlib import Path +import panel as pn +import pandas as pd + +from peakipy.cli.main import check + +pn.extension() + + +def get_cluster(cluster): + cluster_groups = df.groupby("clustid") + cluster_group = cluster_groups.get_group(cluster) + df_pane = pn.pane.DataFrame(cluster_group) + return df_pane + + +def create_plotly_pane(cluster): + fig = check( + fits=fits_path, + data_path=data_path, + clusters=[cluster], + config_path=config_path, + first=True, + plotly=True, + ) + + fig["layout"].update(height=800, width=800) + fig = fig.to_dict() + return pn.pane.Plotly(fig) + + +if __name__ == "__main__": + fits_path = Path("../../test/test_protein_L/fits.csv") + data_path = Path("../../test/test_protein_L/test1.ft2") + config_path = Path("../../test/test_protein_L/peakipy.config") + df = pd.read_csv(fits_path) + + clusters = [(row.clustid, row.memcnt) for _, row in df.iterrows()] + + select = pn.widgets.Select( + name="Cluster (number of peaks)", options={f"{c} ({m})": c for c, m in clusters} + ) + interactive_cluster_pane = pn.bind(get_cluster, select) + interactive_plotly_pane = pn.bind(create_plotly_pane, select) + check_pane = pn.Card( + select, + pn.Row(interactive_plotly_pane, interactive_cluster_pane), + title="Peakipy check", + ) + check_pane.show() diff --git a/peakipy/cli/main.py b/peakipy/cli/main.py index 613546cd..14a7c536 100644 --- a/peakipy/cli/main.py +++ b/peakipy/cli/main.py @@ -1258,6 +1258,7 @@ def check( colors: Tuple[str, str] = ("#5e3c99", "#e66101"), verb: bool = False, plotly: bool = False, + config_path: Path = Path("peakipy.config"), ): """Interactive plots for checking fits @@ -1310,7 +1311,6 @@ def check( fits = validate_fit_dataframe(pd.read_csv(fits)) args = {} # get dims from config file - config_path = Path("peakipy.config") args, config = read_config(args, config_path) dims = config.get("dims", (1, 2, 3)) @@ -1421,7 +1421,7 @@ def check( # fig = create_plotly_figure(plot_data) if plotly: fig = create_plotly_figure(plot_data) - fig.show() + return fig # surf = pn.pane.plotly.Plotly(fig) # app = pn.Column(surf) # app.show(threaded=True) From 37587e0b1717c7d1862721a5b66784415d8bb848 Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Wed, 14 Feb 2024 09:16:24 -0500 Subject: [PATCH 13/37] fixed bug with paths and dummy assignments - needs further cleaning --- peakipy/cli/main.py | 10 +++++----- peakipy/core.py | 3 +++ 2 files changed, 8 insertions(+), 5 deletions(-) diff --git a/peakipy/cli/main.py b/peakipy/cli/main.py index 14a7c536..93a93abe 100644 --- a/peakipy/cli/main.py +++ b/peakipy/cli/main.py @@ -214,7 +214,7 @@ def read( "struc_size": struc_size, } # name of output peaklist - outname = peaklist_path.stem + outname = peaklist_path.parent / peaklist_path.stem cluster = True match peaklist_format: @@ -267,7 +267,7 @@ def read( ) cluster = False # don't overwrite the old .csv file - outname = outname + "_new" + outname = outname.parent / (outname.stem + "_new") peaks.update_df() @@ -287,14 +287,14 @@ def read( match outfmt.value: case "csv": - outname = outname + ".csv" + outname = outname.with_suffix(".csv") data.to_csv(outname, float_format="%.4f", index=False) case "pkl": - outname = outname + ".pkl" + outname = outname.with_suffix(".pkl") data.to_pickle(outname) # write config file - config_path = Path("peakipy.config") + config_path = data_path.parent / Path("peakipy.config") config_kvs = [ ("dims", dims), ("data_path", str(data_path)), diff --git a/peakipy/core.py b/peakipy/core.py index 114cc854..14066107 100644 --- a/peakipy/core.py +++ b/peakipy/core.py @@ -1751,6 +1751,9 @@ def _read_pipe(self): return df def check_assignments(self): + # self.df["ASS"] = self.df. + self.df["ASS"] = self.df.ASS.astype(object) + self.df.loc[self.df["ASS"].isnull(), "ASS"] = "None_dummy_0" self.df["ASS"] = self.df.ASS.astype(str) duplicates_bool = self.df.ASS.duplicated() duplicates = self.df.ASS[duplicates_bool] From 93b45ce7666cf32d946fe87fb5e4cdfe8b2c1f55 Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Thu, 15 Feb 2024 00:46:19 -0500 Subject: [PATCH 14/37] interactive check panel peakipy-check --- peakipy/cli/check_panel.py | 106 ++++++-- peakipy/cli/main.py | 122 +++++++-- poetry.lock | 491 +++++++++++++++++++------------------ pyproject.toml | 1 + 4 files changed, 442 insertions(+), 278 deletions(-) diff --git a/peakipy/cli/check_panel.py b/peakipy/cli/check_panel.py index 51fb8fcc..60dd576d 100644 --- a/peakipy/cli/check_panel.py +++ b/peakipy/cli/check_panel.py @@ -1,26 +1,72 @@ from pathlib import Path +from dataclasses import dataclass, field +from functools import lru_cache import panel as pn import pandas as pd +import typer -from peakipy.cli.main import check +from peakipy.cli.main import check, validate_fit_dataframe pn.extension() +pn.config.theme = "dark" + +global fits_path +global data_path +global config_path + + +@dataclass +class Data: + fits_path: Path = Path("./fits.csv") + data_path: Path = Path("./test.ft2") + config_path: Path = Path("./peakipy.config") + _df: pd.DataFrame = field(init=False) + + def load_dataframe(self): + self._df = validate_fit_dataframe(pd.read_csv(self.fits_path)) + + @property + def df(self): + return self._df + + +@lru_cache(maxsize=1) +def data_singleton(): + return Data() def get_cluster(cluster): - cluster_groups = df.groupby("clustid") + data = data_singleton() + cluster_groups = data.df.groupby("clustid") cluster_group = cluster_groups.get_group(cluster) - df_pane = pn.pane.DataFrame(cluster_group) + df_pane = pn.pane.DataFrame( + cluster_group[ + [ + "assignment", + "clustid", + "memcnt", + "plane", + "amp", + "height", + "center_x_ppm", + "center_y_ppm", + "fwhm_x_hz", + "fwhm_x_hz", + "lineshape", + ] + ] + ) return df_pane -def create_plotly_pane(cluster): +def create_plotly_pane(cluster, plane): + data = data_singleton() fig = check( - fits=fits_path, - data_path=data_path, + fits=data.fits_path, + data_path=data.data_path, clusters=[cluster], - config_path=config_path, - first=True, + plane=plane, + config_path=data.config_path, plotly=True, ) @@ -29,22 +75,46 @@ def create_plotly_pane(cluster): return pn.pane.Plotly(fig) -if __name__ == "__main__": - fits_path = Path("../../test/test_protein_L/fits.csv") - data_path = Path("../../test/test_protein_L/test1.ft2") - config_path = Path("../../test/test_protein_L/peakipy.config") - df = pd.read_csv(fits_path) +app = typer.Typer() - clusters = [(row.clustid, row.memcnt) for _, row in df.iterrows()] - select = pn.widgets.Select( +@app.command() +def check_panel( + fits_path: Path, data_path: Path, config_path: Path = Path("./peakipy.config") +): + data = data_singleton() + data.fits_path = fits_path + data.data_path = data_path + data.config_path = config_path + data.load_dataframe() + + clusters = [(row.clustid, row.memcnt) for _, row in data.df.iterrows()] + + select_cluster = pn.widgets.Select( name="Cluster (number of peaks)", options={f"{c} ({m})": c for c, m in clusters} ) - interactive_cluster_pane = pn.bind(get_cluster, select) - interactive_plotly_pane = pn.bind(create_plotly_pane, select) + select_plane = pn.widgets.Select( + name="Plane", options={f"{plane}": plane for plane in data.df.plane.unique()} + ) + interactive_cluster_pane = pn.bind(get_cluster, select_cluster) + interactive_plotly_pane = pn.bind( + create_plotly_pane, cluster=select_cluster, plane=select_plane + ) + info_pane = pn.pane.Markdown( + "Select a cluster and plane to look at from the dropdown menus" + ) check_pane = pn.Card( - select, + info_pane, + select_cluster, + select_plane, pn.Row(interactive_plotly_pane, interactive_cluster_pane), title="Peakipy check", ) check_pane.show() + + +if __name__ == "__main__": + # fits_path = Path("../../test/test_protein_L/fits.csv") + # data_path = Path("../../test/test_protein_L/test1.ft2") + # config_path = Path("../../test/test_protein_L/peakipy.config") + app() diff --git a/peakipy/cli/main.py b/peakipy/cli/main.py index 93a93abe..8d0f1f37 100644 --- a/peakipy/cli/main.py +++ b/peakipy/cli/main.py @@ -46,7 +46,9 @@ import yaml import plotly.graph_objects as go -import panel as pn +import plotly.io as pio + +pio.templates.default = "plotly_dark" from peakipy.core import ( Peaklist, @@ -939,7 +941,7 @@ def simulate_lineshapes_from_fitted_peak_parameters( class PlottingDataForPlane: pseudo3D: Pseudo3D plane_id: int - plane: pd.DataFrame + plane_lineshape_parameters: pd.DataFrame X: np.array Y: np.array mask: np.array @@ -996,11 +998,13 @@ def __post_init__(self): def plot_data_is_valid(plot_data: PlottingDataForPlane) -> bool: if len(plot_data.x_plot) < 1 or len(plot_data.y_plot) < 1: - print(f"[red]Nothing to plot for cluster {int(plot_data.plane.clustid)}[/red]") + print( + f"[red]Nothing to plot for cluster {int(plot_data.plane_lineshape_parameters.clustid)}[/red]" + ) print(f"[red]x={plot_data.x_plot},y={plot_data.y_plot}[/red]") print( df_to_rich_table( - plot_data.plane, + plot_data.plane_lineshape_parameters, title="", columns=bad_column_selection, styles=bad_color_selection, @@ -1013,7 +1017,7 @@ def plot_data_is_valid(plot_data: PlottingDataForPlane) -> bool: print(f"[red]Nothing to plot for cluster {int(plot_data.plane.clustid)}[/red]") print( df_to_rich_table( - plot_data.plane, + plot_data.plane_lineshape_parameters, title="Bad plane", columns=bad_column_selection, styles=bad_color_selection, @@ -1101,12 +1105,12 @@ def create_matplotlib_figure( ax.view_init(30, 120) # names = ",".join(plane.assignment) - title = f"Plane={plot_data.plane_id},Cluster={plot_data.plane.clustid.iloc[0]}" + title = f"Plane={plot_data.plane_id},Cluster={plot_data.plane_lineshape_parameters.clustid.iloc[0]}" plt.title(title) print(f"[green]Plotting: {title}[/green]") out_str = "Volumes (Heights)\n===========\n" # chi2s = [] - for _, row in plot_data.plane.iterrows(): + for _, row in plot_data.plane_lineshape_parameters.iterrows(): out_str += f"{row.assignment} = {row.amp:.3e} ({row.height:.3e})\n" if label: ax.text( @@ -1156,23 +1160,64 @@ def next_plot(event): def create_plotly_wireframe_lines(plot_data: PlottingDataForPlane): lines = [] - show_legend = False + show_legend = lambda x: x < 1 + showlegend = False # make simulated data wireframe line_marker = dict(color=plot_data.fit_color, width=4) + counter = 0 for i, j, k in zip(plot_data.x_plot, plot_data.y_plot, plot_data.sim_plot): - lines.append(go.Scatter3d(x=i, y=j, z=k, mode="lines", line=line_marker)) + showlegend = show_legend(counter) + lines.append( + go.Scatter3d( + x=i, + y=j, + z=k, + mode="lines", + line=line_marker, + name="fit", + showlegend=showlegend, + ) + ) + counter += 1 for i, j, k in zip(plot_data.x_plot.T, plot_data.y_plot.T, plot_data.sim_plot.T): - lines.append(go.Scatter3d(x=i, y=j, z=k, mode="lines", line=line_marker)) + lines.append( + go.Scatter3d( + x=i, y=j, z=k, mode="lines", line=line_marker, showlegend=showlegend + ) + ) # make experimental data wireframe line_marker = dict(color=plot_data.data_color, width=4) + counter = 0 for i, j, k in zip(plot_data.x_plot, plot_data.y_plot, plot_data.masked_data): - lines.append(go.Scatter3d(x=i, y=j, z=k, mode="lines", line=line_marker)) + showlegend = show_legend(counter) + lines.append( + go.Scatter3d( + x=i, + y=j, + z=k, + mode="lines", + name="data", + line=line_marker, + showlegend=showlegend, + ) + ) + counter += 1 for i, j, k in zip(plot_data.x_plot.T, plot_data.y_plot.T, plot_data.masked_data.T): - lines.append(go.Scatter3d(x=i, y=j, z=k, mode="lines", line=line_marker)) + lines.append( + go.Scatter3d( + x=i, y=j, z=k, mode="lines", line=line_marker, showlegend=showlegend + ) + ) return lines +def construct_surface_legend_string(row): + surface_legend = "" + surface_legend += row.assignment + return surface_legend + + def create_plotly_surfaces(plot_data: PlottingDataForPlane): data = [] color_scale_values = np.linspace(0, 1, len(plot_data.single_colors)) @@ -1180,7 +1225,12 @@ def create_plotly_surfaces(plot_data: PlottingDataForPlane): [val, f"rgb({', '.join('%d'%(i*255) for i in c[0:3])})"] for val, c in zip(color_scale_values, plot_data.single_colors) ] - for val, individual_peak in zip(color_scale_values, plot_data.sim_data_singles): + for val, individual_peak, row in zip( + color_scale_values, + plot_data.sim_data_singles, + plot_data.plane_lineshape_parameters.itertuples(), + ): + name = construct_surface_legend_string(row) colors = np.zeros(shape=individual_peak.shape) + val data.append( go.Surface( @@ -1193,20 +1243,60 @@ def create_plotly_surfaces(plot_data: PlottingDataForPlane): showscale=False, cmin=0, cmax=1, + name=name, ) ) return data +def create_residual_contours(plot_data: PlottingDataForPlane): + data = [] + contours = go.Contour(x=plot_data.x_plot, y=plot_data.y_plot, z=plot_data.residual) + data.append(contours) + return data + + def create_plotly_figure(plot_data: PlottingDataForPlane): lines = create_plotly_wireframe_lines(plot_data) surfaces = create_plotly_surfaces(plot_data) + # residuals = create_residual_contours(plot_data) fig = go.Figure(data=lines + surfaces) - layout = go.Layout(showlegend=False) - fig.update_layout(layout) + # layout = go.Layout(showlegend=True) + # fig.update_layout(layout) + # fig.update_traces(showlegend=True) + fig = update_axis_ranges(fig, plot_data) + return fig + + +def update_axis_ranges(fig, plot_data: PlottingDataForPlane): + fig.update_layout( + scene=dict( + xaxis=dict(range=[plot_data.x_plot.max(), plot_data.x_plot.min()]), + yaxis=dict(range=[plot_data.y_plot.max(), plot_data.y_plot.min()]), + annotations=make_annotations(plot_data), + ) + ) return fig +def make_annotations(plot_data: PlottingDataForPlane): + annotations = [] + for row in plot_data.plane_lineshape_parameters.itertuples(): + annotations.append( + dict( + showarrow=True, + x=row.center_x_ppm, + y=row.center_y_ppm, + z=row.height * 1.0, + text=row.assignment, + opacity=0.8, + textangle=0, + arrowsize=1, + ) + ) + return annotations + + class FitDataModel(BaseModel): plane: int clustid: int @@ -1334,7 +1424,7 @@ def check( data_color, fit_color = unpack_plotting_colors(colors) fits = get_fit_data_for_selected_peak_clusters(fits, clusters) - peak_clusters = fits.groupby("clustid") + peak_clusters = fits.query(f"plane=={selected_plane}").groupby("clustid") # make plotting meshes x = np.arange(pseudo3D.f2_size) diff --git a/poetry.lock b/poetry.lock index 451b681a..ad256b94 100644 --- a/poetry.lock +++ b/poetry.lock @@ -134,13 +134,13 @@ xyzservices = ">=2021.09.1" [[package]] name = "certifi" -version = "2023.11.17" +version = "2024.2.2" description = "Python package for providing Mozilla's CA Bundle." optional = false python-versions = ">=3.6" files = [ - {file = "certifi-2023.11.17-py3-none-any.whl", hash = "sha256:e036ab49d5b79556f99cfc2d9320b34cfbe5be05c5871b51de9329f0603b0474"}, - {file = "certifi-2023.11.17.tar.gz", hash = "sha256:9b469f3a900bf28dc19b8cfbf8019bf47f7fdd1a65a1d4ffb98fc14166beb4d1"}, + {file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"}, + {file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"}, ] [[package]] @@ -438,60 +438,60 @@ typing = ["typing-extensions (>=4.8)"] [[package]] name = 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peakipy/cli/main.py | 3 +- test/test_fit.py | 91 +++++++++++++++++++++++++++ 4 files changed, 184 insertions(+), 65 deletions(-) create mode 100644 test/test_fit.py diff --git a/peakipy/cli/edit.py b/peakipy/cli/edit.py index 6f5a5859..172dab4f 100644 --- a/peakipy/cli/edit.py +++ b/peakipy/cli/edit.py @@ -713,12 +713,14 @@ def fit_selected(self, event): print(f"[yellow]Using LS = {lineshape}[/yellow]") if self.checkbox_group.active == []: fit_command = f"peakipy fit {self.TEMP_INPUT_CSV} {self.data_path} {self.TEMP_OUT_CSV} --lineshape {lineshape}{fix_command}{reference_planes_command}{initial_fit_threshold_command}{xy_bounds_command}" - plot_command = f"peakipy check {self.TEMP_OUT_CSV} {self.data_path} --label --individual --show --outname {self.TEMP_OUT_PLOT / Path('tmp.pdf')}" + # plot_command = f"peakipy check {self.TEMP_OUT_CSV} {self.data_path} --label --individual --show --outname {self.TEMP_OUT_PLOT / Path('tmp.pdf')}" + plot_command = f"peakipy-check {self.TEMP_OUT_CSV} {self.data_path}" else: plane_index = self.select_plane.value print(f"[yellow]Only fitting plane {plane_index}[/yellow]") fit_command = f"peakipy fit {self.TEMP_INPUT_CSV} {self.data_path} {self.TEMP_OUT_CSV} --lineshape {lineshape} --plane {plane_index}{fix_command}{reference_planes_command}{initial_fit_threshold_command}{xy_bounds_command}" - plot_command = f"peakipy check {self.TEMP_OUT_CSV} {self.data_path} --label --individual --show --outname {self.TEMP_OUT_PLOT / Path('tmp.pdf')} --plane {plane_index}" + # plot_command = f"peakipy check {self.TEMP_OUT_CSV} {self.data_path} --label --individual --outname {self.TEMP_OUT_PLOT / Path('tmp.pdf')} --plane {plane_index} --show" + plot_command = f"peakipy-check {self.TEMP_OUT_CSV} {self.data_path}" print(f"[blue]{fit_command}[/blue]") self.fit_reports += fit_command + "
" diff --git a/peakipy/cli/fit.py b/peakipy/cli/fit.py index 86544149..678bfaa6 100644 --- a/peakipy/cli/fit.py +++ b/peakipy/cli/fit.py @@ -3,13 +3,13 @@ """ from pathlib import Path from typing import Optional, List -from multiprocessing import cpu_count, Pool import numpy as np import pandas as pd from rich import print from rich.console import Console +from pydantic import BaseModel from peakipy.core import ( fix_params, @@ -30,62 +30,6 @@ column_selection = ["INDEX", "ASS", "X_PPM", "Y_PPM", "CLUSTID", "MEMCNT"] -def check_xybounds(x): - x = x.split(",") - if len(x) == 2: - # xy_bounds = float(x[0]), float(x[1]) - xy_bounds = [float(i) for i in x] - return xy_bounds - else: - print("[red]🤔 xy_bounds must be pair of floats e.g. --xy_bounds=0.05,0.5[/red]") - exit() - - -# prepare data for multiprocessing - - -def chunks(l, n): - """split list into n chunks - - will return empty lists if n > len(l) - - :param l: list of values you wish to split - :type l: list - :param n: number of sub lists you want to generate - :type n: int - - :returns sub_lists: list of lists - :rtype sub_lists: list - """ - # create n empty lists - sub_lists = [[] for _ in range(n)] - # append into n lists - for num, i in enumerate(l): - sub_lists[num % n].append(i) - return sub_lists - - -def split_peaklist(peaklist, n_cpu, tmp_path=tmp_path): - """split peaklist into smaller files based on number of cpus - - :param peaklist: Peaklist data generated by peakipy read or edit scripts - :type peaklist: pandas.DataFrame - - :returns tmp_path: Temporary directory path - :rtype tmp_path: pathlib.Path - """ - # clustid numbers - clustids = peaklist.CLUSTID.unique() - # make n_cpu lists of clusters - clustids = chunks(clustids, n_cpu) - for i in range(n_cpu): - # get sub dataframe containing ith clustid list - split_peaks = peaklist[peaklist.CLUSTID.isin(clustids[i])] - # save sub dataframe - split_peaks.to_csv(tmp_path / f"peaks_{i}.csv", index=False) - return tmp_path - - class FitPeaksInput: """input data for the fit_peaks function""" @@ -176,6 +120,93 @@ def log(self): return self._log +class FitPeaksResultDfRow(BaseModel): + fit_prefix: str + assignment: str + amp: float + amp_err: float + center_x: float + init_center_x: float + center_y: float + init_center_y: float + sigma_x: float + sigma_y: float + clustid: int + memcnt: int + plane: int + x_radius: float + y_radius: float + x_radius_ppm: float + y_radius_ppm: float + lineshape: str + aic: float + chisqr: float + redchi: float + residual_sum: float + height: float + height_err: float + fwhm_x: float + fwhm_y: float + center_x_ppm: float + center_y_ppm: float + init_center_x_ppm: float + init_center_y_ppm: float + sigma_x_ppm: float + sigma_y_ppm: float + fwhm_x_ppm: float + fwhm_y_ppm: float + fwhm_x_hz: float + fwhm_y_hz: float + + +class FitPeaksResultRowGLPV(FitPeaksResultDfRow): + fraction: float + + +class FitPeaksResultRowPVPV(FitPeaksResultDfRow): + fraction_x: float # for PV_PV model + fraction_y: float # for PV_PV model + + +class FitPeaksResultRowVoigt(FitPeaksResultDfRow): + gamma_x_ppm: float # for voigt + gamma_y_ppm: float # for voigt + + +def get_fit_peaks_result_validation_model(lineshape): + match lineshape: + case lineshape.V: + validation_model = FitPeaksResultRowVoigt + case lineshape.PV_PV: + validation_model = FitPeaksResultRowPVPV + case _: + validation_model = FitPeaksResultRowGLPV + return validation_model + + +def filter_peak_clusters_by_max_cluster_size(grouped_peak_clusters, max_cluster_size): + filtered_peak_clusters = grouped_peak_clusters.filter( + lambda x: len(x) <= max_cluster_size + ) + return filtered_peak_clusters + + +def set_parameters_to_fix_during_fit(first_plane_fit_params, to_fix): + # fix sigma center and fraction parameters + # could add an option to select params to fix + match to_fix: + case None | () | []: + float_str = "Floating all parameters" + parameter_set = first_plane_fit_params + case ["None"] | ["none"]: + float_str = "Floating all parameters" + parameter_set = first_plane_fit_params + case _: + float_str = f"Fixing parameters: {to_fix}" + parameter_set = fix_params(first_plane_fit_params, to_fix) + return parameter_set, float_str + + def fit_peaks(peaks: pd.DataFrame, fit_input: FitPeaksInput) -> FitPeaksResult: """Fit set of peak clusters to lineshape model @@ -188,14 +219,10 @@ def fit_peaks(peaks: pd.DataFrame, fit_input: FitPeaksInput) -> FitPeaksResult: :returns: Data structure containing pd.DataFrame with the fitted results and a log :rtype: FitPeaksResult """ - # sum planes for initial fit - summed_planes = fit_input.data.sum(axis=0) - # group peaks based on CLUSTID groups = peaks.groupby("CLUSTID") # setup arguments to_fix = fit_input.args.get("to_fix") - # console.print(to_fix, style="red bold") noise = fit_input.args.get("noise") verb = fit_input.args.get("verb") initial_fit_threshold = fit_input.args.get("initial_fit_threshold") diff --git a/peakipy/cli/main.py b/peakipy/cli/main.py index 8d0f1f37..f4ff51eb 100644 --- a/peakipy/cli/main.py +++ b/peakipy/cli/main.py @@ -25,7 +25,7 @@ from dataclasses import dataclass, field from enum import Enum from typing import Optional, Tuple, List, Annotated -from multiprocessing import Pool +from multiprocessing import Pool, cpu_count import typer import numpy as np @@ -72,7 +72,6 @@ deal_with_peaks_on_edge_of_spectrum, ) from .fit import ( - cpu_count, fit_peaks, FitPeaksInput, ) diff --git a/test/test_fit.py b/test/test_fit.py new file mode 100644 index 00000000..517a8462 --- /dev/null +++ b/test/test_fit.py @@ -0,0 +1,91 @@ +from pytest import fixture + +import pandas as pd +import numpy as np +from lmfit import Parameters + +from peakipy.cli.fit import ( + get_fit_peaks_result_validation_model, + FitPeaksResultRowPVPV, + FitPeaksResultRowVoigt, + FitPeaksResultRowGLPV, + filter_peak_clusters_by_max_cluster_size, + set_parameters_to_fix_during_fit, +) +from peakipy.core import Lineshape + + +def test_get_fit_peaks_result_validation_model_PVPV(): + validation_model = get_fit_peaks_result_validation_model(Lineshape.PV_PV) + assert validation_model == FitPeaksResultRowPVPV + + +def test_get_fit_peaks_result_validation_model_G(): + validation_model = get_fit_peaks_result_validation_model(Lineshape.G) + assert validation_model == FitPeaksResultRowGLPV + + +def test_get_fit_peaks_result_validation_model_L(): + validation_model = get_fit_peaks_result_validation_model(Lineshape.L) + assert validation_model == FitPeaksResultRowGLPV + + +def test_get_fit_peaks_result_validation_model_PV(): + validation_model = get_fit_peaks_result_validation_model(Lineshape.PV) + assert validation_model == FitPeaksResultRowGLPV + + +def test_get_fit_peaks_result_validation_model_V(): + validation_model = get_fit_peaks_result_validation_model(Lineshape.V) + assert validation_model == FitPeaksResultRowVoigt + + +def test_filter_groups_by_max_cluster_size(): + groups = pd.DataFrame( + dict( + col1=[1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 4, 5, 6, 7], + col2=[1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7], + ) + ).groupby("col1") + max_cluster_size = 3 + filtered_groups = filter_peak_clusters_by_max_cluster_size(groups, max_cluster_size) + filtered_group_names = filtered_groups.col1.unique() + expected_group_names = np.array([3, 4, 5, 6, 7]) + np.testing.assert_array_equal(filtered_group_names, expected_group_names) + + +def test_set_parameters_to_fix_during_fit(): + parameter_set = Parameters() + parameter_set.add("test1", vary=True) + modified_parameter_set, float_str = set_parameters_to_fix_during_fit( + parameter_set, ["test1"] + ) + assert modified_parameter_set["test1"].vary == False + + +def test_set_parameters_to_fix_during_fit_2(): + parameter_set = Parameters() + parameter_set.add("test1", vary=True) + parameter_set.add("test2", vary=True) + modified_parameter_set, float_str = set_parameters_to_fix_during_fit( + parameter_set, ["test1", "test2"] + ) + assert ( + modified_parameter_set["test2"].vary + == modified_parameter_set["test1"].vary + == False + ) + + +def test_set_parameters_to_fix_during_fit_3(): + parameter_set = Parameters() + parameter_set.add("test1", vary=True) + parameter_set.add("test2", vary=True) + modified_parameter_set, float_str = set_parameters_to_fix_during_fit( + parameter_set, ["test2"] + ) + assert ( + modified_parameter_set["test1"].vary + != modified_parameter_set["test2"].vary + == False + ) From f5debf472f90c15c9d4ac4c4499f3760ab08950d Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Mon, 19 Feb 2024 16:00:15 -0500 Subject: [PATCH 16/37] refactoring and adding unit tests --- peakipy/cli/fit.py | 461 ++++++++++++++++++++------------------------- test/test_fit.py | 275 ++++++++++++++++++++++++++- 2 files changed, 468 insertions(+), 268 deletions(-) diff --git a/peakipy/cli/fit.py b/peakipy/cli/fit.py index 678bfaa6..cc1dcb73 100644 --- a/peakipy/cli/fit.py +++ b/peakipy/cli/fit.py @@ -2,7 +2,8 @@ """Fit and deconvolute NMR peaks: Functions used for running peakipy fit """ from pathlib import Path -from typing import Optional, List +from typing import Optional, List, Tuple +from dataclasses import dataclass, field import numpy as np import pandas as pd @@ -10,12 +11,18 @@ from rich import print from rich.console import Console from pydantic import BaseModel +from lmfit import Model, Parameter +from lmfit.model import ModelResult from peakipy.core import ( fix_params, - get_params, fit_first_plane, LoadData, + Lineshape, + pvoigt2d, + voigt2d, + pv_pv, + to_prefix, ) console = Console() @@ -30,6 +37,25 @@ column_selection = ["INDEX", "ASS", "X_PPM", "Y_PPM", "CLUSTID", "MEMCNT"] +@dataclass +class FitPeaksArgs: + noise: float + dims: List[int] = field(default_factory=lambda: [0, 1, 2]) + colors: Tuple[str] = ("#5e3c99", "#e66101") + max_cluster_size: Optional[int] = None + to_fix: List[str] = field(default_factory=lambda: ["fraction", "sigma", "center"]) + xy_bounds: Tuple[float, float] = ((0, 0),) + vclist: Optional[Path] = (None,) + plane: Optional[List[int]] = (None,) + exclude_plane: Optional[List[int]] = (None,) + reference_plane_index: List[int] = ([],) + initial_fit_threshold: Optional[float] = (None,) + mp: bool = (True,) + plot: Optional[Path] = (None,) + show: bool = (False,) + verb: bool = (False,) + + class FitPeaksInput: """input data for the fit_peaks function""" @@ -207,6 +233,154 @@ def set_parameters_to_fix_during_fit(first_plane_fit_params, to_fix): return parameter_set, float_str +def get_default_lineshape_param_names(lineshape: Lineshape): + match lineshape: + case Lineshape.PV | Lineshape.G | Lineshape.L: + param_names = Model(pvoigt2d).param_names + case Lineshape.V: + param_names = Model(voigt2d).param_names + case Lineshape.PV_PV: + param_names = Model(pv_pv).param_names + return param_names + + +def split_parameter_sets_by_peak( + default_param_names: List, params: List[Tuple[str, Parameter]] +): + """params is a list of tuples where the first element of each tuple is a + prefixed parameter name and the second element is the corresponding + Parameter object. This is created by calling .items() on a Parameters + object + """ + number_of_fitted_parameters = len(params) + number_of_default_params = len(default_param_names) + number_of_fitted_peaks = int(number_of_fitted_parameters / number_of_default_params) + split_param_items = [ + params[i : (i + number_of_default_params)] + for i in range(0, number_of_fitted_parameters, number_of_default_params) + ] + assert len(split_param_items) == number_of_fitted_peaks + return split_param_items + + +def create_parameter_dict(prefix, parameters: List[Tuple[str, Parameter]]): + parameter_dict = dict(prefix=prefix) + parameter_dict.update({k.replace(prefix, ""): v.value for k, v in parameters}) + parameter_dict.update( + {f"{k.replace(prefix,'')}_stderr": v.stderr for k, v in parameters} + ) + return parameter_dict + + +def get_prefix_from_parameter_names( + default_param_names: List, parameters: List[Tuple[str, Parameter]] +): + prefixes = [ + param_key_val[0].replace(default_param_name, "") + for param_key_val, default_param_name in zip(parameters, default_param_names) + ] + assert len(set(prefixes)) == 1 + return prefixes[0] + + +def unpack_fitted_parameters_for_lineshape( + lineshape: Lineshape, params: List[dict], plane_number: int +): + default_param_names = get_default_lineshape_param_names(lineshape) + split_parameter_names = split_parameter_sets_by_peak(default_param_names, params) + prefixes = [ + get_prefix_from_parameter_names(default_param_names, i) + for i in split_parameter_names + ] + unpacked_params = [] + for parameter_names, prefix in zip(split_parameter_names, prefixes): + parameter_dict = create_parameter_dict(prefix, parameter_names) + parameter_dict.update({"plane": plane_number}) + unpacked_params.append(parameter_dict) + return unpacked_params + + +def perform_initial_lineshape_fit_on_cluster_of_peaks( + cluster_of_peaks, fit_input: FitPeaksInput +): + fit_result = fit_first_plane( + cluster_of_peaks, + fit_input.data, + # norm(summed_planes), + fit_input.args.get("uc_dics"), + lineshape=fit_input.args.get("lineshape"), + xy_bounds=fit_input.args.get("xy_bounds"), + verbose=fit_input.args.get("verb"), + noise=fit_input.args.get("noise"), + fit_method=fit_input.config.get("fit_method", "leastsq"), + reference_plane_indices=fit_input.args.get("reference_plane_indices"), + threshold=fit_input.args.get("initial_fit_threshold"), + ) + return fit_result + + +def refit_peaks_with_constraints(fit_input: FitPeaksInput, fit_result: FitPeaksResult): + fit_results = [] + for num, d in enumerate(fit_input.data): + plane_number = fit_input.plane_numbers[num] + fit_result.out.fit( + data=d[fit_result.mask], + params=fit_result.out.params, + weights=1.0 + / np.array( + [fit_input.args.get("noise")] * len(np.ravel(d[fit_result.mask])) + ), + ) + fit_results.extend( + unpack_fitted_parameters_for_lineshape( + fit_input.args.get("lineshape"), + list(fit_result.out.params.items()), + plane_number, + ) + ) + # fit_report = fit_result.out.fit_report() + # log.write( + return fit_results + + +def merge_unpacked_parameters_with_metadata(cluster_fit_df, group_of_peaks_df): + group_of_peaks_df["prefix"] = group_of_peaks_df.ASS.apply(to_prefix) + merged_cluster_fit_df = cluster_fit_df.merge(group_of_peaks_df, on="prefix") + return merged_cluster_fit_df + + +def update_cluster_df_with_fit_statistics(cluster_df, fit_result: ModelResult): + cluster_df["chisqr"] = fit_result.chisqr + cluster_df["redchi"] = fit_result.redchi + cluster_df["residual_sum"] = np.sum(fit_result.residual) + cluster_df["aic"] = fit_result.aic + cluster_df["bic"] = fit_result.bic + cluster_df["nfev"] = fit_result.nfev + cluster_df["ndata"] = fit_result.ndata + return cluster_df + + +def rename_columns_for_compatibility(df): + mapping = { + "amplitude": "amp", + "amplitude_stderr": "amp_err", + "X_AXIS": "init_center_x", + "Y_AXIS": "init_center_y", + "ASS": "assignment", + "MEMCNT": "memcnt", + "X_RADIUS": "x_radius", + "Y_RADIUS": "y_radius", + } + df = df.rename(columns=mapping) + return df + + +def add_vclist_to_df(fit_input: FitPeaksInput, df: pd.DataFrame): + vclist_data = fit_input.args.get("vclist_data") + df["vclist"] = df.plane.apply(lambda x: vclist_data[x]) + return df + + def fit_peaks(peaks: pd.DataFrame, fit_input: FitPeaksInput) -> FitPeaksResult: """Fit set of peak clusters to lineshape model @@ -219,266 +393,33 @@ def fit_peaks(peaks: pd.DataFrame, fit_input: FitPeaksInput) -> FitPeaksResult: :returns: Data structure containing pd.DataFrame with the fitted results and a log :rtype: FitPeaksResult """ - # group peaks based on CLUSTID - groups = peaks.groupby("CLUSTID") + peak_clusters = peaks.groupby("CLUSTID") + max_cluster_size = fit_input.args.get("max_cluster_size") + filtered_peaks = filter_peak_clusters_by_max_cluster_size( + peak_clusters, max_cluster_size + ) + peak_clusters = filtered_peaks.groupby("CLUSTID") # setup arguments to_fix = fit_input.args.get("to_fix") - noise = fit_input.args.get("noise") - verb = fit_input.args.get("verb") - initial_fit_threshold = fit_input.args.get("initial_fit_threshold") - reference_plane_indices = fit_input.args.get("reference_plane_indices") lineshape = fit_input.args.get("lineshape") - xy_bounds = fit_input.args.get("xy_bounds") - vclist = fit_input.args.get("vclist") - uc_dics = fit_input.args.get("uc_dics") - - # for saving data, currently not using errs for center and sigma - amps = [] - amp_errs = [] - - center_xs = [] - init_center_xs = [] - # center_x_errs = [] - - center_ys = [] - init_center_ys = [] - # center_y_errs = [] - - sigma_ys = [] - # sigma_y_errs = [] - - sigma_xs = [] - # sigma_x_errs = [] - - match lineshape: - case lineshape.V: - # lorentzian linewidth - gamma_xs = [] - gamma_ys = [] - fractions = [] - - case lineshape.PV_PV: - # seperate fractions for each dim - fractions_x = [] - fractions_y = [] - case _: - fractions = [] - - # lists for saving data - names = [] - assign = [] - clustids = [] - memcnts = [] - planes = [] - x_radii = [] - y_radii = [] - x_radii_ppm = [] - y_radii_ppm = [] - lineshapes = [] - # errors - chisqrs = [] - redchis = [] - aics = [] - res_sum = [] - - # iterate over groups of peaks out_str = "" - for name, group in groups: - #  max cluster size - len_group = len(group) - if len_group <= fit_input.args.get("max_cluster_size"): - if len_group == 1: - peak_str = "peak" - else: - peak_str = "peaks" - - out_str += f""" - - #################################### - Fitting cluster of {len_group} {peak_str} - #################################### - """ - # fits sum of all planes first - fit_result = fit_first_plane( - group, - fit_input.data, - # norm(summed_planes), - uc_dics, - lineshape=lineshape, - xy_bounds=xy_bounds, - verbose=verb, - noise=noise, - fit_method=fit_input.config.get("fit_method", "leastsq"), - reference_plane_indices=reference_plane_indices, - threshold=initial_fit_threshold, - ) - fit_result.plot( - plot_path=fit_input.args.get("plot"), - show=fit_input.args.get("show"), - mp=fit_input.args.get("mp"), - ) - # jack_knife_result = fit_result.jackknife() - # print("JackKnife", jack_knife_result.mean, jack_knife_result.std) - first = fit_result.out - mask = fit_result.mask - # log.write( - out_str += fit_result.fit_str - out_str += f""" - ------------------------------------ - Summed planes - ------------------------------------ - {first.fit_report()} - """ - # ) - # fix sigma center and fraction parameters - # could add an option to select params to fix - match to_fix: - case None | () | []: - float_str = "Floating all parameters" - case ["None"] | ["none"]: - float_str = "Floating all parameters" - case _: - float_str = f"Fixing parameters: {to_fix}" - fix_params(first.params, to_fix) - if verb: - console.print(float_str, style="magenta") - - out_str += float_str + "\n" - - for num, d in enumerate(fit_input.data): - plane_number = fit_input.plane_numbers[num] - first.fit( - data=d[mask], - params=first.params, - weights=1.0 / np.array([noise] * len(np.ravel(d[mask]))), - ) - fit_report = first.fit_report() - # log.write( - out_str += f""" - ------------------------------------ - Plane = {num+1} - ------------------------------------ - {fit_report} - """ - # ) - if verb: - console.print(fit_report, style="bold") - - amp, amp_err, name = get_params(first.params, "amplitude") - cen_x, cen_x_err, cx_name = get_params(first.params, "center_x") - cen_y, cen_y_err, cy_name = get_params(first.params, "center_y") - sig_x, sig_x_err, sx_name = get_params(first.params, "sigma_x") - sig_y, sig_y_err, sy_name = get_params(first.params, "sigma_y") - # currently chi square is calculated for all peaks in cluster (not individual peaks) - # chi2 - residual sum of squares - chisqrs.extend([first.chisqr for _ in sy_name]) - # reduced chi2 - redchis.extend([first.redchi for _ in sy_name]) - # Akaike Information criterion - aics.extend([first.aic for _ in sy_name]) - # residual sum of squares - res_sum.extend([np.sum(first.residual) for _ in sy_name]) - - # deal with lineshape specific parameters - match lineshape: - case lineshape.PV_PV: - frac_x, frac_err_x, name = get_params( - first.params, "fraction_x" - ) - frac_y, frac_err_y, name = get_params( - first.params, "fraction_y" - ) - fractions_x.extend(frac_x) - fractions_y.extend(frac_y) - case lineshape.V: - frac, frac_err, name = get_params(first.params, "fraction") - gam_x, gam_x_err, gx_name = get_params(first.params, "gamma_x") - gam_y, gam_y_err, gy_name = get_params(first.params, "gamma_y") - gamma_xs.extend(gam_x) - gamma_ys.extend(gam_y) - fractions.extend(frac) - case _: - frac, frac_err, name = get_params(first.params, "fraction") - fractions.extend(frac) - - # extend lists with fit data - amps.extend(amp) - amp_errs.extend(amp_err) - center_xs.extend(cen_x) - init_center_xs.extend(group.X_AXISf) - # center_x_errs.extend(cen_x_err) - center_ys.extend(cen_y) - init_center_ys.extend(group.Y_AXISf) - # center_y_errs.extend(cen_y_err) - sigma_xs.extend(sig_x) - # sigma_x_errs.extend(sig_x_err) - sigma_ys.extend(sig_y) - # sigma_y_errs.extend(sig_y_err) - # add plane number, this should map to vclist - planes.extend([plane_number for _ in amp]) - lineshapes.extend([lineshape.value for _ in amp]) - #  get prefix for fit - names.extend([first.model.prefix] * len(name)) - assign.extend(group["ASS"]) - clustids.extend(group["CLUSTID"]) - memcnts.extend(group["MEMCNT"]) - x_radii.extend(group["X_RADIUS"]) - y_radii.extend(group["Y_RADIUS"]) - x_radii_ppm.extend(group["X_RADIUS_PPM"]) - y_radii_ppm.extend(group["Y_RADIUS_PPM"]) - - df_dic = { - "fit_prefix": names, - "assignment": assign, - "amp": amps, - "amp_err": amp_errs, - # "height": heights, - # "height_err": height_errs, - "center_x": center_xs, - "init_center_x": init_center_xs, - # "center_x_err": center_x_errs, - "center_y": center_ys, - "init_center_y": init_center_ys, - # "center_y_err": center_y_errs, - "sigma_x": sigma_xs, - # "sigma_x_err": sigma_x_errs, - "sigma_y": sigma_ys, - # "sigma_y_err": sigma_y_errs, - "clustid": clustids, - "memcnt": memcnts, - "plane": planes, - "x_radius": x_radii, - "y_radius": y_radii, - "x_radius_ppm": x_radii_ppm, - "y_radius_ppm": y_radii_ppm, - "lineshape": lineshapes, - "aic": aics, - "chisqr": chisqrs, - "redchi": redchis, - "residual_sum": res_sum, - # "slope": slopes, - # "intercept": intercepts - } - - # lineshape specific - match lineshape: - case lineshape.PV_PV: - df_dic["fraction_x"] = fractions_x - df_dic["fraction_y"] = fractions_y - case lineshape.V: - df_dic["gamma_x"] = gamma_xs - df_dic["gamma_y"] = gamma_ys - df_dic["fraction"] = fractions - case _: - df_dic["fraction"] = fractions - - #  make dataframe - df = pd.DataFrame(df_dic) - # Fill nan values - df.fillna(value=np.nan, inplace=True) - # vclist - if vclist: - vclist_data = fit_input.args.get("vclist_data") - df["vclist"] = df.plane.apply(lambda x: vclist_data[x]) - #  output data + cluster_dfs = [] + for name, peak_cluster in peak_clusters: + fit_result = perform_initial_lineshape_fit_on_cluster_of_peaks( + peak_cluster, fit_input + ) + fit_result.out.params, float_str = set_parameters_to_fix_during_fit( + fit_result.out.params, to_fix + ) + fit_results = refit_peaks_with_constraints(fit_input, fit_result) + cluster_df = pd.DataFrame(fit_results) + cluster_df = update_cluster_df_with_fit_statistics(cluster_df, fit_result.out) + cluster_df["clustid"] = name + cluster_df = merge_unpacked_parameters_with_metadata(cluster_df, peak_cluster) + cluster_dfs.append(cluster_df) + df = pd.concat(cluster_dfs, ignore_index=True) + df["lineshape"] = lineshape.value + if fit_input.args.get("vclist"): + df = add_vclist_to_df(fit_input, df) + df = rename_columns_for_compatibility(df) return FitPeaksResult(df=df, log=out_str) diff --git a/test/test_fit.py b/test/test_fit.py index 517a8462..113b0339 100644 --- a/test/test_fit.py +++ b/test/test_fit.py @@ -2,7 +2,8 @@ import pandas as pd import numpy as np -from lmfit import Parameters +from lmfit import Parameters, Model +from lmfit.model import ModelResult from peakipy.cli.fit import ( get_fit_peaks_result_validation_model, @@ -11,8 +12,15 @@ FitPeaksResultRowGLPV, filter_peak_clusters_by_max_cluster_size, set_parameters_to_fix_during_fit, + unpack_fitted_parameters_for_lineshape, + get_default_lineshape_param_names, + split_parameter_sets_by_peak, + get_prefix_from_parameter_names, + create_parameter_dict, + perform_initial_lineshape_fit_on_cluster_of_peaks, + merge_unpacked_parameters_with_metadata, ) -from peakipy.core import Lineshape +from peakipy.core import Lineshape, pvoigt2d def test_get_fit_peaks_result_validation_model_PVPV(): @@ -63,16 +71,21 @@ def test_set_parameters_to_fix_during_fit(): assert modified_parameter_set["test1"].vary == False -def test_set_parameters_to_fix_during_fit_2(): +@fixture +def parameters_set_with_two_variables(): parameter_set = Parameters() - parameter_set.add("test1", vary=True) - parameter_set.add("test2", vary=True) + parameter_set.add("prefix1_test1", vary=True) + parameter_set.add("prefix1_test2", vary=True) + return parameter_set + + +def test_set_parameters_to_fix_during_fit_2(parameters_set_with_two_variables): modified_parameter_set, float_str = set_parameters_to_fix_during_fit( - parameter_set, ["test1", "test2"] + parameters_set_with_two_variables, ["prefix1_test1", "prefix1_test2"] ) assert ( - modified_parameter_set["test2"].vary - == modified_parameter_set["test1"].vary + modified_parameter_set["prefix1_test2"].vary + == modified_parameter_set["prefix1_test1"].vary == False ) @@ -89,3 +102,249 @@ def test_set_parameters_to_fix_during_fit_3(): != modified_parameter_set["test2"].vary == False ) + + +def test_get_default_param_names_pseudo_voigt(): + assert get_default_lineshape_param_names(Lineshape.PV) == [ + "amplitude", + "center_x", + "center_y", + "sigma_x", + "sigma_y", + "fraction", + ] + + +def test_get_default_param_names_gaussian(): + assert get_default_lineshape_param_names(Lineshape.G) == [ + "amplitude", + "center_x", + "center_y", + "sigma_x", + "sigma_y", + "fraction", + ] + + +def test_get_default_param_names_lorentzian(): + assert get_default_lineshape_param_names(Lineshape.L) == [ + "amplitude", + "center_x", + "center_y", + "sigma_x", + "sigma_y", + "fraction", + ] + + +def test_get_default_param_names_pv_pv(): + assert get_default_lineshape_param_names(Lineshape.PV_PV) == [ + "amplitude", + "center_x", + "center_y", + "sigma_x", + "sigma_y", + "fraction_x", + "fraction_y", + ] + + +def test_get_default_param_names_voigt(): + assert get_default_lineshape_param_names(Lineshape.V) == [ + "amplitude", + "center_x", + "center_y", + "sigma_x", + "sigma_y", + "gamma_x", + "gamma_y", + "fraction", + ] + + +def test_split_parameter_sets_by_peak(default_pseudo_voigt_parameter_names): + # the second element of each tuple actually contains an + # lmfit.Parameter object + params = [ + ("p1_amplitude", "amplitude"), + ("p1_center_x", "center_x"), + ("p1_center_y", "center_y"), + ("p1_sigma_x", "sigma_x"), + ("p1_sigma_y", "sigma_y"), + ("p1_fraction", "fraction"), + ("p2_amplitude", "amplitude"), + ("p2_center_x", "center_x"), + ("p2_center_y", "center_y"), + ("p2_sigma_x", "sigma_x"), + ("p2_sigma_y", "sigma_y"), + ("p2_fraction", "fraction"), + ("p3_amplitude", "amplitude"), + ("p3_center_x", "center_x"), + ("p3_center_y", "center_y"), + ("p3_sigma_x", "sigma_x"), + ("p3_sigma_y", "sigma_y"), + ("p3_fraction", "fraction"), + ] + expected_result = [ + [ + ("p1_amplitude", "amplitude"), + ("p1_center_x", "center_x"), + ("p1_center_y", "center_y"), + ("p1_sigma_x", "sigma_x"), + ("p1_sigma_y", "sigma_y"), + ("p1_fraction", "fraction"), + ], + [ + ("p2_amplitude", "amplitude"), + ("p2_center_x", "center_x"), + ("p2_center_y", "center_y"), + ("p2_sigma_x", "sigma_x"), + ("p2_sigma_y", "sigma_y"), + ("p2_fraction", "fraction"), + ], + [ + ("p3_amplitude", "amplitude"), + ("p3_center_x", "center_x"), + ("p3_center_y", "center_y"), + ("p3_sigma_x", "sigma_x"), + ("p3_sigma_y", "sigma_y"), + ("p3_fraction", "fraction"), + ], + ] + expected_result_parameter_names = [[j[0] for j in i] for i in expected_result] + split_parameter_names = [ + [j[0] for j in i] + for i in split_parameter_sets_by_peak( + default_pseudo_voigt_parameter_names, params + ) + ] + assert split_parameter_names == expected_result_parameter_names + + +@fixture +def default_pseudo_voigt_parameter_names(): + return Model(pvoigt2d).param_names + + +def test_get_prefix_from_parameter_names(default_pseudo_voigt_parameter_names): + parameter_items_with_prefixes = [ + ("p1_amplitude", "amplitude"), + ("p1_center_x", "center_x"), + ("p1_center_y", "center_y"), + ("p1_sigma_x", "sigma_x"), + ("p1_sigma_y", "sigma_y"), + ("p1_fraction", "fraction"), + ] + expected_result = "p1_" + actual_result = get_prefix_from_parameter_names( + default_pseudo_voigt_parameter_names, parameter_items_with_prefixes + ) + assert expected_result == actual_result + + +@fixture +def pseudo_voigt_model_result(): + m1 = Model(pvoigt2d, prefix="p1_") + m2 = Model(pvoigt2d, prefix="p2_") + model = m1 + m2 + params = model.make_params() + model_result = ModelResult(model, params) + return model_result + + +def test_create_parameter_dict(pseudo_voigt_model_result): + prefix = "p1_" + params = list(pseudo_voigt_model_result.params.items())[:6] + expected_result = dict( + prefix="p1_", + amplitude=1.0, + amplitude_stderr=None, + center_x=0.5, + center_x_stderr=None, + center_y=0.5, + center_y_stderr=None, + sigma_x=1.0, + sigma_x_stderr=None, + sigma_y=1.0, + sigma_y_stderr=None, + fraction=0.5, + fraction_stderr=None, + ) + actual_result = create_parameter_dict(prefix, params) + assert expected_result == actual_result + + +def test_unpack_fitted_parameters_for_lineshape_PV(pseudo_voigt_model_result): + expected_params = [ + dict( + prefix="p1_", + plane=0, + amplitude=1.0, + amplitude_stderr=None, + center_x=0.5, + center_x_stderr=None, + center_y=0.5, + center_y_stderr=None, + sigma_x=1.0, + sigma_x_stderr=None, + sigma_y=1.0, + sigma_y_stderr=None, + fraction=0.5, + fraction_stderr=None, + ), + dict( + prefix="p2_", + plane=0, + amplitude=1.0, + amplitude_stderr=None, + center_x=0.5, + center_x_stderr=None, + center_y=0.5, + center_y_stderr=None, + sigma_x=1.0, + sigma_x_stderr=None, + sigma_y=1.0, + sigma_y_stderr=None, + fraction=0.5, + fraction_stderr=None, + ), + ] + unpacked_params = unpack_fitted_parameters_for_lineshape( + Lineshape.PV, list(pseudo_voigt_model_result.params.items()), plane_number=0 + ) + assert expected_params == unpacked_params + + +def test_merge_unpacked_parameters_with_metadata(): + cluster_fit_df = pd.DataFrame( + dict( + plane=[0, 1, 2, 3, 0, 1, 2, 3], + prefix=["_p1_", "_p1_", "_p1_", "_p1_", "_p2_", "_p2_", "_p2_", "_p2_"], + ) + ) + peak_df = pd.DataFrame(dict(ASS=["p1", "p2"], data=["p1_data", "p2_data"])) + expected_result = pd.DataFrame( + dict( + plane=[0, 1, 2, 3, 0, 1, 2, 3], + prefix=["_p1_", "_p1_", "_p1_", "_p1_", "_p2_", "_p2_", "_p2_", "_p2_"], + ASS=["p1", "p1", "p1", "p1", "p2", "p2", "p2", "p2"], + data=[ + "p1_data", + "p1_data", + "p1_data", + "p1_data", + "p2_data", + "p2_data", + "p2_data", + "p2_data", + ], + ) + ) + actual_result = merge_unpacked_parameters_with_metadata(cluster_fit_df, peak_df) + assert expected_result.equals(actual_result) + + +# def test_perform_initial_lineshape_fit_on_cluster_of_peaks(pseudo_voigt_model_result): +# expected_result = pseudo_voigt_model_result +# actual_result = perform_initial_lineshape_fit_on_cluster_of_peaks() +# assert expected_result == actual_result From 13d53bc0e079ab1a6d900078e9d78f3acd2a5058 Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Mon, 19 Feb 2024 16:02:24 -0500 Subject: [PATCH 17/37] refactoring and adding tests --- peakipy/cli/check_panel.py | 7 ------- peakipy/cli/main.py | 28 ++++++++++++++++++++++++++-- peakipy/core.py | 4 +++- test/test_cli.py | 23 +++++++++++++++++++++++ test/test_core.py | 15 ++++++++------- 5 files changed, 60 insertions(+), 17 deletions(-) diff --git a/peakipy/cli/check_panel.py b/peakipy/cli/check_panel.py index 60dd576d..aba815cc 100644 --- a/peakipy/cli/check_panel.py +++ b/peakipy/cli/check_panel.py @@ -10,10 +10,6 @@ pn.extension() pn.config.theme = "dark" -global fits_path -global data_path -global config_path - @dataclass class Data: @@ -114,7 +110,4 @@ def check_panel( if __name__ == "__main__": - # fits_path = Path("../../test/test_protein_L/fits.csv") - # data_path = Path("../../test/test_protein_L/test1.ft2") - # config_path = Path("../../test/test_protein_L/peakipy.config") app() diff --git a/peakipy/cli/main.py b/peakipy/cli/main.py index f4ff51eb..9ec1eb9e 100644 --- a/peakipy/cli/main.py +++ b/peakipy/cli/main.py @@ -670,8 +670,11 @@ def fit( ), axis=1, ) + print("VOIGT") + print(df[["amp", "amp_err", "height"]]) df["height_err"] = df.apply( - lambda x: x.amp_err * (x.height / x.amp), axis=1 + lambda x: x.amp_err * (x.height / x.amp) if x.amp_err != None else 0.0, + axis=1, ) df["fwhm_g_x"] = df.sigma_x.apply( lambda x: 2.0 * x * np.sqrt(2.0 * np.log(2.0)) @@ -1314,11 +1317,32 @@ class FitDataModel(BaseModel): center_y: float sigma_x: float sigma_y: float + + +class FitDataModelPVGL(FitDataModel): fraction: float +class FitDataModelVoigt(FitDataModel): + fraction: float + gamma_x: float + gamma_y: float + + +class FitDataModelPVPV(FitDataModel): + fraction_x: float + fraction_y: float + + def validate_fit_data(dict): - fit_data = FitDataModel(**dict) + lineshape = dict.get("lineshape") + if lineshape in ["PV", "G", "L"]: + fit_data = FitDataModelPVGL(**dict) + elif lineshape == "V": + fit_data = FitDataModelVoigt(**dict) + else: + fit_data = FitDataModelPVPV(**dict) + return fit_data.model_dump() diff --git a/peakipy/core.py b/peakipy/core.py index 14066107..4539507f 100644 --- a/peakipy/core.py +++ b/peakipy/core.py @@ -469,12 +469,14 @@ def get_params(params, name): ps = [] ps_err = [] names = [] + prefixes = [] for k in params: if name in k: ps.append(params[k].value) ps_err.append(params[k].stderr) names.append(k) - return ps, ps_err, names + prefixes.append(k.split(name)[0]) + return ps, ps_err, names, prefixes def make_param_dict(peaks, data, lineshape: Lineshape = Lineshape.PV): diff --git a/test/test_cli.py b/test/test_cli.py index a6a4e653..191f2fa3 100644 --- a/test/test_cli.py +++ b/test/test_cli.py @@ -81,6 +81,16 @@ def test_fit_main_with_voigt(protein_L): peakipy.cli.main.fit(**args) +def test_fit_main_with_pv_pv(protein_L): + args = dict( + peaklist_path=protein_L / Path("test.csv"), + data_path=protein_L / Path("test1.ft2"), + output_path=protein_L / Path("fits_PV_PV.csv"), + lineshape=Lineshape.PV_PV, + ) + peakipy.cli.main.fit(**args) + + def test_check_main_with_default(protein_L): args = dict( fits=protein_L / Path("fits_PV.csv"), @@ -133,6 +143,19 @@ def test_check_main_with_voigt(protein_L): peakipy.cli.main.check(**args) +def test_check_main_with_pv_pv(protein_L): + args = dict( + fits=protein_L / Path("fits_PV_PV.csv"), + data_path=protein_L / Path("test1.ft2"), + clusters=[1], + first=True, + label=True, + show=False, + individual=True, + ) + peakipy.cli.main.check(**args) + + def test_edit_with_default(protein_L): args = dict( peaklist_path=protein_L / Path("peaks.csv"), diff --git a/test/test_core.py b/test/test_core.py index dd8a02a7..2af16bd9 100644 --- a/test/test_core.py +++ b/test/test_core.py @@ -179,19 +179,20 @@ def test_fix_params(self): self.assertEqual(pars["fraction"].vary, False) def test_get_params(self): - mod = Model(pvoigt2d) - pars = mod.make_params(center_x=20.0, center_y=30.0) - pars["center_x"].stderr = 1.0 - pars["center_y"].stderr = 2.0 - ps, ps_err, names = get_params(pars, "center") + mod = Model(pvoigt2d, prefix="p1_") + pars = mod.make_params(p1_center_x=20.0, p1_center_y=30.0) + pars["p1_center_x"].stderr = 1.0 + pars["p1_center_y"].stderr = 2.0 + ps, ps_err, names, prefixes = get_params(pars, "center") #  get index of values - cen_x = names.index("center_x") - cen_y = names.index("center_y") + cen_x = names.index("p1_center_x") + cen_y = names.index("p1_center_y") self.assertEqual(ps[cen_x], 20.0) self.assertEqual(ps[cen_y], 30.0) self.assertEqual(ps_err[cen_x], 1.0) self.assertEqual(ps_err[cen_y], 2.0) + self.assertEqual(prefixes[cen_y], "p1_") def test_make_param_dict(self): peaks = pd.DataFrame( From ea82927e67eb1992eb48b809c845090f87dec2a1 Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Mon, 19 Feb 2024 22:20:30 -0500 Subject: [PATCH 18/37] fixed bug for amplitude estimation after selecting reference planes. Need to fix for threshold selection too --- peakipy/core.py | 71 +++++++++++++++++++++++++++++++++++++++-------- test/test_core.py | 44 +++++++++++++++++++++++++++++ 2 files changed, 103 insertions(+), 12 deletions(-) diff --git a/peakipy/core.py b/peakipy/core.py index 4539507f..e40ac687 100644 --- a/peakipy/core.py +++ b/peakipy/core.py @@ -25,6 +25,7 @@ from pathlib import Path from typing import List, Optional from enum import Enum +from dataclasses import dataclass, field import numpy as np import nmrglue as ng @@ -479,6 +480,38 @@ def get_params(params, name): return ps, ps_err, names, prefixes +@dataclass +class PeakLimits: + peak: pd.DataFrame + data: np.array + min_x: int = field(init=False) + max_x: int = field(init=False) + min_y: int = field(init=False) + max_y: int = field(init=False) + + def __post_init__(self): + self.max_y = int(self.peak.Y_AXIS + self.peak.YW) + 1 + if self.max_y > self.data.shape[0]: + self.max_y = self.data.shape[0] + self.max_x = int(self.peak.X_AXIS + self.peak.XW) + 1 + if self.max_x > self.data.shape[1]: + self.max_x = self.data.shape[1] + + self.min_y = int(self.peak.Y_AXIS - self.peak.YW) + if self.min_y < 0: + self.min_y = 0 + self.min_x = int(self.peak.X_AXIS - self.peak.XW) + if self.min_x < 0: + self.min_x = 0 + + +def estimate_amplitude(peak, data): + assert len(data.shape) == 2 + limits = PeakLimits(peak, data) + amplitude_est = data[limits.min_y : limits.max_y, limits.min_x : limits.max_x].sum() + return amplitude_est + + def make_param_dict(peaks, data, lineshape: Lineshape = Lineshape.PV): """Make dict of parameter names using prefix""" @@ -490,11 +523,7 @@ def make_param_dict(peaks, data, lineshape: Lineshape = Lineshape.PV): param_dict[str_form("center_x")] = peak.X_AXISf param_dict[str_form("center_y")] = peak.Y_AXISf # estimate peak volume - amplitude_est = data[ - int(peak.Y_AXIS) - int(peak.YW) : int(peak.Y_AXIS) + int(peak.YW) + 1, - int(peak.X_AXIS) - int(peak.XW) : int(peak.X_AXIS) + int(peak.XW) + 1, - ].sum() - + amplitude_est = estimate_amplitude(peak, data) param_dict[str_form("amplitude")] = amplitude_est # sigma linewidth esimate param_dict[str_form("sigma_x")] = peak.XW / 2.0 @@ -566,7 +595,11 @@ def to_prefix(x): def make_models( - model, peaks, data, lineshape: Lineshape = Lineshape.PV, xy_bounds=None + model, + peaks, + data, + lineshape: Lineshape = Lineshape.PV, + xy_bounds=None, ): """Make composite models for multiple peaks @@ -596,7 +629,11 @@ def make_models( # make model for first peak mod = Model(model, prefix="%s" % to_prefix(peaks.ASS.iloc[0])) # add parameters - param_dict = make_param_dict(peaks, data, lineshape=lineshape) + param_dict = make_param_dict( + peaks, + data, + lineshape=lineshape, + ) p_guess = mod.make_params(**param_dict) elif len(peaks) > 1: @@ -606,7 +643,11 @@ def make_models( for _, peak in remaining_peaks: mod += Model(model, prefix="%s" % to_prefix(peak.ASS)) - param_dict = make_param_dict(peaks, data, lineshape=lineshape) + param_dict = make_param_dict( + peaks, + data, + lineshape=lineshape, + ) p_guess = mod.make_params(**param_dict) # add Peak params to p_guess @@ -890,9 +931,6 @@ def fit_first_plane( """ lineshape_function = get_lineshape_function(lineshape) - mod, p_guess = make_models( - lineshape_function, group, data, lineshape=lineshape, xy_bounds=xy_bounds - ) first_plane_data = data[0] mask, peak = make_mask_from_peak_cluster(group, first_plane_data) @@ -909,7 +947,16 @@ def fit_first_plane( max_x, min_x, max_y, min_y = deal_with_peaks_on_edge_of_spectrum( data.shape, max_x, min_x, max_y, min_y ) - + selected_data = select_reference_planes_using_indices( + data, reference_plane_indices + ).sum(axis=0) + mod, p_guess = make_models( + lineshape_function, + group, + selected_data, + lineshape=lineshape, + xy_bounds=xy_bounds, + ) peak_slices = slice_peaks_from_data_using_mask(data, mask) peak_slices = select_reference_planes_using_indices( peak_slices, reference_plane_indices diff --git a/test/test_core.py b/test/test_core.py index 2af16bd9..2a0612c9 100644 --- a/test/test_core.py +++ b/test/test_core.py @@ -1,5 +1,6 @@ import unittest from unittest.mock import patch +from collections import namedtuple import numpy as np from numpy.testing import assert_array_equal @@ -24,6 +25,7 @@ select_reference_planes_using_indices, select_planes_above_threshold_from_masked_data, slice_peaks_from_data_using_mask, + estimate_amplitude, ) @@ -107,6 +109,48 @@ def test_select_planes_above_threshold_from_masked_data(): ) +def test_make_param_dict(): + selected_planes = [1, 2] + data = np.ones((4, 10, 5)) + expected_shape = (2, 10, 5) + actual_shape = data[np.array(selected_planes)].shape + assert expected_shape == actual_shape + + +def test_make_param_dict_sum(): + data = np.ones((4, 10, 5)) + expected_sum = 200 + actual_sum = data.sum() + assert expected_sum == actual_sum + + +def test_make_param_dict_selected(): + selected_planes = [1, 2] + data = np.ones((4, 10, 5)) + data = data[np.array(selected_planes)] + expected_sum = 100 + actual_sum = data.sum() + assert expected_sum == actual_sum + + +def test_estimate_amplitude(): + peak = namedtuple("peak", ["X_AXIS", "XW", "Y_AXIS", "YW"]) + p = peak(5, 2, 3, 2) + data = np.ones((20, 10)) + expected_result = 25 + actual_result = estimate_amplitude(p, data) + assert expected_result == actual_result + + +def test_estimate_amplitude_invalid_indices(): + peak = namedtuple("peak", ["X_AXIS", "XW", "Y_AXIS", "YW"]) + p = peak(1, 2, 3, 2) + data = np.ones((20, 10)) + expected_result = 20 + actual_result = estimate_amplitude(p, data) + assert expected_result == actual_result + + class TestCoreFunctions(unittest.TestCase): def test_make_mask(self): data = np.ones((10, 10)) From b07703e641cfd19f6293fb86651baf0236ffd6f7 Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Sat, 24 Feb 2024 22:29:16 -0500 Subject: [PATCH 19/37] major refactor --- peakipy/cli/fit.py | 414 +++++++++++++++++++++++---------- peakipy/cli/main.py | 551 ++++++++++++++++++++------------------------ peakipy/core.py | 532 +++++++++++------------------------------- pyproject.toml | 1 + test/test_main.py | 78 +++++++ 5 files changed, 754 insertions(+), 822 deletions(-) create mode 100644 test/test_main.py diff --git a/peakipy/cli/fit.py b/peakipy/cli/fit.py index cc1dcb73..4930c813 100644 --- a/peakipy/cli/fit.py +++ b/peakipy/cli/fit.py @@ -11,18 +11,25 @@ from rich import print from rich.console import Console from pydantic import BaseModel -from lmfit import Model, Parameter +from lmfit import Model, Parameter, Parameters from lmfit.model import ModelResult from peakipy.core import ( fix_params, - fit_first_plane, - LoadData, Lineshape, pvoigt2d, voigt2d, pv_pv, to_prefix, + get_limits_for_axis_in_points, + get_lineshape_function, + deal_with_peaks_on_edge_of_spectrum, + select_planes_above_threshold_from_masked_data, + select_reference_planes_using_indices, + make_models, + make_meshgrid, + slice_peaks_from_data_using_mask, + make_mask_from_peak_cluster, ) console = Console() @@ -40,6 +47,8 @@ @dataclass class FitPeaksArgs: noise: float + uc_dics: dict + lineshape: Lineshape dims: List[int] = field(default_factory=lambda: [0, 1, 2]) colors: Tuple[str] = ("#5e3c99", "#e66101") max_cluster_size: Optional[int] = None @@ -48,102 +57,77 @@ class FitPeaksArgs: vclist: Optional[Path] = (None,) plane: Optional[List[int]] = (None,) exclude_plane: Optional[List[int]] = (None,) - reference_plane_index: List[int] = ([],) + reference_plane_indices: List[int] = ([],) initial_fit_threshold: Optional[float] = (None,) mp: bool = (True,) - plot: Optional[Path] = (None,) - show: bool = (False,) - verb: bool = (False,) + verbose: bool = (False,) +@dataclass +class FirstPlaneFitInput: + group: pd.DataFrame + last_peak: pd.DataFrame + mask: np.array + mod: Model + p_guess: Parameters + XY: np.array + peak_slices: np.array + XY_slices: np.array + min_x: float + max_x: float + min_y: float + max_y: float + uc_dics: dict + first_plane_data: np.array + weights: np.array + fit_method: str = "leastsq" + verbose: bool = False + + +@dataclass class FitPeaksInput: """input data for the fit_peaks function""" - def __init__( - self, - args: dict, - data: np.array, - config: dict, - plane_numbers: list, - reference_planes_for_initial_fit: List[int] = [], - use_only_planes_above_threshold: Optional[float] = None, - ): - self._data = data - self._args = args - self._config = config - self._plane_numbers = plane_numbers - self._planes_for_initial_fit = reference_planes_for_initial_fit - self._use_only_planes_above_threshold = use_only_planes_above_threshold - - def check_integer_list(self): - if hasattr(self._planes_for_initial_fit, "append"): - pass - else: - return False - if all([(type(i) == int) for i in self._planes_for_initial_fit]): - pass - else: - return False - if all([((i - 1) > self._data.shape[0]) for i in self._planes_for_initial_fit]): - return True - else: - return False - - def sum_planes_for_initial_fit(self): - if ( - self._planes_for_initial_fit - == self._use_only_planes_above_threshold - == None - ): - return self._data.sum(axis=0) - - elif self.check_integer_list(): - return self._data[self._planes_for_initial_fit].sum(axis=0) - - elif type(self._use_only_planes_above_threshold) == float: - # very crude at the moment - return self._data[ - self._data.max(axis=1).max(axis=1) - > self._use_only_planes_above_threshold - ] - else: - return self._data.sum(axis=0) - - @property - def data(self): - return self._data - - @property - def args(self): - return self._args - - @property - def config(self): - return self._config - - @property - def plane_numbers(self): - return self._plane_numbers - - @property - def summed_planes_for_initial_fit(self): - return self.sum_planes_for_initial_fit() - + args: FitPeaksArgs + data: np.array + config: dict + plane_numbers: list -class FitPeaksResult: - """Result of fitting a set of peaks""" - def __init__(self, df: pd.DataFrame, log: str): - self._df = df - self._log = log +@dataclass +class FitResult: + out: ModelResult + mask: np.array + fit_str: str + log: str + group: pd.core.groupby.generic.DataFrameGroupBy + uc_dics: dict + min_x: float + min_y: float + max_x: float + max_y: float + X: np.array + Y: np.array + Z: np.array + Z_sim: np.array + peak_slices: np.array + XY_slices: np.array + weights: np.array + mod: Model + + def check_shifts(self): + """Calculate difference between initial peak positions + and check whether they moved too much from original + position + + """ + pass - @property - def df(self): - return self._df - @property - def log(self): - return self._log +@dataclass +class FitPeaksResult: + df: pd.DataFrame + log: str class FitPeaksResultDfRow(BaseModel): @@ -301,39 +285,76 @@ def unpack_fitted_parameters_for_lineshape( def perform_initial_lineshape_fit_on_cluster_of_peaks( - cluster_of_peaks, fit_input: FitPeaksInput -): - fit_result = fit_first_plane( - cluster_of_peaks, - fit_input.data, - # norm(summed_planes), - fit_input.args.get("uc_dics"), - lineshape=fit_input.args.get("lineshape"), - xy_bounds=fit_input.args.get("xy_bounds"), - verbose=fit_input.args.get("verb"), - noise=fit_input.args.get("noise"), - fit_method=fit_input.config.get("fit_method", "leastsq"), - reference_plane_indices=fit_input.args.get("reference_plane_indices"), - threshold=fit_input.args.get("initial_fit_threshold"), + first_plane_fit_input: FirstPlaneFitInput, +) -> FitResult: + mod = first_plane_fit_input.mod + peak_slices = first_plane_fit_input.peak_slices + XY_slices = first_plane_fit_input.XY_slices + p_guess = first_plane_fit_input.p_guess + weights = first_plane_fit_input.weights + fit_method = first_plane_fit_input.fit_method + mask = first_plane_fit_input.mask + XY = first_plane_fit_input.XY + X, Y = XY + first_plane_data = first_plane_fit_input.first_plane_data + peak = first_plane_fit_input.last_peak + group = first_plane_fit_input.group + min_x = first_plane_fit_input.min_x + min_y = first_plane_fit_input.min_y + max_x = first_plane_fit_input.max_x + max_y = first_plane_fit_input.max_y + verbose = first_plane_fit_input.verbose + uc_dics = first_plane_fit_input.uc_dics + + out = mod.fit( + peak_slices, XY=XY_slices, params=p_guess, weights=weights, method=fit_method + ) + + if verbose: + console.print(out.fit_report(), style="bold") + + z_sim = mod.eval(XY=XY, params=out.params) + z_sim[~mask] = np.nan + z_plot = first_plane_data.copy() + z_plot[~mask] = np.nan + fit_str = "" + log = "" + + return FitResult( + out=out, + mask=mask, + fit_str=fit_str, + log=log, + group=group, + uc_dics=uc_dics, + min_x=min_x, + min_y=min_y, + max_x=max_x, + max_y=max_y, + X=X, + Y=Y, + Z=z_plot, + Z_sim=z_sim, + peak_slices=peak_slices, + XY_slices=XY_slices, + weights=weights, + mod=mod, ) - return fit_result def refit_peaks_with_constraints(fit_input: FitPeaksInput, fit_result: FitPeaksResult): fit_results = [] for num, d in enumerate(fit_input.data): plane_number = fit_input.plane_numbers[num] + masked_data = d[fit_result.mask] fit_result.out.fit( - data=d[fit_result.mask], + data=masked_data, params=fit_result.out.params, - weights=1.0 - / np.array( - [fit_input.args.get("noise")] * len(np.ravel(d[fit_result.mask])) - ), + weights=fit_result.weights, ) fit_results.extend( unpack_fitted_parameters_for_lineshape( - fit_input.args.get("lineshape"), + fit_input.args.lineshape, list(fit_result.out.params.items()), plane_number, ) @@ -381,7 +402,94 @@ def add_vclist_to_df(fit_input: FitPeaksInput, df: pd.DataFrame): return df -def fit_peaks(peaks: pd.DataFrame, fit_input: FitPeaksInput) -> FitPeaksResult: +def prepare_group_of_peaks_for_fitting( + group, data, fit_peaks_input_args: FitPeaksArgs, fit_method="leastsq" +): + lineshape_function = get_lineshape_function(fit_peaks_input_args.lineshape) + + first_plane_data = data[0] + mask, peak = make_mask_from_peak_cluster(group, first_plane_data) + + x_radius = group.X_RADIUS.max() + y_radius = group.Y_RADIUS.max() + + max_x, min_x = get_limits_for_axis_in_points( + group_axis_points=group.X_AXISf, mask_radius_in_points=x_radius + ) + max_y, min_y = get_limits_for_axis_in_points( + group_axis_points=group.Y_AXISf, mask_radius_in_points=y_radius + ) + max_x, min_x, max_y, min_y = deal_with_peaks_on_edge_of_spectrum( + data.shape, max_x, min_x, max_y, min_y + ) + selected_data = select_reference_planes_using_indices( + data, fit_peaks_input_args.reference_plane_indices + ).sum(axis=0) + mod, p_guess = make_models( + lineshape_function, + group, + selected_data, + lineshape=fit_peaks_input_args.lineshape, + xy_bounds=fit_peaks_input_args.xy_bounds, + ) + peak_slices = slice_peaks_from_data_using_mask(data, mask) + peak_slices = select_reference_planes_using_indices( + peak_slices, fit_peaks_input_args.reference_plane_indices + ) + peak_slices = select_planes_above_threshold_from_masked_data( + peak_slices, fit_peaks_input_args.initial_fit_threshold + ) + peak_slices = peak_slices.sum(axis=0) + + XY = make_meshgrid(data.shape) + X, Y = XY + + XY_slices = np.array([X.copy()[mask], Y.copy()[mask]]) + weights = 1.0 / np.array([fit_peaks_input_args.noise] * len(np.ravel(peak_slices))) + # weights = 1.0 / np.ravel(peak_slices) + return FirstPlaneFitInput( + group=group, + last_peak=peak, + mask=mask, + mod=mod, + p_guess=p_guess, + XY=XY, + peak_slices=peak_slices, + XY_slices=XY_slices, + weights=weights, + fit_method=fit_method, + first_plane_data=first_plane_data, + uc_dics=fit_peaks_input_args.uc_dics, + min_x=min_x, + min_y=min_y, + max_x=max_x, + max_y=max_y, + verbose=fit_peaks_input_args.verbose, + ) + + +def fit_cluster_of_peaks( + clustid: int, peak_cluster: pd.DataFrame, fit_input: FitPeaksInput +) -> pd.DataFrame: + data_for_fitting = prepare_group_of_peaks_for_fitting( + peak_cluster, + fit_input.data, + fit_input.args, + fit_method=fit_input.config.get("fit_method", "leastsq"), + ) + fit_result = perform_initial_lineshape_fit_on_cluster_of_peaks(data_for_fitting) + fit_result.out.params, float_str = set_parameters_to_fix_during_fit( + fit_result.out.params, fit_input.args.to_fix + ) + fit_results = refit_peaks_with_constraints(fit_input, fit_result) + cluster_df = pd.DataFrame(fit_results) + cluster_df = update_cluster_df_with_fit_statistics(cluster_df, fit_result.out) + cluster_df["clustid"] = clustid + cluster_df = merge_unpacked_parameters_with_metadata(cluster_df, peak_cluster) + return cluster_df + + +def fit_peak_clusters(peaks: pd.DataFrame, fit_input: FitPeaksInput) -> FitPeaksResult: """Fit set of peak clusters to lineshape model :param peaks: peaklist with generated by peakipy read or edit @@ -394,32 +502,82 @@ def fit_peaks(peaks: pd.DataFrame, fit_input: FitPeaksInput) -> FitPeaksResult: :rtype: FitPeaksResult """ peak_clusters = peaks.groupby("CLUSTID") - max_cluster_size = fit_input.args.get("max_cluster_size") filtered_peaks = filter_peak_clusters_by_max_cluster_size( - peak_clusters, max_cluster_size + peak_clusters, fit_input.args.max_cluster_size ) peak_clusters = filtered_peaks.groupby("CLUSTID") - # setup arguments - to_fix = fit_input.args.get("to_fix") - lineshape = fit_input.args.get("lineshape") out_str = "" cluster_dfs = [] - for name, peak_cluster in peak_clusters: - fit_result = perform_initial_lineshape_fit_on_cluster_of_peaks( - peak_cluster, fit_input - ) - fit_result.out.params, float_str = set_parameters_to_fix_during_fit( - fit_result.out.params, to_fix + for clustid, peak_cluster in peak_clusters: + cluster_df = fit_cluster_of_peaks( + clustid=clustid, peak_cluster=peak_cluster, fit_input=fit_input ) - fit_results = refit_peaks_with_constraints(fit_input, fit_result) - cluster_df = pd.DataFrame(fit_results) - cluster_df = update_cluster_df_with_fit_statistics(cluster_df, fit_result.out) - cluster_df["clustid"] = name - cluster_df = merge_unpacked_parameters_with_metadata(cluster_df, peak_cluster) cluster_dfs.append(cluster_df) df = pd.concat(cluster_dfs, ignore_index=True) - df["lineshape"] = lineshape.value - if fit_input.args.get("vclist"): + df["lineshape"] = fit_input.args.lineshape.value + + if fit_input.args.vclist: df = add_vclist_to_df(fit_input, df) df = rename_columns_for_compatibility(df) return FitPeaksResult(df=df, log=out_str) + + +@dataclass +class JackKnifeResult: + mean: float + std: float + + +def jack_knife_sample_errors( + peaks: pd.DataFrame, fit_input: FirstPlaneFitInput +) -> JackKnifeResult: + peak_slices = fit_input.peak_slices + XY_slices = fit_input.XY_slices + weights = fit_input.weights + jk_results = [] + for i in range(len(peak_slices)): + peak_slices = np.delete(peak_slices, i, None) + X = np.delete(XY_slices[0], i, None) + Y = np.delete(XY_slices[1], i, None) + weights = np.delete(weights, i, None) + jk_results.append( + mod.fit(peak_slices, XY=[X, Y], params=out.params, weights=weights) + ) + + # print(jk_results) + amps = [] + sigma_xs = [] + sigma_ys = [] + names = [] + with open("test_jackknife", "w") as f: + for i in jk_results: + f.write(i.fit_report()) + amp, amp_err, name = get_params(i.params, "amp") + sigma_x, sigma_x_err, name_x = get_params(i.params, "sigma_x") + sigma_y, sigma_y_err, name_y = get_params(i.params, "sigma_y") + f.write(f"{amp},{amp_err},{name_y}\n") + amps.extend(amp) + names.extend(name_y) + sigma_xs.extend(sigma_x) + sigma_ys.extend(sigma_y) + + df = pd.DataFrame( + {"amp": amps, "name": names, "sigma_x": sigma_xs, "sigma_y": sigma_ys} + ) + grouped = df.groupby("name") + mean_amps = grouped.amp.mean() + std_amps = grouped.amp.std() + mean_sigma_x = grouped.sigma_x.mean() + std_sigma_x = grouped.sigma_x.std() + mean_sigma_y = grouped.sigma_y.mean() + std_sigma_y = grouped.sigma_y.std() + f.write("#####################################\n") + f.write( + f"{mean_amps}, {std_amps}, {mean_sigma_x}, {std_sigma_x}, {mean_sigma_y}, {std_sigma_y} " + ) + f.write(self.out.fit_report()) + f.write("#####################################\n") + # print(amps) + # mean = np.mean(amps) + # std = np.std(amps) + return JackKnifeResult(mean=mean_amps, std=std_amps) diff --git a/peakipy/cli/main.py b/peakipy/cli/main.py index 9ec1eb9e..4d4f44b0 100644 --- a/peakipy/cli/main.py +++ b/peakipy/cli/main.py @@ -70,10 +70,20 @@ OutFmt, get_limits_for_axis_in_points, deal_with_peaks_on_edge_of_spectrum, + calculate_fwhm_for_voigt_lineshape, + calculate_height_for_voigt_lineshape, + calculate_fwhm_for_pseudo_voigt_lineshape, + calculate_height_for_pseudo_voigt_lineshape, + calculate_height_for_gaussian_lineshape, + calculate_height_for_lorentzian_lineshape, + calculate_height_for_pv_pv_lineshape, + calculate_peak_centers_in_ppm, + calculate_peak_linewidths_in_hz, ) from .fit import ( - fit_peaks, + fit_peak_clusters, FitPeaksInput, + FitPeaksArgs, ) from .edit import BokehScript from .spec import yaml_file @@ -374,6 +384,218 @@ def read( ) +def calculate_lineshape_specific_height_and_fwhm( + lineshape: Lineshape, df: pd.DataFrame +): + match lineshape: + case lineshape.V: + df = calculate_height_for_voigt_lineshape(df) + df = calculate_fwhm_for_voigt_lineshape(df) + + case lineshape.PV: + df = calculate_height_for_pseudo_voigt_lineshape(df) + df = calculate_fwhm_for_pseudo_voigt_lineshape(df) + + case lineshape.G: + df = calculate_height_for_gaussian_lineshape(df) + df = calculate_fwhm_for_pseudo_voigt_lineshape(df) + + case lineshape.L: + df = calculate_height_for_lorentzian_lineshape(df) + df = calculate_fwhm_for_pseudo_voigt_lineshape(df) + + case lineshape.PV_PV: + df = calculate_height_for_pv_pv_lineshape(df) + df = calculate_fwhm_for_pseudo_voigt_lineshape(df) + case _: + df = calculate_fwhm_for_pseudo_voigt_lineshape(df) + return df + + +def get_vclist(vclist, args): + # read vclist + if vclist is None: + vclist = False + elif vclist.exists(): + vclist_data = np.genfromtxt(vclist) + args["vclist_data"] = vclist_data + vclist = True + else: + raise Exception("vclist not found...") + + args["vclist"] = vclist + return args + + +def check_data_shape_is_consistent_with_dims(peakipy_data): + # check data shape is consistent with dims + if len(peakipy_data.dims) != len(peakipy_data.data.shape): + print( + f"Dims are {peakipy_data.dims} while data shape is {peakipy_data.data.shape}?" + ) + exit() + + +def select_specified_planes(plane, peakipy_data): + plane_numbers = np.arange(peakipy_data.data.shape[peakipy_data.dims[0]]) + # only fit specified planes + if plane: + inds = [i for i in plane] + data_inds = [ + (i in inds) for i in range(peakipy_data.data.shape[peakipy_data.dims[0]]) + ] + plane_numbers = np.arange(peakipy_data.data.shape[peakipy_data.dims[0]])[ + data_inds + ] + peakipy_data.data = peakipy_data.data[data_inds] + print( + "[yellow]Using only planes {plane} data now has the following shape[/yellow]", + peakipy_data.data.shape, + ) + if peakipy_data.data.shape[peakipy_data.dims[0]] == 0: + print("[red]You have excluded all the data![/red]", peakipy_data.data.shape) + exit() + return plane_numbers, peakipy_data + + +def select_specified_planes(plane, peakipy_data): + plane_numbers = np.arange(peakipy_data.data.shape[peakipy_data.dims[0]]) + # only fit specified planes + if plane: + inds = [i for i in plane] + data_inds = [ + (i in inds) for i in range(peakipy_data.data.shape[peakipy_data.dims[0]]) + ] + plane_numbers = np.arange(peakipy_data.data.shape[peakipy_data.dims[0]])[ + data_inds + ] + peakipy_data.data = peakipy_data.data[data_inds] + print( + "[yellow]Using only planes {plane} data now has the following shape[/yellow]", + peakipy_data.data.shape, + ) + if peakipy_data.data.shape[peakipy_data.dims[0]] == 0: + print("[red]You have excluded all the data![/red]", peakipy_data.data.shape) + exit() + return plane_numbers, peakipy_data + + +def exclude_specified_planes(exclude_plane, peakipy_data): + plane_numbers = np.arange(peakipy_data.data.shape[peakipy_data.dims[0]]) + # do not fit these planes + if exclude_plane: + inds = [i for i in exclude_plane] + data_inds = [ + (i not in inds) + for i in range(peakipy_data.data.shape[peakipy_data.dims[0]]) + ] + plane_numbers = np.arange(peakipy_data.data.shape[peakipy_data.dims[0]])[ + data_inds + ] + peakipy_data.data = peakipy_data.data[data_inds] + print( + f"[yellow]Excluding planes {exclude_plane} data now has the following shape[/yellow]", + peakipy_data.data.shape, + ) + if peakipy_data.data.shape[peakipy_data.dims[0]] == 0: + print("[red]You have excluded all the data![/red]", peakipy_data.data.shape) + exit() + return plane_numbers, peakipy_data + + +def check_for_include_column_and_add_if_missing(peakipy_data): + # only include peaks with 'include' + if "include" in peakipy_data.df.columns: + pass + else: + # for compatibility + peakipy_data.df["include"] = peakipy_data.df.apply(lambda _: "yes", axis=1) + return peakipy_data + + +def remove_excluded_peaks(peakipy_data): + if len(peakipy_data.df[peakipy_data.df.include != "yes"]) > 0: + excluded = peakipy_data.df[peakipy_data.df.include != "yes"][column_selection] + table = df_to_rich_table( + excluded, + title="[yellow] Excluded peaks [/yellow]", + columns=excluded.columns, + styles=["yellow" for i in excluded.columns], + ) + print(table) + peakipy_data.df = peakipy_data.df[peakipy_data.df.include == "yes"] + return peakipy_data + + +def warn_if_trying_to_fit_large_clusters(max_cluster_size, peakipy_data): + if max_cluster_size is None: + max_cluster_size = peakipy_data.df.MEMCNT.max() + if peakipy_data.df.MEMCNT.max() > 10: + print( + f"""[red] + ################################################################## + You have some clusters of as many as {max_cluster_size} peaks. + You may want to consider reducing the size of your clusters as the + fits will struggle. + + Otherwise you can use the --max-cluster-size flag to exclude large + clusters + ################################################################## + [/red]""" + ) + else: + max_cluster_size = max_cluster_size + return max_cluster_size + + +def update_linewidths_from_hz_to_points(peakipy_data): + """in case they were adjusted when running edit.py""" + peakipy_data.df["XW"] = peakipy_data.df.XW_HZ * peakipy_data.pt_per_hz_f2 + peakipy_data.df["YW"] = peakipy_data.df.YW_HZ * peakipy_data.pt_per_hz_f1 + return peakipy_data + + +def update_peak_positions_from_ppm_to_points(peakipy_data): + # convert peak positions from ppm to points in case they were adjusted running edit.py + peakipy_data.df["X_AXIS"] = peakipy_data.df.X_PPM.apply( + lambda x: peakipy_data.uc_f2(x, "PPM") + ) + peakipy_data.df["Y_AXIS"] = peakipy_data.df.Y_PPM.apply( + lambda x: peakipy_data.uc_f1(x, "PPM") + ) + peakipy_data.df["X_AXISf"] = peakipy_data.df.X_PPM.apply( + lambda x: peakipy_data.uc_f2.f(x, "PPM") + ) + peakipy_data.df["Y_AXISf"] = peakipy_data.df.Y_PPM.apply( + lambda x: peakipy_data.uc_f1.f(x, "PPM") + ) + return peakipy_data + + +def unpack_xy_bounds(xy_bounds, peakipy_data): + match xy_bounds: + case (0, 0): + xy_bounds = None + case (x, y): + # convert ppm to points + xy_bounds = list(xy_bounds) + xy_bounds[0] = xy_bounds[0] * peakipy_data.pt_per_ppm_f2 + xy_bounds[1] = xy_bounds[1] * peakipy_data.pt_per_ppm_f1 + return xy_bounds + + +def save_data(df, output_name): + suffix = output_name.suffix + if suffix == ".csv": + df.to_csv(output_name, float_format="%.4f", index=False) + + elif suffix == ".tab": + df.to_csv(output_name, sep="\t", float_format="%.4f", index=False) + + else: + df.to_pickle(output_name) + + reference_plane_index_help = ( "Select planes to use for initial estimation of lineshape parameters" ) @@ -396,9 +618,7 @@ def fit( ] = [], initial_fit_threshold: Optional[float] = None, mp: bool = True, - plot: Optional[Path] = None, - show: bool = False, - verb: bool = False, + verbose: bool = False, ): """Fit NMR data to lineshape models and deconvolute overlapping peaks @@ -436,13 +656,6 @@ def fit( intensities above this threshold will be included in the intial fit of summed planes. mp : bool Use multiprocessing [default: True] - plot : Optional[Path] - Whether to plot wireframe fits for each peak - (saved into Path provided) [default: None] - show : bool - Whether to show (using plt.show()) wireframe - fits for each peak. Only works if Path is provided to the plot - argument verb : bool Print what's going on """ @@ -455,153 +668,36 @@ def fit( args, config = read_config(args) dims = config.get("dims", [0, 1, 2]) peakipy_data = LoadData(peaklist_path, data_path, dims=dims) + peakipy_data = check_for_include_column_and_add_if_missing(peakipy_data) + peakipy_data = remove_excluded_peaks(peakipy_data) + max_cluster_size = warn_if_trying_to_fit_large_clusters( + max_cluster_size, peakipy_data + ) - # only include peaks with 'include' - if "include" in peakipy_data.df.columns: - pass - else: - # for compatibility - peakipy_data.df["include"] = peakipy_data.df.apply(lambda _: "yes", axis=1) - - if len(peakipy_data.df[peakipy_data.df.include != "yes"]) > 0: - excluded = peakipy_data.df[peakipy_data.df.include != "yes"][column_selection] - table = df_to_rich_table( - excluded, - title="[yellow] Excluded peaks [/yellow]", - columns=excluded.columns, - styles=["yellow" for i in excluded.columns], - ) - print(table) - peakipy_data.df = peakipy_data.df[peakipy_data.df.include == "yes"] - - # filter list based on cluster size - if max_cluster_size is None: - max_cluster_size = peakipy_data.df.MEMCNT.max() - if peakipy_data.df.MEMCNT.max() > 10: - print( - f"""[red] - ################################################################## - You have some clusters of as many as {max_cluster_size} peaks. - You may want to consider reducing the size of your clusters as the - fits will struggle. - - Otherwise you can use the --max-cluster-size flag to exclude large - clusters - ################################################################## - [/red]""" - ) - else: - max_cluster_size = max_cluster_size args["max_cluster_size"] = max_cluster_size args["to_fix"] = fix - args["verb"] = verb - args["show"] = show + args["verbose"] = verbose args["mp"] = mp args["initial_fit_threshold"] = initial_fit_threshold args["reference_plane_indices"] = reference_plane_index - # read vclist - if vclist is None: - vclist = False - elif vclist.exists(): - vclist_data = np.genfromtxt(vclist) - args["vclist_data"] = vclist_data - vclist = True - else: - raise Exception("vclist not found...") - - args["vclist"] = vclist - + args = get_vclist(vclist, args) # plot results or not log_file = open(tmp_path / log_path, "w") - if plot: - plot.mkdir(parents=True, exist_ok=True) - - args["plot"] = plot uc_dics = {"f1": peakipy_data.uc_f1, "f2": peakipy_data.uc_f2} args["uc_dics"] = uc_dics - # check data shape is consistent with dims - if len(peakipy_data.dims) != len(peakipy_data.data.shape): - print( - f"Dims are {peakipy_data.dims} while data shape is {peakipy_data.data.shape}?" - ) - exit() - - plane_numbers = np.arange(peakipy_data.data.shape[peakipy_data.dims[0]]) - # only fit specified planes - if plane: - inds = [i for i in plane] - data_inds = [ - (i in inds) for i in range(peakipy_data.data.shape[peakipy_data.dims[0]]) - ] - plane_numbers = np.arange(peakipy_data.data.shape[peakipy_data.dims[0]])[ - data_inds - ] - peakipy_data.data = peakipy_data.data[data_inds] - print( - "[yellow]Using only planes {plane} data now has the following shape[/yellow]", - peakipy_data.data.shape, - ) - if peakipy_data.data.shape[peakipy_data.dims[0]] == 0: - print("[red]You have excluded all the data![/red]", peakipy_data.data.shape) - exit() - - # do not fit these planes - if exclude_plane: - inds = [i for i in exclude_plane] - data_inds = [ - (i not in inds) - for i in range(peakipy_data.data.shape[peakipy_data.dims[0]]) - ] - plane_numbers = np.arange(peakipy_data.data.shape[peakipy_data.dims[0]])[ - data_inds - ] - peakipy_data.data = peakipy_data.data[data_inds] - print( - f"[yellow]Excluding planes {exclude_plane} data now has the following shape[/yellow]", - peakipy_data.data.shape, - ) - if peakipy_data.data.shape[peakipy_data.dims[0]] == 0: - print("[red]You have excluded all the data![/red]", peakipy_data.data.shape) - exit() - - # setting noise for calculation of chi square - # if noise is None: + check_data_shape_is_consistent_with_dims(peakipy_data) + plane_numbers, peakipy_data = select_specified_planes(plane, peakipy_data) + plane_numbers, peakipy_data = exclude_specified_planes(exclude_plane, peakipy_data) noise = abs(threshold_otsu(peakipy_data.data)) args["noise"] = noise args["lineshape"] = lineshape - - match xy_bounds: - case (0, 0): - xy_bounds = None - case (x, y): - # convert ppm to points - xy_bounds = list(xy_bounds) - xy_bounds[0] = xy_bounds[0] * peakipy_data.pt_per_ppm_f2 - xy_bounds[1] = xy_bounds[1] * peakipy_data.pt_per_ppm_f1 - + xy_bounds = unpack_xy_bounds(xy_bounds, peakipy_data) args["xy_bounds"] = xy_bounds - # args, config = read_config(args) - # convert linewidths from Hz to points in case they were adjusted when running edit.py - peakipy_data.df["XW"] = peakipy_data.df.XW_HZ * peakipy_data.pt_per_hz_f2 - peakipy_data.df["YW"] = peakipy_data.df.YW_HZ * peakipy_data.pt_per_hz_f1 - - # convert peak positions from ppm to points in case they were adjusted running edit.py - peakipy_data.df["X_AXIS"] = peakipy_data.df.X_PPM.apply( - lambda x: peakipy_data.uc_f2(x, "PPM") - ) - peakipy_data.df["Y_AXIS"] = peakipy_data.df.Y_PPM.apply( - lambda x: peakipy_data.uc_f1(x, "PPM") - ) - peakipy_data.df["X_AXISf"] = peakipy_data.df.X_PPM.apply( - lambda x: peakipy_data.uc_f2.f(x, "PPM") - ) - peakipy_data.df["Y_AXISf"] = peakipy_data.df.Y_PPM.apply( - lambda x: peakipy_data.uc_f1.f(x, "PPM") - ) - # start fitting data + peakipy_data = update_linewidths_from_hz_to_points(peakipy_data) + peakipy_data = update_peak_positions_from_ppm_to_points(peakipy_data) # prepare data for multiprocessing nclusters = peakipy_data.df.CLUSTID.nunique() npeaks = peakipy_data.df.shape[0] @@ -610,9 +706,9 @@ def fit( f"[green]Using multiprocessing to fit {npeaks} peaks in {nclusters} clusters [/green]" + "\n" ) - # split peak lists - # tmp_dir = split_peaklist(peakipy_data.df, n_cpu) - fit_peaks_args = FitPeaksInput(args, peakipy_data.data, config, plane_numbers) + fit_peaks_args = FitPeaksInput( + FitPeaksArgs(**args), peakipy_data.data, config, plane_numbers + ) with ( Pool(processes=n_cpu) as pool, tqdm( @@ -621,11 +717,9 @@ def fit( colour="green", ) as pbar, ): - # result = pool.map(fit_peaks, peaklists) - # result = pool.starmap(fit_peaks, zip(peaklists, args_list)) result = [ pool.apply_async( - fit_peaks, + fit_peak_clusters, args=( peaklist, fit_peaks_args, @@ -636,13 +730,14 @@ def fit( ] df = pd.concat([i.df for i in result], ignore_index=True) for num, i in enumerate(result): - # i.df.to_csv(tmp_dir / Path(f"peaks_{num}_fit.csv"), index=False) log_file.write(i.log + "\n") else: print("[green]Not using multiprocessing[green]") - result = fit_peaks( + result = fit_peak_clusters( peakipy_data.df, - FitPeaksInput(args, peakipy_data.data, config, plane_numbers), + FitPeaksInput( + FitPeaksArgs(**args), peakipy_data.data, config, plane_numbers + ), ) df = result.df log_file.write(result.log) @@ -652,159 +747,11 @@ def fit( # close log file log_file.close() output = Path(output_path) - suffix = output.suffix - #  convert sigmas to fwhm - match lineshape: - case lineshape.V: - # calculate peak height - df["height"] = df.apply( - lambda x: voigt2d( - XY=[0, 0], - center_x=0.0, - center_y=0.0, - sigma_x=x.sigma_x, - sigma_y=x.sigma_y, - gamma_x=x.gamma_x, - gamma_y=x.gamma_y, - amplitude=x.amp, - ), - axis=1, - ) - print("VOIGT") - print(df[["amp", "amp_err", "height"]]) - df["height_err"] = df.apply( - lambda x: x.amp_err * (x.height / x.amp) if x.amp_err != None else 0.0, - axis=1, - ) - df["fwhm_g_x"] = df.sigma_x.apply( - lambda x: 2.0 * x * np.sqrt(2.0 * np.log(2.0)) - ) # fwhm of gaussian - df["fwhm_g_y"] = df.sigma_y.apply( - lambda x: 2.0 * x * np.sqrt(2.0 * np.log(2.0)) - ) - df["fwhm_l_x"] = df.gamma_x.apply(lambda x: 2.0 * x) # fwhm of lorentzian - df["fwhm_l_y"] = df.gamma_y.apply(lambda x: 2.0 * x) - df["fwhm_x"] = df.apply( - lambda x: 0.5346 * x.fwhm_l_x - + np.sqrt(0.2166 * x.fwhm_l_x**2.0 + x.fwhm_g_x**2.0), - axis=1, - ) - df["fwhm_y"] = df.apply( - lambda x: 0.5346 * x.fwhm_l_y - + np.sqrt(0.2166 * x.fwhm_l_y**2.0 + x.fwhm_g_y**2.0), - axis=1, - ) - # df["fwhm_y"] = df.apply(lambda x: x.gamma_y + np.sqrt(x.gamma_y**2.0 + 4 * x.sigma_y**2.0 * 2.0 * np.log(2.)), axis=1) - # df["fwhm_x"] = df.apply(lambda x: x.gamma_x + np.sqrt(x.gamma_x**2.0 + 4 * x.sigma_x**2.0 * 2.0 * np.log(2.)), axis=1) - # df["fwhm_y"] = df.apply(lambda x: x.gamma_y + np.sqrt(x.gamma_y**2.0 + 4 * x.sigma_y**2.0 * 2.0 * np.log(2.)), axis=1) - - case lineshape.PV: - # calculate peak height - df["height"] = df.apply( - lambda x: pvoigt2d( - XY=[0, 0], - center_x=0.0, - center_y=0.0, - sigma_x=x.sigma_x, - sigma_y=x.sigma_y, - amplitude=x.amp, - fraction=x.fraction, - ), - axis=1, - ) - df["height_err"] = df.apply( - lambda x: x.amp_err * (x.height / x.amp), axis=1 - ) - df["fwhm_x"] = df.sigma_x.apply(lambda x: x * 2.0) - df["fwhm_y"] = df.sigma_y.apply(lambda x: x * 2.0) + df = calculate_lineshape_specific_height_and_fwhm(lineshape, df) + df = calculate_peak_centers_in_ppm(df, peakipy_data) + df = calculate_peak_linewidths_in_hz(df, peakipy_data) - case lineshape.G: - df["height"] = df.apply( - lambda x: pvoigt2d( - XY=[0, 0], - center_x=0.0, - center_y=0.0, - sigma_x=x.sigma_x, - sigma_y=x.sigma_y, - amplitude=x.amp, - fraction=0.0, # gaussian - ), - axis=1, - ) - df["height_err"] = df.apply( - lambda x: x.amp_err * (x.height / x.amp), axis=1 - ) - df["fwhm_x"] = df.sigma_x.apply(lambda x: x * 2.0) - df["fwhm_y"] = df.sigma_y.apply(lambda x: x * 2.0) - - case lineshape.L: - df["height"] = df.apply( - lambda x: pvoigt2d( - XY=[0, 0], - center_x=0.0, - center_y=0.0, - sigma_x=x.sigma_x, - sigma_y=x.sigma_y, - amplitude=x.amp, - fraction=1.0, # lorentzian - ), - axis=1, - ) - df["height_err"] = df.apply( - lambda x: x.amp_err * (x.height / x.amp), axis=1 - ) - df["fwhm_x"] = df.sigma_x.apply(lambda x: x * 2.0) - df["fwhm_y"] = df.sigma_y.apply(lambda x: x * 2.0) - - case lineshape.PV_PV: - # calculate peak height - df["height"] = df.apply( - lambda x: pv_pv( - XY=[0, 0], - center_x=0.0, - center_y=0.0, - sigma_x=x.sigma_x, - sigma_y=x.sigma_y, - amplitude=x.amp, - fraction_x=x.fraction_x, - fraction_y=x.fraction_y, - ), - axis=1, - ) - df["height_err"] = df.apply( - lambda x: x.amp_err * (x.height / x.amp), axis=1 - ) - df["fwhm_x"] = df.sigma_x.apply(lambda x: x * 2.0) - df["fwhm_y"] = df.sigma_y.apply(lambda x: x * 2.0) - - case _: - df["fwhm_x"] = df.sigma_x.apply(lambda x: x * 2.0) - df["fwhm_y"] = df.sigma_y.apply(lambda x: x * 2.0) - #  convert values to ppm - df["center_x_ppm"] = df.center_x.apply(lambda x: peakipy_data.uc_f2.ppm(x)) - df["center_y_ppm"] = df.center_y.apply(lambda x: peakipy_data.uc_f1.ppm(x)) - df["init_center_x_ppm"] = df.init_center_x.apply( - lambda x: peakipy_data.uc_f2.ppm(x) - ) - df["init_center_y_ppm"] = df.init_center_y.apply( - lambda x: peakipy_data.uc_f1.ppm(x) - ) - df["sigma_x_ppm"] = df.sigma_x.apply(lambda x: x * peakipy_data.ppm_per_pt_f2) - df["sigma_y_ppm"] = df.sigma_y.apply(lambda x: x * peakipy_data.ppm_per_pt_f1) - df["fwhm_x_ppm"] = df.fwhm_x.apply(lambda x: x * peakipy_data.ppm_per_pt_f2) - df["fwhm_y_ppm"] = df.fwhm_y.apply(lambda x: x * peakipy_data.ppm_per_pt_f1) - df["fwhm_x_hz"] = df.fwhm_x.apply(lambda x: x * peakipy_data.hz_per_pt_f2) - df["fwhm_y_hz"] = df.fwhm_y.apply(lambda x: x * peakipy_data.hz_per_pt_f1) - - # save data - if suffix == ".csv": - df.to_csv(output, float_format="%.4f", index=False) - - elif suffix == ".tab": - df.to_csv(output, sep="\t", float_format="%.4f", index=False) - - else: - df.to_pickle(output) + save_data(df, output) print( """[green] diff --git a/peakipy/core.py b/peakipy/core.py index e40ac687..e14c0b40 100644 --- a/peakipy/core.py +++ b/peakipy/core.py @@ -38,7 +38,7 @@ from numpy import sqrt, log, pi, exp, finfo -from lmfit import Model +from lmfit import Model, Parameters from lmfit.model import ModelResult from lmfit.models import LinearModel from scipy.special import wofz @@ -878,397 +878,6 @@ def make_meshgrid(data_shape): return XY -def fit_first_plane( - group, - data, - uc_dics, - lineshape: Lineshape.PV, - xy_bounds=None, - verbose=False, - log=None, - noise=1.0, - fit_method="leastsq", - reference_plane_indices: List[int] = [], - threshold: Optional[float] = None, -): - r"""Deconvolute group of peaks - - :param group: pandas data from containing group of peaks using groupby("CLUSTID") - :type group: pandas.core.groupby.generic.DataFrameGroupBy - - :param data: NMR data - :type data: numpy.array - - :param uc_dics: nmrglue unit conversion dics {"f1":uc_f1,"f2":uc_f2} - :type uc_dics: dict - - :param lineshape: lineshape to fit (PV, G, L, V, G_L, PV_L, PV_G or PV_PV) - :type lineshape: str - - :param xy_bounds: set bounds on x y positions. None or (x_bound, y_bound) - :type xy_bounds: tuple - - :param plot: dir to save wireframe plots - :type plot: str - - :param show: interactive matplotlib plot - :type show: bool - - :param verbose: print what is happening to terminal - :type verbose: bool - - :param log: file - :type log: str - - :param noise: estimate of spectral noise for calculation of :math:`\chi^2` and :math:`\chi^2_{red}` - :type noise: float - - :param fit_method: method used by lmfit - :type fit_method: str - - :return: FitResult - :rtype: FitResult - - """ - lineshape_function = get_lineshape_function(lineshape) - - first_plane_data = data[0] - mask, peak = make_mask_from_peak_cluster(group, first_plane_data) - - x_radius = group.X_RADIUS.max() - y_radius = group.Y_RADIUS.max() - - max_x, min_x = get_limits_for_axis_in_points( - group_axis_points=group.X_AXISf, mask_radius_in_points=x_radius - ) - max_y, min_y = get_limits_for_axis_in_points( - group_axis_points=group.Y_AXISf, mask_radius_in_points=y_radius - ) - max_x, min_x, max_y, min_y = deal_with_peaks_on_edge_of_spectrum( - data.shape, max_x, min_x, max_y, min_y - ) - selected_data = select_reference_planes_using_indices( - data, reference_plane_indices - ).sum(axis=0) - mod, p_guess = make_models( - lineshape_function, - group, - selected_data, - lineshape=lineshape, - xy_bounds=xy_bounds, - ) - peak_slices = slice_peaks_from_data_using_mask(data, mask) - peak_slices = select_reference_planes_using_indices( - peak_slices, reference_plane_indices - ) - peak_slices = select_planes_above_threshold_from_masked_data(peak_slices, threshold) - peak_slices = peak_slices.sum(axis=0) - - XY = make_meshgrid(data.shape) - X, Y = XY - - XY_slices = np.array([X.copy()[mask], Y.copy()[mask]]) - - weights = 1.0 / np.array([noise] * len(np.ravel(peak_slices))) - - out = mod.fit( - peak_slices, XY=XY_slices, params=p_guess, weights=weights, method=fit_method - ) - - if verbose: - console.print(out.fit_report(), style="bold") - - z_sim = mod.eval(XY=XY, params=out.params) - z_sim[~mask] = np.nan - z_plot = first_plane_data.copy() - z_plot[~mask] = np.nan - #  also if peak position changed significantly from start then add warning - - _z_plot = z_plot[~np.isnan(z_plot)] - _z_sim = z_sim[~np.isnan(z_sim)] - - linmod = LinearModel() - linpars = linmod.guess(_z_sim, x=_z_plot) - linfit = linmod.fit(_z_sim, x=_z_plot, params=linpars) - slope = linfit.params["slope"].value - #  number of peaks in cluster - n_peaks = len(group) - - chi2 = out.chisqr - redchi = out.redchi - - fit_str = f""" - Cluster {peak.CLUSTID} containing {n_peaks} peaks - slope={slope:.3f} - - chi^2 = {chi2:.5f} - redchi = {redchi:.5f} - - """ - if (slope > 1.05) or (slope < 0.95): - fit_str += """ - 🧐 NEEDS CHECKING 🧐 - """ - # console.print(fit_str, style="bold yellow") - else: - # console.print(fit_str, style="green") - pass - - if log is not None: - log.write("".join("#" for _ in range(60)) + "\n\n") - log.write(fit_str + "\n\n") - # pass - else: - pass - - return FitResult( - out=out, - mask=mask, - fit_str=fit_str, - log=log, - group=group, - uc_dics=uc_dics, - min_x=min_x, - min_y=min_y, - max_x=max_x, - max_y=max_y, - X=X, - Y=Y, - Z=z_plot, - Z_sim=z_sim, - peak_slices=peak_slices, - XY_slices=XY_slices, - weights=weights, - mod=mod, - ) - - -class FitResult: - """Data structure for storing fit results""" - - def __init__( - self, - out: ModelResult, - mask: np.array, - fit_str: str, - log: str, - group: pd.core.groupby.generic.DataFrameGroupBy, - uc_dics: dict, - min_x: float, - min_y: float, - max_x: float, - max_y: float, - X: np.array, - Y: np.array, - Z: np.array, - Z_sim: np.array, - peak_slices: np.array, - XY_slices: np.array, - weights: np.array, - mod: Model, - ): - """Store output of fit_first_plane function""" - self.out = out - self.mask = mask - self.fit_str = fit_str - self.log = log - self.group = group - self.uc_dics = uc_dics - self.min_x = min_x - self.min_y = min_y - self.max_x = max_x - self.max_y = max_y - self.X = X - self.Y = Y - self.Z = Z - self.Z_sim = Z_sim - self.peak_slices = peak_slices - self.XY_slices = XY_slices - self.weights = weights - self.mod = mod - - def check_shifts(self): - """Calculate difference between initial peak positions - and check whether they moved too much from original - position - - """ - pass - - def jackknife(self): - """perform jackknife sampling to estimate fitting errors""" - jk_results = [] - for i in range(len(self.peak_slices)): - peak_slices = np.delete(self.peak_slices, i, None) - X = np.delete(self.XY_slices[0], i, None) - Y = np.delete(self.XY_slices[1], i, None) - weights = np.delete(self.weights, i, None) - jk_results.append( - self.mod.fit( - peak_slices, XY=[X, Y], params=self.out.params, weights=weights - ) - ) - - # print(jk_results) - amps = [] - sigma_xs = [] - sigma_ys = [] - names = [] - with open("test_jackknife", "w") as f: - for i in jk_results: - f.write(i.fit_report()) - amp, amp_err, name = get_params(i.params, "amp") - sigma_x, sigma_x_err, name_x = get_params(i.params, "sigma_x") - sigma_y, sigma_y_err, name_y = get_params(i.params, "sigma_y") - f.write(f"{amp},{amp_err},{name_y}\n") - amps.extend(amp) - names.extend(name_y) - sigma_xs.extend(sigma_x) - sigma_ys.extend(sigma_y) - - df = pd.DataFrame( - {"amp": amps, "name": names, "sigma_x": sigma_xs, "sigma_y": sigma_ys} - ) - grouped = df.groupby("name") - mean_amps = grouped.amp.mean() - std_amps = grouped.amp.std() - mean_sigma_x = grouped.sigma_x.mean() - std_sigma_x = grouped.sigma_x.std() - mean_sigma_y = grouped.sigma_y.mean() - std_sigma_y = grouped.sigma_y.std() - f.write("#####################################\n") - f.write( - f"{mean_amps}, {std_amps}, {mean_sigma_x}, {std_sigma_x}, {mean_sigma_y}, {std_sigma_y} " - ) - f.write(self.out.fit_report()) - f.write("#####################################\n") - # print(amps) - # mean = np.mean(amps) - # std = np.std(amps) - return JackKnifeResult(mean=mean_amps, std=std_amps) - - def plot(self, plot_path=None, show=False, mp=False): - """Matplotlib interactive plot of the fits""" - - if plot_path != None: - plot_path = Path(plot_path) - plot_path.mkdir(parents=True, exist_ok=True) - # plotting - fig = plt.figure(figsize=(8, 6)) - ax = fig.add_subplot(111, projection="3d") - # slice out plot area - x_plot = self.uc_dics["f2"].ppm( - self.X[self.min_y : self.max_y, self.min_x : self.max_x] - ) - y_plot = self.uc_dics["f1"].ppm( - self.Y[self.min_y : self.max_y, self.min_x : self.max_x] - ) - z_plot = self.Z[self.min_y : self.max_y, self.min_x : self.max_x] - - z_sim = self.Z_sim[self.min_y : self.max_y, self.min_x : self.max_x] - - ax.set_title( - r"$\chi^2$=" - + f"{self.out.chisqr:.3f}, " - + r"$\chi_{red}^2$=" - + f"{self.out.redchi:.4f}" - ) - - residual = z_plot - z_sim - cset = ax.contourf( - x_plot, - y_plot, - residual, - zdir="z", - offset=np.nanmin(z_plot) * 1.1, - alpha=0.5, - cmap=cm.coolwarm, - ) - cbl = fig.colorbar(cset, ax=ax, shrink=0.5, format="%.2e") - cbl.ax.set_title("Residual") - # plot raw data - ax.plot_wireframe(x_plot, y_plot, z_plot, color="#03353E", label="data") - - ax.set_xlabel("F2 ppm") - ax.set_ylabel("F1 ppm") - ax.plot_wireframe( - x_plot, y_plot, z_sim, color="#C1403D", linestyle="--", label="fit" - ) - - # axes will appear inverted - ax.view_init(30, 120) - - # Annotate plots - labs = [] - Z_lab = [] - Y_lab = [] - X_lab = [] - for k, v in self.out.params.valuesdict().items(): - if "amplitude" in k: - Z_lab.append(v) - # get prefix - labs.append(" ".join(k.split("_")[:-1])) - elif "center_x" in k: - X_lab.append(self.uc_dics["f2"].ppm(v)) - elif "center_y" in k: - Y_lab.append(self.uc_dics["f1"].ppm(v)) - #  this is dumb as !£$@ - # Z_lab = [ - # self.Z[ - # int(round(self.uc_dics["f1"](y, "ppm"))), - # int(round(self.uc_dics["f2"](x, "ppm"))), - # ] - # for x, y in zip(X_lab, Y_lab) - # ] - z_max = np.nanmax(z_plot.ravel()) - Z_lab = np.array(Z_lab) - z_max = z_max * (Z_lab / max(Z_lab)) - for l, x, y, z in zip(labs, X_lab, Y_lab, z_max): - # print(l, x, y, z) - # ax.text(x, y, z * 1.2, l, None) - z = z * 1.2 - ax.text(x, y, z, l, None) - ax.plot([x, x], [y, y], [0, z], linestyle="dotted", c="k", alpha=0.5) - - # plt.colorbar(contf) - plt.legend(bbox_to_anchor=(1.2, 1.1)) - - name = self.group.CLUSTID.iloc[0] - if show and not mp: - plt.savefig(plot_path / f"{name}.png", dpi=300) - - def exit_program(event): - exit() - - def next_plot(event): - plt.close() - - axexit = plt.axes([0.81, 0.05, 0.1, 0.075]) - bnexit = Button(axexit, "Exit") - bnexit.on_clicked(exit_program) - - axnext = plt.axes([0.71, 0.05, 0.1, 0.075]) - bnnext = Button(axnext, "Next") - bnnext.on_clicked(next_plot) - - plt.show() - else: - print( - "[red]Cannot use interactive matplotlib in multiprocess mode. Use --no-mp flag.[/red]" - ) - plt.savefig(plot_path / f"{name}.png", dpi=300) - # print(p_guess) - # close plot - plt.close() - else: - pass - - -class JackKnifeResult: - def __init__(self, mean, std): - self.mean = mean - self.std = std - - class Pseudo3D: """Read dic, data from NMRGlue and dims from input to create a Pseudo3D dataset @@ -2233,3 +1842,142 @@ def read_config(args, config_path="peakipy.config"): args["colors"] = colors return args, config + + +def calculate_height_for_voigt_lineshape(df): + df["height"] = df.apply( + lambda x: voigt2d( + XY=[0, 0], + center_x=0.0, + center_y=0.0, + sigma_x=x.sigma_x, + sigma_y=x.sigma_y, + gamma_x=x.gamma_x, + gamma_y=x.gamma_y, + amplitude=x.amp, + ), + axis=1, + ) + df["height_err"] = df.apply( + lambda x: x.amp_err * (x.height / x.amp) if x.amp_err != None else 0.0, + axis=1, + ) + return df + + +def calculate_fwhm_for_voigt_lineshape(df): + df["fwhm_g_x"] = df.sigma_x.apply( + lambda x: 2.0 * x * np.sqrt(2.0 * np.log(2.0)) + ) # fwhm of gaussian + df["fwhm_g_y"] = df.sigma_y.apply(lambda x: 2.0 * x * np.sqrt(2.0 * np.log(2.0))) + df["fwhm_l_x"] = df.gamma_x.apply(lambda x: 2.0 * x) # fwhm of lorentzian + df["fwhm_l_y"] = df.gamma_y.apply(lambda x: 2.0 * x) + df["fwhm_x"] = df.apply( + lambda x: 0.5346 * x.fwhm_l_x + + np.sqrt(0.2166 * x.fwhm_l_x**2.0 + x.fwhm_g_x**2.0), + axis=1, + ) + df["fwhm_y"] = df.apply( + lambda x: 0.5346 * x.fwhm_l_y + + np.sqrt(0.2166 * x.fwhm_l_y**2.0 + x.fwhm_g_y**2.0), + axis=1, + ) + return df + + +def calculate_height_for_pseudo_voigt_lineshape(df): + df["height"] = df.apply( + lambda x: pvoigt2d( + XY=[0, 0], + center_x=0.0, + center_y=0.0, + sigma_x=x.sigma_x, + sigma_y=x.sigma_y, + amplitude=x.amp, + fraction=x.fraction, + ), + axis=1, + ) + df["height_err"] = df.apply(lambda x: x.amp_err * (x.height / x.amp), axis=1) + return df + + +def calculate_fwhm_for_pseudo_voigt_lineshape(df): + df["fwhm_x"] = df.sigma_x.apply(lambda x: x * 2.0) + df["fwhm_y"] = df.sigma_y.apply(lambda x: x * 2.0) + return df + + +def calculate_height_for_gaussian_lineshape(df): + df["height"] = df.apply( + lambda x: pvoigt2d( + XY=[0, 0], + center_x=0.0, + center_y=0.0, + sigma_x=x.sigma_x, + sigma_y=x.sigma_y, + amplitude=x.amp, + fraction=0.0, # gaussian + ), + axis=1, + ) + df["height_err"] = df.apply(lambda x: x.amp_err * (x.height / x.amp), axis=1) + return df + + +def calculate_height_for_lorentzian_lineshape(df): + df["height"] = df.apply( + lambda x: pvoigt2d( + XY=[0, 0], + center_x=0.0, + center_y=0.0, + sigma_x=x.sigma_x, + sigma_y=x.sigma_y, + amplitude=x.amp, + fraction=1.0, # lorentzian + ), + axis=1, + ) + df["height_err"] = df.apply(lambda x: x.amp_err * (x.height / x.amp), axis=1) + return df + + +def calculate_height_for_pv_pv_lineshape(df): + df["height"] = df.apply( + lambda x: pv_pv( + XY=[0, 0], + center_x=0.0, + center_y=0.0, + sigma_x=x.sigma_x, + sigma_y=x.sigma_y, + amplitude=x.amp, + fraction_x=x.fraction_x, + fraction_y=x.fraction_y, + ), + axis=1, + ) + df["height_err"] = df.apply(lambda x: x.amp_err * (x.height / x.amp), axis=1) + return df + + +def calculate_peak_centers_in_ppm(df, peakipy_data): + #  convert values to ppm + df["center_x_ppm"] = df.center_x.apply(lambda x: peakipy_data.uc_f2.ppm(x)) + df["center_y_ppm"] = df.center_y.apply(lambda x: peakipy_data.uc_f1.ppm(x)) + df["init_center_x_ppm"] = df.init_center_x.apply( + lambda x: peakipy_data.uc_f2.ppm(x) + ) + df["init_center_y_ppm"] = df.init_center_y.apply( + lambda x: peakipy_data.uc_f1.ppm(x) + ) + return df + + +def calculate_peak_linewidths_in_hz(df, peakipy_data): + df["sigma_x_ppm"] = df.sigma_x.apply(lambda x: x * peakipy_data.ppm_per_pt_f2) + df["sigma_y_ppm"] = df.sigma_y.apply(lambda x: x * peakipy_data.ppm_per_pt_f1) + df["fwhm_x_ppm"] = df.fwhm_x.apply(lambda x: x * peakipy_data.ppm_per_pt_f2) + df["fwhm_y_ppm"] = df.fwhm_y.apply(lambda x: x * peakipy_data.ppm_per_pt_f1) + df["fwhm_x_hz"] = df.fwhm_x.apply(lambda x: x * peakipy_data.hz_per_pt_f2) + df["fwhm_y_hz"] = df.fwhm_y.apply(lambda x: x * peakipy_data.hz_per_pt_f1) + return df diff --git a/pyproject.toml b/pyproject.toml index ad532fa7..52c1d01e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -52,6 +52,7 @@ pytest = "^7.4.2" mkdocs-material = "^8.5.7" ipython = "^8.15.0" pre-commit = "^3.6.0" +coverage = "^7.4.2" [build-system] requires = ["poetry>=0.12"] diff --git a/test/test_main.py b/test/test_main.py new file mode 100644 index 00000000..ea40fe02 --- /dev/null +++ b/test/test_main.py @@ -0,0 +1,78 @@ +import tempfile +from dataclasses import dataclass, field + +import numpy as np +import pandas as pd +from pytest import fixture + +from peakipy.cli.main import ( + get_vclist, + check_for_include_column_and_add_if_missing, + select_specified_planes, + exclude_specified_planes, +) + + +@fixture +def actual_vclist(): + with tempfile.TemporaryFile() as fp: + fp.write(b"1\n2\n3\n") + fp.seek(0) + vclist = np.genfromtxt(fp) + return vclist + + +@dataclass +class PeakipyData: + df: pd.DataFrame = pd.DataFrame() + data: np.array = np.zeros((4, 10, 20)) + dims: list = field(default_factory=lambda: [0, 1, 2]) + + +def test_get_vclist(actual_vclist): + expected_vclist = np.array([1.0, 2.0, 3.0]) + np.testing.assert_array_equal(actual_vclist, expected_vclist) + + +def test_get_vclist_none(): + vclist = None + args = {} + args = get_vclist(vclist, args) + expected_args = dict(vclist=False) + assert args == expected_args + + +def test_check_for_include_column(): + peakipy_data = PeakipyData(pd.DataFrame()) + peakipy_data = check_for_include_column_and_add_if_missing(peakipy_data) + assert "include" in peakipy_data.df.columns + + +def test_select_specified_planes(): + plane = None + expected_plane_numbers = np.arange(4) + actual_plane_numbers, peakipy_data = select_specified_planes(plane, PeakipyData()) + np.testing.assert_array_equal(expected_plane_numbers, actual_plane_numbers) + + +def test_select_specified_planes_2(): + plane = [1, 2] + expected_plane_numbers = np.array([1, 2]) + actual_plane_numbers, peakipy_data = select_specified_planes(plane, PeakipyData()) + np.testing.assert_array_equal(expected_plane_numbers, actual_plane_numbers) + assert peakipy_data.data.shape == (2, 10, 20) + + +def test_exclude_specified_planes(): + plane = None + expected_plane_numbers = np.arange(4) + actual_plane_numbers, peakipy_data = exclude_specified_planes(plane, PeakipyData()) + np.testing.assert_array_equal(expected_plane_numbers, actual_plane_numbers) + + +def test_exclude_specified_planes_2(): + plane = [1, 2] + expected_plane_numbers = np.array([0, 3]) + actual_plane_numbers, peakipy_data = exclude_specified_planes(plane, PeakipyData()) + np.testing.assert_array_equal(expected_plane_numbers, actual_plane_numbers) + assert peakipy_data.data.shape == (2, 10, 20) From 1c4bafcc33b77b211a87b2f64850642313f33e11 Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Sun, 25 Feb 2024 21:43:13 -0500 Subject: [PATCH 20/37] more re-factoring added jackknife error --- peakipy/cli/fit.py | 228 ++++++++++++++++++++------------------------ peakipy/cli/main.py | 2 + 2 files changed, 107 insertions(+), 123 deletions(-) diff --git a/peakipy/cli/fit.py b/peakipy/cli/fit.py index 4930c813..854d9301 100644 --- a/peakipy/cli/fit.py +++ b/peakipy/cli/fit.py @@ -59,12 +59,33 @@ class FitPeaksArgs: exclude_plane: Optional[List[int]] = (None,) reference_plane_indices: List[int] = ([],) initial_fit_threshold: Optional[float] = (None,) + jack_knife_sample_errors: bool = False mp: bool = (True,) verbose: bool = (False,) @dataclass -class FirstPlaneFitInput: +class Config: + fit_method: str = "leastsq" + + +@dataclass +class FitPeaksInput: + """input data for the fit_peaks function""" + + args: FitPeaksArgs + data: np.array + config: Config + plane_numbers: list + + +@dataclass +class FitPeakClusterInput: + args: FitPeaksArgs + data: np.array + config: Config + plane_numbers: list + clustid: int group: pd.DataFrame last_peak: pd.DataFrame mask: np.array @@ -82,16 +103,10 @@ class FirstPlaneFitInput: weights: np.array fit_method: str = "leastsq" verbose: bool = False + masked_plane_data: np.array = field(init=False) - -@dataclass -class FitPeaksInput: - """input data for the fit_peaks function""" - - args: FitPeaksArgs - data: np.array - config: dict - plane_numbers: list + def __post_init__(self): + self.masked_plane_data = np.array([d[self.mask] for d in self.data]) @dataclass @@ -167,6 +182,7 @@ class FitPeaksResultDfRow(BaseModel): fwhm_y_ppm: float fwhm_x_hz: float fwhm_y_hz: float + jack_knife_sample_index: Optional[int] class FitPeaksResultRowGLPV(FitPeaksResultDfRow): @@ -285,26 +301,26 @@ def unpack_fitted_parameters_for_lineshape( def perform_initial_lineshape_fit_on_cluster_of_peaks( - first_plane_fit_input: FirstPlaneFitInput, + fit_peak_cluster_input: FitPeakClusterInput, ) -> FitResult: - mod = first_plane_fit_input.mod - peak_slices = first_plane_fit_input.peak_slices - XY_slices = first_plane_fit_input.XY_slices - p_guess = first_plane_fit_input.p_guess - weights = first_plane_fit_input.weights - fit_method = first_plane_fit_input.fit_method - mask = first_plane_fit_input.mask - XY = first_plane_fit_input.XY + mod = fit_peak_cluster_input.mod + peak_slices = fit_peak_cluster_input.peak_slices + XY_slices = fit_peak_cluster_input.XY_slices + p_guess = fit_peak_cluster_input.p_guess + weights = fit_peak_cluster_input.weights + fit_method = fit_peak_cluster_input.fit_method + mask = fit_peak_cluster_input.mask + XY = fit_peak_cluster_input.XY X, Y = XY - first_plane_data = first_plane_fit_input.first_plane_data - peak = first_plane_fit_input.last_peak - group = first_plane_fit_input.group - min_x = first_plane_fit_input.min_x - min_y = first_plane_fit_input.min_y - max_x = first_plane_fit_input.max_x - max_y = first_plane_fit_input.max_y - verbose = first_plane_fit_input.verbose - uc_dics = first_plane_fit_input.uc_dics + first_plane_data = fit_peak_cluster_input.first_plane_data + peak = fit_peak_cluster_input.last_peak + group = fit_peak_cluster_input.group + min_x = fit_peak_cluster_input.min_x + min_y = fit_peak_cluster_input.min_y + max_x = fit_peak_cluster_input.max_x + max_y = fit_peak_cluster_input.max_y + verbose = fit_peak_cluster_input.verbose + uc_dics = fit_peak_cluster_input.uc_dics out = mod.fit( peak_slices, XY=XY_slices, params=p_guess, weights=weights, method=fit_method @@ -342,13 +358,14 @@ def perform_initial_lineshape_fit_on_cluster_of_peaks( ) -def refit_peaks_with_constraints(fit_input: FitPeaksInput, fit_result: FitPeaksResult): +def refit_peak_cluster_with_constraints( + fit_input: FitPeakClusterInput, fit_result: FitPeaksResult +): fit_results = [] - for num, d in enumerate(fit_input.data): + for num, d in enumerate(fit_input.masked_plane_data): plane_number = fit_input.plane_numbers[num] - masked_data = d[fit_result.mask] fit_result.out.fit( - data=masked_data, + data=d, params=fit_result.out.params, weights=fit_result.weights, ) @@ -402,12 +419,10 @@ def add_vclist_to_df(fit_input: FitPeaksInput, df: pd.DataFrame): return df -def prepare_group_of_peaks_for_fitting( - group, data, fit_peaks_input_args: FitPeaksArgs, fit_method="leastsq" -): - lineshape_function = get_lineshape_function(fit_peaks_input_args.lineshape) +def prepare_group_of_peaks_for_fitting(clustid, group, fit_peaks_input: FitPeaksInput): + lineshape_function = get_lineshape_function(fit_peaks_input.args.lineshape) - first_plane_data = data[0] + first_plane_data = fit_peaks_input.data[0] mask, peak = make_mask_from_peak_cluster(group, first_plane_data) x_radius = group.X_RADIUS.max() @@ -420,34 +435,39 @@ def prepare_group_of_peaks_for_fitting( group_axis_points=group.Y_AXISf, mask_radius_in_points=y_radius ) max_x, min_x, max_y, min_y = deal_with_peaks_on_edge_of_spectrum( - data.shape, max_x, min_x, max_y, min_y + fit_peaks_input.data.shape, max_x, min_x, max_y, min_y ) selected_data = select_reference_planes_using_indices( - data, fit_peaks_input_args.reference_plane_indices + fit_peaks_input.data, fit_peaks_input.args.reference_plane_indices ).sum(axis=0) mod, p_guess = make_models( lineshape_function, group, selected_data, - lineshape=fit_peaks_input_args.lineshape, - xy_bounds=fit_peaks_input_args.xy_bounds, + lineshape=fit_peaks_input.args.lineshape, + xy_bounds=fit_peaks_input.args.xy_bounds, ) - peak_slices = slice_peaks_from_data_using_mask(data, mask) + peak_slices = slice_peaks_from_data_using_mask(fit_peaks_input.data, mask) peak_slices = select_reference_planes_using_indices( - peak_slices, fit_peaks_input_args.reference_plane_indices + peak_slices, fit_peaks_input.args.reference_plane_indices ) peak_slices = select_planes_above_threshold_from_masked_data( - peak_slices, fit_peaks_input_args.initial_fit_threshold + peak_slices, fit_peaks_input.args.initial_fit_threshold ) peak_slices = peak_slices.sum(axis=0) - XY = make_meshgrid(data.shape) + XY = make_meshgrid(fit_peaks_input.data.shape) X, Y = XY XY_slices = np.array([X.copy()[mask], Y.copy()[mask]]) - weights = 1.0 / np.array([fit_peaks_input_args.noise] * len(np.ravel(peak_slices))) + weights = 1.0 / np.array([fit_peaks_input.args.noise] * len(np.ravel(peak_slices))) # weights = 1.0 / np.ravel(peak_slices) - return FirstPlaneFitInput( + return FitPeakClusterInput( + args=fit_peaks_input.args, + data=fit_peaks_input.data, + config=fit_peaks_input.config, + plane_numbers=fit_peaks_input.plane_numbers, + clustid=clustid, group=group, last_peak=peak, mask=mask, @@ -457,35 +477,29 @@ def prepare_group_of_peaks_for_fitting( peak_slices=peak_slices, XY_slices=XY_slices, weights=weights, - fit_method=fit_method, + fit_method=Config.fit_method, first_plane_data=first_plane_data, - uc_dics=fit_peaks_input_args.uc_dics, + uc_dics=fit_peaks_input.args.uc_dics, min_x=min_x, min_y=min_y, max_x=max_x, max_y=max_y, - verbose=fit_peaks_input_args.verbose, + verbose=fit_peaks_input.args.verbose, ) -def fit_cluster_of_peaks( - clustid: int, peak_cluster: pd.DataFrame, fit_input: FitPeaksInput -) -> pd.DataFrame: - data_for_fitting = prepare_group_of_peaks_for_fitting( - peak_cluster, - fit_input.data, - fit_input.args, - fit_method=fit_input.config.get("fit_method", "leastsq"), - ) +def fit_cluster_of_peaks(data_for_fitting: FitPeakClusterInput) -> pd.DataFrame: fit_result = perform_initial_lineshape_fit_on_cluster_of_peaks(data_for_fitting) fit_result.out.params, float_str = set_parameters_to_fix_during_fit( - fit_result.out.params, fit_input.args.to_fix + fit_result.out.params, data_for_fitting.args.to_fix ) - fit_results = refit_peaks_with_constraints(fit_input, fit_result) + fit_results = refit_peak_cluster_with_constraints(data_for_fitting, fit_result) cluster_df = pd.DataFrame(fit_results) cluster_df = update_cluster_df_with_fit_statistics(cluster_df, fit_result.out) - cluster_df["clustid"] = clustid - cluster_df = merge_unpacked_parameters_with_metadata(cluster_df, peak_cluster) + cluster_df["clustid"] = data_for_fitting.clustid + cluster_df = merge_unpacked_parameters_with_metadata( + cluster_df, data_for_fitting.group + ) return cluster_df @@ -509,9 +523,15 @@ def fit_peak_clusters(peaks: pd.DataFrame, fit_input: FitPeaksInput) -> FitPeaks out_str = "" cluster_dfs = [] for clustid, peak_cluster in peak_clusters: - cluster_df = fit_cluster_of_peaks( - clustid=clustid, peak_cluster=peak_cluster, fit_input=fit_input + data_for_fitting = prepare_group_of_peaks_for_fitting( + clustid, + peak_cluster, + fit_input, ) + if fit_input.args.jack_knife_sample_errors: + cluster_df = jack_knife_sample_errors(data_for_fitting) + else: + cluster_df = fit_cluster_of_peaks(data_for_fitting) cluster_dfs.append(cluster_df) df = pd.concat(cluster_dfs, ignore_index=True) df["lineshape"] = fit_input.args.lineshape.value @@ -522,62 +542,24 @@ def fit_peak_clusters(peaks: pd.DataFrame, fit_input: FitPeaksInput) -> FitPeaks return FitPeaksResult(df=df, log=out_str) -@dataclass -class JackKnifeResult: - mean: float - std: float - - -def jack_knife_sample_errors( - peaks: pd.DataFrame, fit_input: FirstPlaneFitInput -) -> JackKnifeResult: - peak_slices = fit_input.peak_slices - XY_slices = fit_input.XY_slices - weights = fit_input.weights +def jack_knife_sample_errors(fit_input: FitPeakClusterInput) -> pd.DataFrame: + peak_slices = fit_input.peak_slices.copy() + XY_slices = fit_input.XY_slices.copy() + weights = fit_input.weights.copy() + masked_plane_data = fit_input.masked_plane_data.copy() jk_results = [] - for i in range(len(peak_slices)): - peak_slices = np.delete(peak_slices, i, None) - X = np.delete(XY_slices[0], i, None) - Y = np.delete(XY_slices[1], i, None) - weights = np.delete(weights, i, None) - jk_results.append( - mod.fit(peak_slices, XY=[X, Y], params=out.params, weights=weights) - ) - - # print(jk_results) - amps = [] - sigma_xs = [] - sigma_ys = [] - names = [] - with open("test_jackknife", "w") as f: - for i in jk_results: - f.write(i.fit_report()) - amp, amp_err, name = get_params(i.params, "amp") - sigma_x, sigma_x_err, name_x = get_params(i.params, "sigma_x") - sigma_y, sigma_y_err, name_y = get_params(i.params, "sigma_y") - f.write(f"{amp},{amp_err},{name_y}\n") - amps.extend(amp) - names.extend(name_y) - sigma_xs.extend(sigma_x) - sigma_ys.extend(sigma_y) - - df = pd.DataFrame( - {"amp": amps, "name": names, "sigma_x": sigma_xs, "sigma_y": sigma_ys} - ) - grouped = df.groupby("name") - mean_amps = grouped.amp.mean() - std_amps = grouped.amp.std() - mean_sigma_x = grouped.sigma_x.mean() - std_sigma_x = grouped.sigma_x.std() - mean_sigma_y = grouped.sigma_y.mean() - std_sigma_y = grouped.sigma_y.std() - f.write("#####################################\n") - f.write( - f"{mean_amps}, {std_amps}, {mean_sigma_x}, {std_sigma_x}, {mean_sigma_y}, {std_sigma_y} " - ) - f.write(self.out.fit_report()) - f.write("#####################################\n") - # print(amps) - # mean = np.mean(amps) - # std = np.std(amps) - return JackKnifeResult(mean=mean_amps, std=std_amps) + # first fit without jackknife + jk_result = fit_cluster_of_peaks(data_for_fitting=fit_input) + jk_result["jack_knife_sample_index"] = 0 + jk_results.append(jk_result) + for i in np.arange(0, len(peak_slices), 10, dtype=int): + fit_input.peak_slices = np.delete(peak_slices, i, None) + XY_slices_0 = np.delete(XY_slices[0], i, None) + XY_slices_1 = np.delete(XY_slices[1], i, None) + fit_input.XY_slices = np.array([XY_slices_0, XY_slices_1]) + fit_input.weights = np.delete(weights, i, None) + fit_input.masked_plane_data = np.delete(masked_plane_data, i, axis=1) + jk_result = fit_cluster_of_peaks(data_for_fitting=fit_input) + jk_result["jack_knife_sample_index"] = i + 1 + jk_results.append(jk_result) + return pd.concat(jk_results, ignore_index=True) diff --git a/peakipy/cli/main.py b/peakipy/cli/main.py index 4d4f44b0..a7687395 100644 --- a/peakipy/cli/main.py +++ b/peakipy/cli/main.py @@ -617,6 +617,7 @@ def fit( List[int], typer.Option(help=reference_plane_index_help) ] = [], initial_fit_threshold: Optional[float] = None, + jack_knife_sample_errors: bool = False, mp: bool = True, verbose: bool = False, ): @@ -680,6 +681,7 @@ def fit( args["mp"] = mp args["initial_fit_threshold"] = initial_fit_threshold args["reference_plane_indices"] = reference_plane_index + args["jack_knife_sample_errors"] = jack_knife_sample_errors args = get_vclist(vclist, args) # plot results or not From 933810c205771d2db36f62c97d71e1dea110fdf8 Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Mon, 26 Feb 2024 22:25:21 -0500 Subject: [PATCH 21/37] prototype edit and check panel --- peakipy/cli/check_panel.py | 12 +++++++++--- peakipy/cli/edit.py | 17 ++++++++++++++--- peakipy/cli/edit_panel.py | 38 ++++++++++++++++++++++++++++++++++++++ peakipy/cli/main.py | 12 ++++++------ 4 files changed, 67 insertions(+), 12 deletions(-) create mode 100644 peakipy/cli/edit_panel.py diff --git a/peakipy/cli/check_panel.py b/peakipy/cli/check_panel.py index aba815cc..3df7f2c9 100644 --- a/peakipy/cli/check_panel.py +++ b/peakipy/cli/check_panel.py @@ -75,8 +75,11 @@ def create_plotly_pane(cluster, plane): @app.command() -def check_panel( - fits_path: Path, data_path: Path, config_path: Path = Path("./peakipy.config") +def create_check_panel( + fits_path: Path, + data_path: Path, + config_path: Path = Path("./peakipy.config"), + edit_panel: bool = False, ): data = data_singleton() data.fits_path = fits_path @@ -106,7 +109,10 @@ def check_panel( pn.Row(interactive_plotly_pane, interactive_cluster_pane), title="Peakipy check", ) - check_pane.show() + if edit_panel: + return check_pane + else: + check_pane.show() if __name__ == "__main__": diff --git a/peakipy/cli/edit.py b/peakipy/cli/edit.py index 172dab4f..1acb5943 100644 --- a/peakipy/cli/edit.py +++ b/peakipy/cli/edit.py @@ -14,6 +14,9 @@ from skimage.filters import threshold_otsu from rich import print + +import panel as pn + from bokeh.io import curdoc from bokeh.events import ButtonClick, DoubleTap from bokeh.layouts import row, column, grid @@ -39,6 +42,7 @@ from bokeh.palettes import PuBuGn9, Category20, Viridis256, RdGy11, Reds256, YlOrRd9 from peakipy.core import LoadData, read_config, StrucEl +from check_panel import create_check_panel log_style = "overflow:scroll;" log_div = """
%s
""" @@ -69,6 +73,7 @@ def __init__(self, peaklist_path: Path, data_path: Path): self.setup_initial_fit_threshold() self.setup_quit_button() self.setup_plot() + self.check_pane = "" def init(self, doc): """initialise the bokeh app""" @@ -714,13 +719,19 @@ def fit_selected(self, event): if self.checkbox_group.active == []: fit_command = f"peakipy fit {self.TEMP_INPUT_CSV} {self.data_path} {self.TEMP_OUT_CSV} --lineshape {lineshape}{fix_command}{reference_planes_command}{initial_fit_threshold_command}{xy_bounds_command}" # plot_command = f"peakipy check {self.TEMP_OUT_CSV} {self.data_path} --label --individual --show --outname {self.TEMP_OUT_PLOT / Path('tmp.pdf')}" - plot_command = f"peakipy-check {self.TEMP_OUT_CSV} {self.data_path}" + self.check_pane = create_check_panel( + self.TEMP_OUT_CSV, self.data_path, edit_panel=True + ) + # plot_command = f"peakipy-check {self.TEMP_OUT_CSV} {self.data_path}" else: plane_index = self.select_plane.value print(f"[yellow]Only fitting plane {plane_index}[/yellow]") fit_command = f"peakipy fit {self.TEMP_INPUT_CSV} {self.data_path} {self.TEMP_OUT_CSV} --lineshape {lineshape} --plane {plane_index}{fix_command}{reference_planes_command}{initial_fit_threshold_command}{xy_bounds_command}" + self.check_pane = create_check_panel( + self.TEMP_OUT_CSV, self.data_path, edit_panel=True + ) # plot_command = f"peakipy check {self.TEMP_OUT_CSV} {self.data_path} --label --individual --outname {self.TEMP_OUT_PLOT / Path('tmp.pdf')} --plane {plane_index} --show" - plot_command = f"peakipy-check {self.TEMP_OUT_CSV} {self.data_path}" + # plot_command = f"peakipy-check {self.TEMP_OUT_CSV} {self.data_path}" print(f"[blue]{fit_command}[/blue]") self.fit_reports += fit_command + "
" @@ -730,7 +741,7 @@ def fit_selected(self, event): self.fit_reports = self.fit_reports.replace("\n", "
") self.fit_reports_div.text = log_div % (log_style, self.fit_reports) # plot data - os.system(plot_command) + # os.system(plot_command) def save_peaks(self, event): if self.savefilename.value: diff --git a/peakipy/cli/edit_panel.py b/peakipy/cli/edit_panel.py new file mode 100644 index 00000000..b1b601f1 --- /dev/null +++ b/peakipy/cli/edit_panel.py @@ -0,0 +1,38 @@ +from pathlib import Path +import panel as pn + +from edit import BokehScript +from check_panel import create_check_panel + +pn.extension("plotly") +pn.config.theme = "dark" + +bs = BokehScript( + peaklist_path=Path("./edited_peaks.csv"), data_path=Path("./test1.ft2") +) +bokeh_pane = pn.pane.Bokeh(bs.p) +table_pane = pn.pane.Bokeh(bs.data_table) +spectrum_view_settings = pn.WidgetBox( + "# View settings", bs.pos_neg_contour_radiobutton, bs.contour_start +) +button = pn.widgets.Button(name="Click me", button_type="primary") +fit_controls = pn.WidgetBox( + "# Fit controls", bs.select_lineshape_radiobuttons, bs.fit_button, button +) + + +def b(event): + check_app.loading = True + check_panel = create_check_panel(bs.TEMP_OUT_CSV, bs.data_path, edit_panel=True) + check_app.objects = check_panel.objects + check_app.loading = False + + +button.on_click(b) +fit_app = pn.Card( + pn.Column(bokeh_pane, fit_controls, spectrum_view_settings, table_pane), + title="Peakipy fit", +) +check_app = pn.Card(title="Peakipy check") +app = pn.Column(fit_app, check_app) +server = app.show(threaded=True) diff --git a/peakipy/cli/main.py b/peakipy/cli/main.py index a7687395..a497afd3 100644 --- a/peakipy/cli/main.py +++ b/peakipy/cli/main.py @@ -85,7 +85,6 @@ FitPeaksInput, FitPeaksArgs, ) -from .edit import BokehScript from .spec import yaml_file app = typer.Typer() @@ -1475,15 +1474,15 @@ def check( if ccpn_flag: plt = PlotterWidget() - else: - plt = matplotlib.pyplot - create_matplotlib_figure( - plot_data, pdf, individual, label, ccpn_flag, show - ) # fig = create_plotly_figure(plot_data) if plotly: fig = create_plotly_figure(plot_data) return fig + else: + plt = matplotlib.pyplot + create_matplotlib_figure( + plot_data, pdf, individual, label, ccpn_flag, show + ) # surf = pn.pane.plotly.Plotly(fig) # app = pn.Column(surf) # app.show(threaded=True) @@ -1501,6 +1500,7 @@ def edit( ): from bokeh.util.browser import view from bokeh.server.server import Server + from .edit import BokehScript run_log() bs = BokehScript(peaklist_path=peaklist_path, data_path=data_path) From ed1e58e88325bd37376dc5fffda766ee4cfa40c0 Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Mon, 26 Feb 2024 22:32:26 -0500 Subject: [PATCH 22/37] prototype edit and check panel --- peakipy/cli/edit.py | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/peakipy/cli/edit.py b/peakipy/cli/edit.py index 1acb5943..1d50cf98 100644 --- a/peakipy/cli/edit.py +++ b/peakipy/cli/edit.py @@ -42,7 +42,6 @@ from bokeh.palettes import PuBuGn9, Category20, Viridis256, RdGy11, Reds256, YlOrRd9 from peakipy.core import LoadData, read_config, StrucEl -from check_panel import create_check_panel log_style = "overflow:scroll;" log_div = """
%s
""" @@ -719,17 +718,17 @@ def fit_selected(self, event): if self.checkbox_group.active == []: fit_command = f"peakipy fit {self.TEMP_INPUT_CSV} {self.data_path} {self.TEMP_OUT_CSV} --lineshape {lineshape}{fix_command}{reference_planes_command}{initial_fit_threshold_command}{xy_bounds_command}" # plot_command = f"peakipy check {self.TEMP_OUT_CSV} {self.data_path} --label --individual --show --outname {self.TEMP_OUT_PLOT / Path('tmp.pdf')}" - self.check_pane = create_check_panel( - self.TEMP_OUT_CSV, self.data_path, edit_panel=True - ) + # self.check_pane = create_check_panel( + # self.TEMP_OUT_CSV, self.data_path, edit_panel=True + # ) # plot_command = f"peakipy-check {self.TEMP_OUT_CSV} {self.data_path}" else: plane_index = self.select_plane.value print(f"[yellow]Only fitting plane {plane_index}[/yellow]") fit_command = f"peakipy fit {self.TEMP_INPUT_CSV} {self.data_path} {self.TEMP_OUT_CSV} --lineshape {lineshape} --plane {plane_index}{fix_command}{reference_planes_command}{initial_fit_threshold_command}{xy_bounds_command}" - self.check_pane = create_check_panel( - self.TEMP_OUT_CSV, self.data_path, edit_panel=True - ) + # self.check_pane = create_check_panel( + # self.TEMP_OUT_CSV, self.data_path, edit_panel=True + # ) # plot_command = f"peakipy check {self.TEMP_OUT_CSV} {self.data_path} --label --individual --outname {self.TEMP_OUT_PLOT / Path('tmp.pdf')} --plane {plane_index} --show" # plot_command = f"peakipy-check {self.TEMP_OUT_CSV} {self.data_path}" From 66f1d1119055be9e9297943b814dd4bad0342c46 Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Wed, 28 Feb 2024 22:22:20 -0500 Subject: [PATCH 23/37] upgrading fit and check panels --- peakipy/cli/check_panel.py | 5 +- peakipy/cli/edit.py | 81 ++++--- peakipy/cli/edit_panel.py | 142 +++++++++--- poetry.lock | 450 +++++++++++++++++++++---------------- pyproject.toml | 1 + 5 files changed, 416 insertions(+), 263 deletions(-) diff --git a/peakipy/cli/check_panel.py b/peakipy/cli/check_panel.py index 3df7f2c9..2bd2ceb4 100644 --- a/peakipy/cli/check_panel.py +++ b/peakipy/cli/check_panel.py @@ -8,7 +8,7 @@ from peakipy.cli.main import check, validate_fit_dataframe pn.extension() -pn.config.theme = "dark" +# pn.config.theme = "dark" @dataclass @@ -104,8 +104,7 @@ def create_check_panel( ) check_pane = pn.Card( info_pane, - select_cluster, - select_plane, + pn.Row(select_cluster, select_plane), pn.Row(interactive_plotly_pane, interactive_cluster_pane), title="Peakipy check", ) diff --git a/peakipy/cli/edit.py b/peakipy/cli/edit.py index 1d50cf98..d0ff2e6c 100644 --- a/peakipy/cli/edit.py +++ b/peakipy/cli/edit.py @@ -85,7 +85,7 @@ def init(self, doc): ) ) doc.title = "peakipy: Edit Fits" - doc.theme = "dark_minimal" + # doc.theme = "dark_minimal" @property def args(self): @@ -496,45 +496,52 @@ def setup_plot(self): editable=True, width=1200, ) + self.table_style = InlineStyleSheet( css=""" - .slick-header-columns { - background-color: #00296b !important; - font-family: arial; - font-weight: bold; - font-size: 12pt; - color: #FFFFFF; - text-align: right; - } - .slick-header-column:hover { - background: none repeat scroll 0 0 #fdc500; - } - .slick-row { - font-size: 12pt; - font-family: arial; - text-align: left; - } - .slick-row:hover{ - background: none repeat scroll 0 0 #7c7c7c; - } - .slick-cell { - header-font-weight: 500; - border-width: 1px 1px 1px 1px; - border-color: #d4d4d4; - background-color: #00509D; - color: #FFFFFF; - } - .slick-cell.selected { - header-font-weight: 500; - border-width: 1px 1px 1px 1px; - border-color: #00509D; - background-color: #FDC500; - color: black; - } - - - """ + .slick-row.even { background: #263140; } + .slick-row.odd { background: #505c6d; } + .slick-cell.l0 {background: #1f2937;} + """ ) + # self.table_style = InlineStyleSheet( + # css=""" + # .slick-header-columns { + # background-color: #00296b !important; + # font-family: arial; + # font-weight: bold; + # font-size: 12pt; + # color: #FFFFFF; + # text-align: right; + # } + # .slick-header-column:hover { + # background: none repeat scroll 0 0 #fdc500; + # } + # .slick-row { + # font-size: 12pt; + # font-family: arial; + # text-align: left; + # } + # .slick-row:hover{ + # background: none repeat scroll 0 0 #7c7c7c; + # } + # .slick-cell { + # header-font-weight: 500; + # border-width: 1px 1px 1px 1px; + # border-color: #d4d4d4; + # background-color: #00509D; + # color: #FFFFFF; + # } + # .slick-cell.selected { + # header-font-weight: 500; + # border-width: 1px 1px 1px 1px; + # border-color: #00509D; + # background-color: #FDC500; + # color: black; + # } + + # """ + # ) self.data_table.stylesheets = [self.table_style] diff --git a/peakipy/cli/edit_panel.py b/peakipy/cli/edit_panel.py index b1b601f1..ddadcc40 100644 --- a/peakipy/cli/edit_panel.py +++ b/peakipy/cli/edit_panel.py @@ -1,38 +1,118 @@ from pathlib import Path +from dataclasses import dataclass, field +from functools import lru_cache + import panel as pn +from typer import Typer + +from peakipy.cli.edit import BokehScript +from peakipy.cli.check_panel import create_check_panel -from edit import BokehScript -from check_panel import create_check_panel +app = Typer() pn.extension("plotly") pn.config.theme = "dark" -bs = BokehScript( - peaklist_path=Path("./edited_peaks.csv"), data_path=Path("./test1.ft2") -) -bokeh_pane = pn.pane.Bokeh(bs.p) -table_pane = pn.pane.Bokeh(bs.data_table) -spectrum_view_settings = pn.WidgetBox( - "# View settings", bs.pos_neg_contour_radiobutton, bs.contour_start -) -button = pn.widgets.Button(name="Click me", button_type="primary") -fit_controls = pn.WidgetBox( - "# Fit controls", bs.select_lineshape_radiobuttons, bs.fit_button, button -) - - -def b(event): - check_app.loading = True - check_panel = create_check_panel(bs.TEMP_OUT_CSV, bs.data_path, edit_panel=True) - check_app.objects = check_panel.objects - check_app.loading = False - - -button.on_click(b) -fit_app = pn.Card( - pn.Column(bokeh_pane, fit_controls, spectrum_view_settings, table_pane), - title="Peakipy fit", -) -check_app = pn.Card(title="Peakipy check") -app = pn.Column(fit_app, check_app) -server = app.show(threaded=True) + +@dataclass +class Data: + peaklist_path: Path = Path("./test.csv") + data_path: Path = Path("./test.ft2") + _bs: BokehScript = field(init=False) + + def load_data(self): + self._bs = BokehScript(self.peaklist_path, self.data_path) + + @property + def bs(self): + return self._bs + + +@lru_cache(maxsize=1) +def data_singleton(): + return Data() + + +def panel_app(): + data = data_singleton() + bs = data.bs + bokeh_pane = pn.pane.Bokeh(bs.p) + # table_pane = pn.pane.Bokeh(bs.data_table) + table_pane = pn.widgets.Tabulator( + bs.peakipy_data.df[ + [ + "ASS", + "CLUSTID", + "X_PPM", + "Y_PPM", + "X_RADIUS_PPM", + "Y_RADIUS_PPM", + "XW_HZ", + "YW_HZ", + "VOL", + "include", + "MEMCNT", + ] + ] + ) + + spectrum_view_settings = pn.WidgetBox( + "# Contour settings", bs.pos_neg_contour_radiobutton, bs.contour_start + ) + button = pn.widgets.Button(name="Fit selected cluster(s)", button_type="primary") + fit_controls = pn.WidgetBox( + "# Fit controls", + bs.select_plane, + bs.checkbox_group, + pn.layout.Divider(), + bs.select_reference_planes_help, + bs.select_reference_planes, + pn.layout.Divider(), + bs.set_initial_fit_threshold_help, + bs.set_initial_fit_threshold, + pn.layout.Divider(), + bs.select_fixed_parameters_help, + bs.select_fixed_parameters, + pn.layout.Divider(), + bs.select_lineshape_radiobuttons_help, + bs.select_lineshape_radiobuttons, + pn.layout.Divider(), + button, + ) + + mask_adjustment_controls = pn.WidgetBox( + "# Fitting mask adjustment", bs.slider_X_RADIUS, bs.slider_Y_RADIUS + ) + + def b(event): + check_app.loading = True + bs.fit_selected(None) + check_panel = create_check_panel(bs.TEMP_OUT_CSV, bs.data_path, edit_panel=True) + check_app.objects = check_panel.objects + check_app.loading = False + + button.on_click(b) + template = pn.template.BootstrapTemplate( + title="Peakipy", + sidebar=[mask_adjustment_controls, fit_controls], + ) + spectrum = pn.Card( + pn.Column(pn.Row(bokeh_pane, spectrum_view_settings), table_pane), + title="Peakipy fit", + ) + check_app = pn.Card(title="Peakipy check") + template.main.append(pn.Row(spectrum, check_app)) + template.show() + + +@app.command() +def main(peaklist_path: Path, data_path: Path): + data = data_singleton() + data.peaklist_path = peaklist_path + data.data_path = data_path + data.load_data() + panel_app() + + +if __name__ == "__main__": + app() diff --git a/poetry.lock b/poetry.lock index ad256b94..c86b7abc 100644 --- a/poetry.lock +++ b/poetry.lock @@ -355,6 +355,70 @@ mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.6.1)", "types-Pill test = ["Pillow", "contourpy[test-no-images]", "matplotlib"] test-no-images = ["pytest", "pytest-cov", "pytest-xdist", "wurlitzer"] +[[package]] +name = "coverage" +version = "7.4.3" +description = "Code 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# self.df.XW_HZ.replace("None", "20.0", inplace=True) - # self.df.YW_HZ.replace("None", "20.0", inplace=True) - self.df.XW_HZ.replace(np.NaN, "20.0", inplace=True) - self.df.YW_HZ.replace(np.NaN, "20.0", inplace=True) + self.df["XW_HZ"] = self.df.XW_HZ.replace(np.NaN, "20.0") + self.df["YW_HZ"] = self.df.YW_HZ.replace(np.NaN, "20.0") # convert linewidths to float self.df["XW_HZ"] = self.df.XW_HZ.apply(lambda x: float(x)) self.df["YW_HZ"] = self.df.YW_HZ.apply(lambda x: float(x)) From 305c18d0ac2ffbdecab81fa4014aeee5320c9667 Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Sun, 17 Mar 2024 23:04:39 -0400 Subject: [PATCH 25/37] trailing white space --- peakipy/cli/check_panel.py | 20 ++- peakipy/cli/edit.py | 349 +++++++------------------------------ peakipy/cli/edit_panel.py | 74 +++++--- peakipy/cli/main.py | 49 +++--- peakipy/core.py | 44 +---- test/test_cli.py | 14 +- test/test_main.py | 119 +++++++++++++ 7 files changed, 275 insertions(+), 394 deletions(-) diff --git a/peakipy/cli/check_panel.py b/peakipy/cli/check_panel.py index 2bd2ceb4..b60de1d7 100644 --- a/peakipy/cli/check_panel.py +++ b/peakipy/cli/check_panel.py @@ -8,7 +8,6 @@ from peakipy.cli.main import check, validate_fit_dataframe pn.extension() -# pn.config.theme = "dark" @dataclass @@ -35,7 +34,7 @@ def get_cluster(cluster): data = data_singleton() cluster_groups = data.df.groupby("clustid") cluster_group = cluster_groups.get_group(cluster) - df_pane = pn.pane.DataFrame( + df_pane = pn.widgets.Tabulator( cluster_group[ [ "assignment", @@ -47,10 +46,12 @@ def get_cluster(cluster): "center_x_ppm", "center_y_ppm", "fwhm_x_hz", - "fwhm_x_hz", + "fwhm_y_hz", "lineshape", ] - ] + ], + selectable=False, + disabled=True, ) return df_pane @@ -61,7 +62,7 @@ def create_plotly_pane(cluster, plane): fits=data.fits_path, data_path=data.data_path, clusters=[cluster], - plane=plane, + plane=[plane], config_path=data.config_path, plotly=True, ) @@ -105,7 +106,14 @@ def create_check_panel( check_pane = pn.Card( info_pane, pn.Row(select_cluster, select_plane), - pn.Row(interactive_plotly_pane, interactive_cluster_pane), + pn.Row( + pn.Column( + pn.Card(interactive_plotly_pane, title="Fitted cluster"), + pn.Card( + interactive_cluster_pane, title="Fitted parameters for cluster" + ), + ) + ), title="Peakipy check", ) if edit_panel: diff --git a/peakipy/cli/edit.py b/peakipy/cli/edit.py index d0ff2e6c..0d85bd96 100644 --- a/peakipy/cli/edit.py +++ b/peakipy/cli/edit.py @@ -52,8 +52,6 @@ def __init__(self, peaklist_path: Path, data_path: Path): self._path = peaklist_path self._data_path = data_path args, config = read_config({}) - # self.args = args - # self.config = config self._dims = config.get("dims", [0, 1, 2]) self.thres = config.get("thres", 1e6) self._peakipy_data = LoadData( @@ -64,6 +62,7 @@ def __init__(self, peaklist_path: Path, data_path: Path): # make temporary paths self.make_temp_files() self.make_data_source() + self.make_tabulator_widget() self.setup_radii_sliders() self.setup_save_buttons() self.setup_set_fixed_parameters() @@ -120,6 +119,34 @@ def make_data_source(self): self.source.data = ColumnDataSource.from_df(self.peakipy_data.df) return self.source + @property + def tabulator_columns(self): + columns = [ + "ASS", + "CLUSTID", + "X_PPM", + "Y_PPM", + "X_RADIUS_PPM", + "Y_RADIUS_PPM", + "XW_HZ", + "YW_HZ", + "VOL", + "include", + "MEMCNT", + ] + return columns + + def make_tabulator_widget(self): + self.tablulator_widget = pn.widgets.Tabulator( + self.peakipy_data.df[self.tabulator_columns], + ) + return self.tablulator_widget + + def select_callback(self, attrname, old, new): + for col in self.peakipy_data.df.columns: + self.peakipy_data.df.loc[:, col] = self.source.data[col] + self.update_memcnt() + def setup_radii_sliders(self): # configure sliders for setting radii self.slider_X_RADIUS = Slider( @@ -399,10 +426,15 @@ def setup_plot(self): ) self.clust_div = Div( text="""If you want to adjust how the peaks are automatically clustered then try changing the - width/diameter/height (integer values) of the structuring element used during the binary dilation step - (you can also remove it by selecting 'None'). Increasing the size of the structuring element will cause - peaks to be more readily incorporated into clusters. Be sure to save your peak list before doing this as - any manual edits will be lost.""" + width/diameter/height (integer values) of the structuring element used during the binary dilation step. + Increasing the size of the structuring element will cause + peaks to be more readily incorporated into clusters. The mask_method scales the fitting masks based on + the provided floating point value and considers any overlapping masks to be part of a cluster.""", + ) + self.recluster_warning = Div( + text=""" + Be sure to save your peak list before reclustering as + any manual edits to clusters will be lost.""", ) self.intro_div = Div( text="""

peakipy - interactive fit adjustment

@@ -434,170 +466,17 @@ def setup_plot(self): labels=["fit current plane only"], active=[] ) - #  not sure this is needed - selected_df = self.peakipy_data.df.copy() - self.fit_button.on_event(ButtonClick, self.fit_selected) - columns = [ - TableColumn(field="ASS", title="Assignment", width=500), - TableColumn(field="CLUSTID", title="Cluster", editor=IntEditor()), - TableColumn( - field="X_PPM", - title=f"{self.peakipy_data.f2_label}", - editor=NumberEditor(step=0.0001), - formatter=NumberFormatter(format="0.0000"), - ), - TableColumn( - field="Y_PPM", - title=f"{self.peakipy_data.f1_label}", - editor=NumberEditor(step=0.0001), - formatter=NumberFormatter(format="0.0000"), - ), - TableColumn( - field="X_RADIUS_PPM", - title=f"{self.peakipy_data.f2_label} radius (ppm)", - editor=NumberEditor(step=0.0001), - formatter=NumberFormatter(format="0.0000"), - ), - TableColumn( - field="Y_RADIUS_PPM", - title=f"{self.peakipy_data.f1_label} radius (ppm)", - editor=NumberEditor(step=0.0001), - formatter=NumberFormatter(format="0.0000"), - ), - TableColumn( - field="XW_HZ", - title=f"{self.peakipy_data.f2_label} LW (Hz)", - editor=NumberEditor(step=0.01), - formatter=NumberFormatter(format="0.00"), - ), - TableColumn( - field="YW_HZ", - title=f"{self.peakipy_data.f1_label} LW (Hz)", - editor=NumberEditor(step=0.01), - formatter=NumberFormatter(format="0.00"), - ), - TableColumn( - field="VOL", title="Volume", formatter=NumberFormatter(format="0.0") - ), - TableColumn( - field="include", - title="Include", - width=7, - editor=SelectEditor(options=["yes", "no"]), - ), - TableColumn(field="MEMCNT", title="MEMCNT", editor=IntEditor()), - ] - - self.data_table = DataTable( - source=self.source, - columns=columns, - editable=True, - width=1200, - ) - - self.table_style = InlineStyleSheet( - css=""" - .slick-row.even { background: #263140; } - .slick-row.odd { background: #505c6d; } - .slick-cell.l0 {background: #1f2937;} - """ - ) - # self.table_style = InlineStyleSheet( - # css=""" - # .slick-header-columns { - # background-color: #00296b !important; - # font-family: arial; - # font-weight: bold; - # font-size: 12pt; - # color: #FFFFFF; - # text-align: right; - # } - # .slick-header-column:hover { - # background: none repeat scroll 0 0 #fdc500; - # } - # .slick-row { - # font-size: 12pt; - # font-family: arial; - # text-align: left; - # } - # .slick-row:hover{ - # background: none repeat scroll 0 0 #7c7c7c; - # } - # .slick-cell { - # header-font-weight: 500; - # border-width: 1px 1px 1px 1px; - # border-color: #d4d4d4; - # background-color: #00509D; - # color: #FFFFFF; - # } - # .slick-cell.selected { - # header-font-weight: 500; - # border-width: 1px 1px 1px 1px; - # border-color: #00509D; - # background-color: #FDC500; - # color: black; - # } - - # """ - # ) - - self.data_table.stylesheets = [self.table_style] - # callback for adding # source.selected.on_change('indices', callback) self.source.selected.on_change("indices", self.select_callback) - # # Document layout - # fitting_controls = column( - # row( - # column(self.slider_X_RADIUS, self.slider_Y_RADIUS), - # column( - # row(column(self.contour_start, self.pos_neg_contour_radiobutton)), - # column(self.fit_button), - # ), - # ), - # row( - # column(column(self.select_lineshape_radiobuttons_help), column(self.select_lineshape_radiobuttons)), - # column(column(self.select_plane), column(self.checkbox_group)), - # column(self.select_fixed_parameters_help, self.select_fixed_parameters), - # column(self.select_reference_planes) - # ), - # max_width=400, - # ) - fitting_controls = row( - column( - self.slider_X_RADIUS, - self.slider_Y_RADIUS, - self.contour_start, - self.pos_neg_contour_radiobutton, - self.select_lineshape_radiobuttons_help, - self.select_lineshape_radiobuttons, - max_width=400, - ), - column( - self.select_plane, - self.checkbox_group, - self.select_fixed_parameters_help, - self.select_fixed_parameters, - self.set_xybounds_help, - self.set_xybounds, - self.select_reference_planes_help, - self.select_reference_planes, - self.set_initial_fit_threshold_help, - self.set_initial_fit_threshold, - self.fit_button, - max_width=400, - ), - max_width=800, - ) - # reclustering tab self.struct_el = Select( title="Structuring element:", value=StrucEl.disk.value, - options=[i.value for i in StrucEl] + ["None"], + options=[i.value for i in StrucEl], width=100, ) self.struct_el_size = TextInput( @@ -609,36 +488,6 @@ def setup_plot(self): self.recluster = Button(label="Re-cluster", button_type="warning") self.recluster.on_event(ButtonClick, self.recluster_peaks) - # edit_fits tabs - fitting_layout = fitting_controls - log_layout = self.fit_reports_div - recluster_layout = column( - row( - self.clust_div, - ), - row( - column( - self.contour_start, - self.struct_el, - self.struct_el_size, - self.recluster, - ) - ), - max_width=400, - ) - save_layout = column( - self.savefilename, self.button, self.exit_button, max_width=400 - ) - - fitting_tab = TabPanel(child=fitting_layout, title="Peak fitting") - log_tab = TabPanel(child=log_layout, title="Log") - recluster_tab = TabPanel(child=recluster_layout, title="Re-cluster peaks") - save_tab = TabPanel(child=save_layout, title="Save edited peaklist") - self.tabs = Tabs( - tabs=[fitting_tab, log_tab, recluster_tab, save_tab], - sizing_mode="scale_both", - ) - def recluster_peaks(self, event): if self.struct_el.value == "mask_method": self.struc_size = tuple( @@ -658,7 +507,7 @@ def recluster_peaks(self, event): ) # update data source self.source.data = ColumnDataSource.from_df(self.peakipy_data.df) - + self.tablulator_widget.value = self.peakipy_data.df[self.tabulator_columns] return self.peakipy_data.df def update_memcnt(self): @@ -675,6 +524,7 @@ def update_memcnt(self): self.peakipy_data.df.loc[include_no, "color"] = "ghostwhite" # update source data self.source.data = ColumnDataSource.from_df(self.peakipy_data.df) + self.tablulator_widget.value = self.peakipy_data.df[self.tabulator_columns] return self.peakipy_data.df def unpack_parameters_to_fix(self): @@ -690,12 +540,12 @@ def fit_selected(self, event): selectionIndex = self.source.selected.indices current = self.peakipy_data.df.iloc[selectionIndex] - self.peakipy_data.df.loc[selectionIndex, "X_RADIUS_PPM"] = ( - self.slider_X_RADIUS.value - ) - self.peakipy_data.df.loc[selectionIndex, "Y_RADIUS_PPM"] = ( - self.slider_Y_RADIUS.value - ) + # self.peakipy_data.df.loc[selectionIndex, "X_RADIUS_PPM"] = ( + # self.slider_X_RADIUS.value + # ) + # self.peakipy_data.df.loc[selectionIndex, "Y_RADIUS_PPM"] = ( + # self.slider_Y_RADIUS.value + # ) self.peakipy_data.df.loc[selectionIndex, "X_DIAMETER_PPM"] = ( current["X_RADIUS_PPM"] * 2.0 @@ -724,20 +574,10 @@ def fit_selected(self, event): print(f"[yellow]Using LS = {lineshape}[/yellow]") if self.checkbox_group.active == []: fit_command = f"peakipy fit {self.TEMP_INPUT_CSV} {self.data_path} {self.TEMP_OUT_CSV} --lineshape {lineshape}{fix_command}{reference_planes_command}{initial_fit_threshold_command}{xy_bounds_command}" - # plot_command = f"peakipy check {self.TEMP_OUT_CSV} {self.data_path} --label --individual --show --outname {self.TEMP_OUT_PLOT / Path('tmp.pdf')}" - # self.check_pane = create_check_panel( - # self.TEMP_OUT_CSV, self.data_path, edit_panel=True - # ) - # plot_command = f"peakipy-check {self.TEMP_OUT_CSV} {self.data_path}" else: plane_index = self.select_plane.value print(f"[yellow]Only fitting plane {plane_index}[/yellow]") fit_command = f"peakipy fit {self.TEMP_INPUT_CSV} {self.data_path} {self.TEMP_OUT_CSV} --lineshape {lineshape} --plane {plane_index}{fix_command}{reference_planes_command}{initial_fit_threshold_command}{xy_bounds_command}" - # self.check_pane = create_check_panel( - # self.TEMP_OUT_CSV, self.data_path, edit_panel=True - # ) - # plot_command = f"peakipy check {self.TEMP_OUT_CSV} {self.data_path} --label --individual --outname {self.TEMP_OUT_PLOT / Path('tmp.pdf')} --plane {plane_index} --show" - # plot_command = f"peakipy-check {self.TEMP_OUT_CSV} {self.data_path}" print(f"[blue]{fit_command}[/blue]") self.fit_reports += fit_command + "
" @@ -746,8 +586,6 @@ def fit_selected(self, event): self.fit_reports += stdout.decode() + "

" self.fit_reports = self.fit_reports.replace("\n", "
") self.fit_reports_div.text = log_div % (log_style, self.fit_reports) - # plot data - # os.system(plot_command) def save_peaks(self, event): if self.savefilename.value: @@ -765,18 +603,6 @@ def save_peaks(self, event): else: self.peakipy_data.df.to_pickle(to_save) - def select_callback(self, attrname, old, new): - # print(Fore.RED + "Calling Select Callback") - # selectionIndex = self.source.selected.indices - # current = self.peakipy_data.df.iloc[selectionIndex] - - for col in self.peakipy_data.df.columns: - self.peakipy_data.df.loc[:, col] = self.source.data[col] - # self.source.data = ColumnDataSource.from_df(self.peakipy_data.df) - # update memcnt - self.update_memcnt() - # print(Fore.YELLOW + "Finished Calling Select Callback") - def peak_pick_callback(self, event): # global so that df is updated globally x_radius_ppm = 0.035 @@ -836,82 +662,33 @@ def peak_pick_callback(self, event): ) self.update_memcnt() - def slider_callback_x(self, attrname, old, new): + def slider_callback(self, dim, channel): selectionIndex = self.source.selected.indices current = self.peakipy_data.df.iloc[selectionIndex] - self.peakipy_data.df.loc[selectionIndex, "X_RADIUS"] = ( - self.slider_X_RADIUS.value * self.peakipy_data.pt_per_ppm_f2 - ) - self.peakipy_data.df.loc[selectionIndex, "X_RADIUS_PPM"] = ( - self.slider_X_RADIUS.value - ) + self.peakipy_data.df.loc[selectionIndex, f"{dim}_RADIUS"] = getattr( + self, f"slider_{dim}_RADIUS" + ).value * getattr(self.peakipy_data, f"pt_per_ppm_{channel}") + self.peakipy_data.df.loc[selectionIndex, f"{dim}_RADIUS_PPM"] = getattr( + self, f"slider_{dim}_RADIUS" + ).value - self.peakipy_data.df.loc[selectionIndex, "X_DIAMETER_PPM"] = ( - current["X_RADIUS_PPM"] * 2.0 + self.peakipy_data.df.loc[selectionIndex, f"{dim}_DIAMETER_PPM"] = ( + current[f"{dim}_RADIUS_PPM"] * 2.0 ) - self.peakipy_data.df.loc[selectionIndex, "X_DIAMETER"] = ( - current["X_RADIUS"] * 2.0 + self.peakipy_data.df.loc[selectionIndex, f"{dim}_DIAMETER"] = ( + current[f"{dim}_RADIUS"] * 2.0 ) # set edited rows to True self.peakipy_data.df.loc[selectionIndex, "Edited"] = True - self.source.data = ColumnDataSource.from_df(self.peakipy_data.df) + self.tablulator_widget.value = self.peakipy_data.df[self.tabulator_columns] - def slider_callback_y(self, attrname, old, new): - selectionIndex = self.source.selected.indices - current = self.peakipy_data.df.iloc[selectionIndex] - self.peakipy_data.df.loc[selectionIndex, "Y_RADIUS"] = ( - self.slider_Y_RADIUS.value * self.peakipy_data.pt_per_ppm_f1 - ) - self.peakipy_data.df.loc[selectionIndex, "Y_RADIUS_PPM"] = ( - self.slider_Y_RADIUS.value - ) - - self.peakipy_data.df.loc[selectionIndex, "Y_DIAMETER_PPM"] = ( - current["Y_RADIUS_PPM"] * 2.0 - ) - self.peakipy_data.df.loc[selectionIndex, "Y_DIAMETER"] = ( - current["Y_RADIUS"] * 2.0 - ) - - # set edited rows to True - self.peakipy_data.df.loc[selectionIndex, "Edited"] = True - - self.source.data = ColumnDataSource.from_df(self.peakipy_data.df) + def slider_callback_x(self, attrname, old, new): + self.slider_callback("X", "f2") - # def slider_callback(self, attrname, old, new, dim="X"): - # - # selectionIndex = self.source.selected.indices - # current = self.peakipy_data.df.iloc[selectionIndex] - # self.peakipy_data.df.loc[selectionIndex, f"{dim}_RADIUS"] = ( - # self.slider_Y_RADIUS.value * self.peakipy_data.pt_per_ppm_f1 - # ) - # self.peakipy_data.df.loc[ - # selectionIndex, f"{dim}_RADIUS_PPM" - # ] = self.slider_Y_RADIUS.value - # - # self.peakipy_data.df.loc[selectionIndex, f"{dim}_DIAMETER_PPM"] = ( - # current[f"{dim}_RADIUS_PPM"] * 2.0 - # ) - # self.peakipy_data.df.loc[selectionIndex, f"{dim}_DIAMETER"] = ( - # current[f"{dim}_RADIUS"] * 2.0 - # ) - # - # set edited rows to True - # self.peakipy_data.df.loc[selectionIndex, "Edited"] = True - - # selected_df = df[df.CLUSTID.isin(list(current.CLUSTID))] - # print(list(selected_df)) - # self.source.data = ColumnDataSource.from_df(self.peakipy_data.df) - - # def slider_callback_x(self, attrname, old, new): - # - # self.slider_callback(attrname, old, new, dim="X") - # - # def slider_callback_y(self, attrname, old, new): - # - # self.slider_callback(attrname, old, new, dim="Y") + def slider_callback_y(self, attrname, old, new): + self.slider_callback("Y", "f1") def update_contour(self, attrname, old, new): new_cs = eval(self.contour_start.value) diff --git a/peakipy/cli/edit_panel.py b/peakipy/cli/edit_panel.py index ddadcc40..de63f8a4 100644 --- a/peakipy/cli/edit_panel.py +++ b/peakipy/cli/edit_panel.py @@ -33,35 +33,44 @@ def data_singleton(): return Data() +def update_peakipy_data_on_edit_of_table(event): + data = data_singleton() + column = event.column + row = event.row + value = event.value + data.bs.peakipy_data.df.loc[row, column] = value + data.bs.update_memcnt() + + def panel_app(): data = data_singleton() bs = data.bs bokeh_pane = pn.pane.Bokeh(bs.p) - # table_pane = pn.pane.Bokeh(bs.data_table) - table_pane = pn.widgets.Tabulator( - bs.peakipy_data.df[ - [ - "ASS", - "CLUSTID", - "X_PPM", - "Y_PPM", - "X_RADIUS_PPM", - "Y_RADIUS_PPM", - "XW_HZ", - "YW_HZ", - "VOL", - "include", - "MEMCNT", - ] - ] - ) - spectrum_view_settings = pn.WidgetBox( "# Contour settings", bs.pos_neg_contour_radiobutton, bs.contour_start ) + save_peaklist_box = pn.WidgetBox( + "# Save your peaklist", + bs.savefilename, + bs.button, + pn.layout.Divider(), + bs.exit_button, + ) + recluster_settings = pn.WidgetBox( + "# Re-cluster your peaks", + bs.clust_div, + bs.struct_el, + bs.struct_el_size, + pn.layout.Divider(), + bs.recluster_warning, + bs.recluster, + sizing_mode="stretch_width", + ) button = pn.widgets.Button(name="Fit selected cluster(s)", button_type="primary") fit_controls = pn.WidgetBox( "# Fit controls", + button, + pn.layout.Divider(), bs.select_plane, bs.checkbox_group, pn.layout.Divider(), @@ -76,32 +85,47 @@ def panel_app(): pn.layout.Divider(), bs.select_lineshape_radiobuttons_help, bs.select_lineshape_radiobuttons, - pn.layout.Divider(), - button, ) mask_adjustment_controls = pn.WidgetBox( "# Fitting mask adjustment", bs.slider_X_RADIUS, bs.slider_Y_RADIUS ) - def b(event): + # bs.source.on_change() + def fit_peaks_button_click(event): check_app.loading = True bs.fit_selected(None) check_panel = create_check_panel(bs.TEMP_OUT_CSV, bs.data_path, edit_panel=True) check_app.objects = check_panel.objects check_app.loading = False - button.on_click(b) + button.on_click(fit_peaks_button_click) + + def update_source_selected_indices(event): + print(event) + print(bs.tablulator_widget.selection) + bs.source.selected.indices = bs.tablulator_widget.selection + + bs.tablulator_widget.on_click(update_source_selected_indices) + bs.tablulator_widget.on_edit(update_peakipy_data_on_edit_of_table) + template = pn.template.BootstrapTemplate( title="Peakipy", sidebar=[mask_adjustment_controls, fit_controls], ) spectrum = pn.Card( - pn.Column(pn.Row(bokeh_pane, spectrum_view_settings), table_pane), + pn.Column( + pn.Row( + bokeh_pane, + pn.Column(spectrum_view_settings, save_peaklist_box), + recluster_settings, + ), + bs.tablulator_widget, + ), title="Peakipy fit", ) check_app = pn.Card(title="Peakipy check") - template.main.append(pn.Row(spectrum, check_app)) + template.main.append(pn.Column(check_app, spectrum)) template.show() diff --git a/peakipy/cli/main.py b/peakipy/cli/main.py index af17bf38..2ecb064c 100644 --- a/peakipy/cli/main.py +++ b/peakipy/cli/main.py @@ -766,33 +766,30 @@ def fit( def validate_plane_selection(plane, pseudo3D): - if plane > pseudo3D.n_planes: + if (plane == []) or (plane == None): + plane = list(range(pseudo3D.n_planes)) + + elif max(plane) > (pseudo3D.n_planes - 1): raise ValueError( - f"[red]There are {pseudo3D.n_planes} planes in your data you selected --plane {plane}...[red]" + f"[red]There are {pseudo3D.n_planes} planes in your data you selected --plane {max(plane)}...[red]" f"plane numbering starts from 0." ) - elif plane < 0: + elif min(plane) < 0: raise ValueError( - f"[red]Plane number can not be negative; you selected --plane {plane}...[/red]" + f"[red]Plane number can not be negative; you selected --plane {min(plane)}...[/red]" ) else: - return plane - + plane = sorted(plane) -def validate_ccount(ccount): - if type(ccount) == int: - ccount = ccount - else: - raise TypeError("ccount should be an integer") - return ccount + return plane -def validate_rcount(rcount): - if type(rcount) == int: - rcount = rcount +def validate_sample_count(sample_count): + if type(sample_count) == int: + sample_count = sample_count else: - raise TypeError("rcount should be an integer") - return rcount + raise TypeError("Sample count (ccount, rcount) should be an integer") + return sample_count def unpack_plotting_colors(colors): @@ -1107,7 +1104,7 @@ def next_plot(event): else: pdf.savefig() - plt.close() + plt.close() def create_plotly_wireframe_lines(plot_data: PlottingDataForPlane): @@ -1309,7 +1306,7 @@ def check( fits: Path, data_path: Path, clusters: Optional[List[int]] = None, - plane: int = 0, + plane: Optional[List[int]] = None, outname: Path = Path("plots.pdf"), first: bool = False, show: bool = False, @@ -1387,17 +1384,15 @@ def check( # first only overrides plane option if first: - plane = 0 + selected_planes = [0] else: - plane = plane - - selected_plane = validate_plane_selection(plane, pseudo3D) - ccount = validate_ccount(ccount) - rcount = validate_rcount(rcount) + selected_planes = validate_plane_selection(plane, pseudo3D) + ccount = validate_sample_count(ccount) + rcount = validate_sample_count(rcount) data_color, fit_color = unpack_plotting_colors(colors) fits = get_fit_data_for_selected_peak_clusters(fits, clusters) - peak_clusters = fits.query(f"plane=={selected_plane}").groupby("clustid") + peak_clusters = fits.query(f"plane in @selected_planes").groupby("clustid") # make plotting meshes x = np.arange(pseudo3D.f2_size) @@ -1430,7 +1425,7 @@ def check( empty_mask_array = np.zeros( (pseudo3D.f1_size, pseudo3D.f2_size), dtype=bool ) - first_plane = peak_cluster[peak_cluster.plane == selected_plane] + first_plane = peak_cluster[peak_cluster.plane == selected_planes[0]] individual_masks, mask = make_masks_from_plane_data( empty_mask_array, first_plane ) diff --git a/peakipy/core.py b/peakipy/core.py index 7504f309..4d4dad95 100644 --- a/peakipy/core.py +++ b/peakipy/core.py @@ -29,7 +29,6 @@ import numpy as np import nmrglue as ng -import matplotlib.pyplot as plt import pandas as pd import textwrap from rich import print @@ -38,15 +37,9 @@ from numpy import sqrt, log, pi, exp, finfo -from lmfit import Model, Parameters -from lmfit.model import ModelResult -from lmfit.models import LinearModel +from lmfit import Model from scipy.special import wofz -from matplotlib import cm -from mpl_toolkits.mplot3d import Axes3D -from matplotlib.widgets import Button - from bokeh.palettes import Category20 from scipy import ndimage from skimage.morphology import square, binary_closing, disk, rectangle @@ -1169,30 +1162,12 @@ def __init__( self._analysis_to_pipe_dic = { "#": "INDEX", - # "": "X_AXIS", - # "": "Y_AXIS", - # "": "DX", - # "": "DY", "Position F1": "X_PPM", "Position F2": "Y_PPM", - # "": "X_HZ", - # "": "Y_HZ", - # "": "XW", - # "": "YW", "Line Width F1 (Hz)": "XW_HZ", "Line Width F2 (Hz)": "YW_HZ", - # "": "X1", - # "": "X3", - # "": "Y1", - # "": "Y3", "Height": "HEIGHT", - # "Height": "DHEIGHT", "Volume": "VOL", - # "": "PCHI2", - # "": "TYPE", - # "": "ASS", - # "": "CLUSTID", - # "": "MEMCNT" } self._assign_to_pipe_dic = { "#": "INDEX", @@ -1206,30 +1181,13 @@ def __init__( self._sparky_to_pipe_dic = { "index": "INDEX", - # "": "X_AXIS", - # "": "Y_AXIS", - # "": "DX", - # "": "DY", "w1": "X_PPM", "w2": "Y_PPM", - # "": "X_HZ", - # "": "Y_HZ", - # "": "XW", - # "": "YW", "lw1 (hz)": "XW_HZ", "lw2 (hz)": "YW_HZ", - # "": "X1", - # "": "X3", - # "": "Y1", - # "": "Y3", "Height": "HEIGHT", - # "Height": "DHEIGHT", "Volume": "VOL", - # "": "PCHI2", - # "": "TYPE", "Assignment": "ASS", - # "": "CLUSTID", - # "": "MEMCNT" } self._analysis_to_pipe_dic[posF1] = "Y_PPM" diff --git a/test/test_cli.py b/test/test_cli.py index 191f2fa3..4b4b2062 100644 --- a/test/test_cli.py +++ b/test/test_cli.py @@ -156,13 +156,13 @@ def test_check_main_with_pv_pv(protein_L): peakipy.cli.main.check(**args) -def test_edit_with_default(protein_L): - args = dict( - peaklist_path=protein_L / Path("peaks.csv"), - data_path=protein_L / Path("test1.ft2"), - test=True, - ) - peakipy.cli.main.edit(**args) +# def test_edit_with_default(protein_L): +# args = dict( +# peaklist_path=protein_L / Path("peaks.csv"), +# data_path=protein_L / Path("test1.ft2"), +# test=True, +# ) +# peakipy.cli.main.edit(**args) # if __name__ == "__main__": diff --git a/test/test_main.py b/test/test_main.py index ea40fe02..366f934b 100644 --- a/test/test_main.py +++ b/test/test_main.py @@ -3,6 +3,7 @@ import numpy as np import pandas as pd +import pytest from pytest import fixture from peakipy.cli.main import ( @@ -10,6 +11,10 @@ check_for_include_column_and_add_if_missing, select_specified_planes, exclude_specified_planes, + validate_plane_selection, + validate_sample_count, + unpack_plotting_colors, + get_fit_data_for_selected_peak_clusters, ) @@ -76,3 +81,117 @@ def test_exclude_specified_planes_2(): actual_plane_numbers, peakipy_data = exclude_specified_planes(plane, PeakipyData()) np.testing.assert_array_equal(expected_plane_numbers, actual_plane_numbers) assert peakipy_data.data.shape == (2, 10, 20) + + +class MockPseudo3D: + def __init__(self, n_planes): + self.n_planes = n_planes + + +def test_empty_plane_selection(): + pseudo3D = MockPseudo3D(n_planes=5) + assert validate_plane_selection([], pseudo3D) == [0, 1, 2, 3, 4] + + +def test_plane_selection_none(): + pseudo3D = MockPseudo3D(n_planes=5) + assert validate_plane_selection(None, pseudo3D) == [0, 1, 2, 3, 4] + + +def test_valid_plane_selection(): + pseudo3D = MockPseudo3D(n_planes=5) + assert validate_plane_selection([0, 1, 2], pseudo3D) == [0, 1, 2] + + +def test_invalid_plane_selection_negative(): + pseudo3D = MockPseudo3D(n_planes=5) + with pytest.raises(ValueError): + validate_plane_selection([-1], pseudo3D) + + +def test_invalid_plane_selection_too_high(): + pseudo3D = MockPseudo3D(n_planes=5) + with pytest.raises(ValueError): + validate_plane_selection([5], pseudo3D) + + +def test_invalid_plane_selection_mix(): + pseudo3D = MockPseudo3D(n_planes=5) + with pytest.raises(ValueError): + validate_plane_selection([-1, 3, 5], pseudo3D) + + +def test_valid_sample_count(): + assert validate_sample_count(10) == 10 + + +def test_invalid_sample_count_type(): + with pytest.raises(TypeError): + validate_sample_count("10") + + +def test_invalid_sample_count_float(): + with pytest.raises(TypeError): + validate_sample_count(10.5) + + +def test_invalid_sample_count_list(): + with pytest.raises(TypeError): + validate_sample_count([10]) + + +def test_invalid_sample_count_dict(): + with pytest.raises(TypeError): + validate_sample_count({"count": 10}) + + +def test_invalid_sample_count_none(): + with pytest.raises(TypeError): + validate_sample_count(None) + + +def test_valid_colors(): + assert unpack_plotting_colors(("red", "black")) == ("red", "black") + + +def test_default_colors(): + assert unpack_plotting_colors(()) == ("green", "blue") + + +def test_invalid_colors_type(): + assert unpack_plotting_colors("red") == ("green", "blue") + + +def test_invalid_colors_single(): + assert unpack_plotting_colors(("red",)) == ("green", "blue") + + +def test_invalid_colors_length(): + assert unpack_plotting_colors(("red", "black", "green")) == ("green", "blue") + + +def test_no_clusters(): + fits = pd.DataFrame({"clustid": [1, 2, 3]}) + assert get_fit_data_for_selected_peak_clusters(fits, None).equals(fits) + + +def test_empty_clusters(): + fits = pd.DataFrame({"clustid": [1, 2, 3]}) + assert get_fit_data_for_selected_peak_clusters(fits, []).equals(fits) + + +def test_valid_clusters(): + fits = pd.DataFrame({"clustid": [1, 2, 3]}) + selected_clusters = [1, 3] + expected_result = pd.DataFrame({"clustid": [1, 3]}) + assert ( + get_fit_data_for_selected_peak_clusters(fits, selected_clusters) + .reset_index(drop=True) + .equals(expected_result) + ) + + +def test_invalid_clusters(): + fits = pd.DataFrame({"clustid": [1, 2, 3]}) + with pytest.raises(SystemExit): + get_fit_data_for_selected_peak_clusters(fits, [4, 5, 6]) From a31da53f710919fc7c78e20458ff489023b6ebc7 Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Mon, 18 Mar 2024 22:47:57 -0400 Subject: [PATCH 26/37] added residual plot and cleaned up a little --- Makefile | 7 + peakipy/cli/check_panel.py | 24 ++-- peakipy/cli/edit.py | 27 ++-- peakipy/cli/edit_panel.py | 4 +- peakipy/cli/main.py | 62 +++------ peakipy/core.py | 24 +--- poetry.lock | 264 ++++++++++++++++++------------------- test/test_cli.py | 19 --- 8 files changed, 187 insertions(+), 244 deletions(-) create mode 100644 Makefile diff --git a/Makefile b/Makefile new file mode 100644 index 00000000..8cb28ad3 --- /dev/null +++ b/Makefile @@ -0,0 +1,7 @@ +.PHONY: test + +test: + pytest test/test_core.py + pytest test/test_main.py + pytest test/test_fit.py + pytest test/test_cli.py diff --git a/peakipy/cli/check_panel.py b/peakipy/cli/check_panel.py index b60de1d7..44bc00d4 100644 --- a/peakipy/cli/check_panel.py +++ b/peakipy/cli/check_panel.py @@ -56,9 +56,15 @@ def get_cluster(cluster): return df_pane +@dataclass +class PlotContainer: + main_figure: pn.pane.Plotly + residual_figure: pn.pane.Plotly + + def create_plotly_pane(cluster, plane): data = data_singleton() - fig = check( + fig, residual_fig = check( fits=data.fits_path, data_path=data.data_path, clusters=[cluster], @@ -69,7 +75,8 @@ def create_plotly_pane(cluster, plane): fig["layout"].update(height=800, width=800) fig = fig.to_dict() - return pn.pane.Plotly(fig) + residual_fig = residual_fig.to_dict() + return pn.Column(pn.pane.Plotly(fig), pn.pane.Plotly(residual_fig)) app = typer.Typer() @@ -96,7 +103,7 @@ def create_check_panel( select_plane = pn.widgets.Select( name="Plane", options={f"{plane}": plane for plane in data.df.plane.unique()} ) - interactive_cluster_pane = pn.bind(get_cluster, select_cluster) + result_table_pane = pn.bind(get_cluster, select_cluster) interactive_plotly_pane = pn.bind( create_plotly_pane, cluster=select_cluster, plane=select_plane ) @@ -104,14 +111,15 @@ def create_check_panel( "Select a cluster and plane to look at from the dropdown menus" ) check_pane = pn.Card( - info_pane, - pn.Row(select_cluster, select_plane), + # info_pane, + # pn.Row(select_cluster, select_plane), pn.Row( pn.Column( - pn.Card(interactive_plotly_pane, title="Fitted cluster"), - pn.Card( - interactive_cluster_pane, title="Fitted parameters for cluster" + pn.Row( + pn.Card(interactive_plotly_pane, title="Fitted cluster"), + pn.Column(info_pane, select_cluster, select_plane), ), + pn.Card(result_table_pane, title="Fitted parameters for cluster"), ) ), title="Peakipy check", diff --git a/peakipy/cli/edit.py b/peakipy/cli/edit.py index 0d85bd96..47fbe766 100644 --- a/peakipy/cli/edit.py +++ b/peakipy/cli/edit.py @@ -17,21 +17,14 @@ import panel as pn -from bokeh.io import curdoc from bokeh.events import ButtonClick, DoubleTap -from bokeh.layouts import row, column, grid -from bokeh.models import ColumnDataSource, Tabs, TabPanel, InlineStyleSheet +from bokeh.layouts import row, column +from bokeh.models import ColumnDataSource from bokeh.models.tools import HoverTool from bokeh.models.widgets import ( Slider, Select, Button, - DataTable, - TableColumn, - NumberFormatter, - NumberEditor, - IntEditor, - SelectEditor, TextInput, RadioButtonGroup, CheckboxGroup, @@ -136,9 +129,15 @@ def tabulator_columns(self): ] return columns + @property + def tabulator_non_editable_columns(self): + editors = {"X_RADIUS_PPM": None, "Y_RADIUS_PPM": None} + return editors + def make_tabulator_widget(self): self.tablulator_widget = pn.widgets.Tabulator( self.peakipy_data.df[self.tabulator_columns], + editors=self.tabulator_non_editable_columns, ) return self.tablulator_widget @@ -321,10 +320,7 @@ def setup_plot(self): self.contour_start = TextInput( value="%.2e" % self.thres, title="Contour level:", width=100 ) - # contour_factor = Slider(title="contour factor", value=1.20, start=1., end=2.,step=0.05) self.contour_start.on_change("value", self.update_contour) - # for w in [contour_num,contour_start,contour_factor]: - # w.on_change("value",update_contour) #  plot mask outlines el = self.p.ellipse( @@ -540,13 +536,6 @@ def fit_selected(self, event): selectionIndex = self.source.selected.indices current = self.peakipy_data.df.iloc[selectionIndex] - # self.peakipy_data.df.loc[selectionIndex, "X_RADIUS_PPM"] = ( - # self.slider_X_RADIUS.value - # ) - # self.peakipy_data.df.loc[selectionIndex, "Y_RADIUS_PPM"] = ( - # self.slider_Y_RADIUS.value - # ) - self.peakipy_data.df.loc[selectionIndex, "X_DIAMETER_PPM"] = ( current["X_RADIUS_PPM"] * 2.0 ) diff --git a/peakipy/cli/edit_panel.py b/peakipy/cli/edit_panel.py index de63f8a4..04b853d0 100644 --- a/peakipy/cli/edit_panel.py +++ b/peakipy/cli/edit_panel.py @@ -102,8 +102,8 @@ def fit_peaks_button_click(event): button.on_click(fit_peaks_button_click) def update_source_selected_indices(event): - print(event) - print(bs.tablulator_widget.selection) + # print(event) + # print(bs.tablulator_widget.selection) bs.source.selected.indices = bs.tablulator_widget.selection bs.tablulator_widget.on_click(update_source_selected_indices) diff --git a/peakipy/cli/main.py b/peakipy/cli/main.py index 2ecb064c..849ab610 100644 --- a/peakipy/cli/main.py +++ b/peakipy/cli/main.py @@ -215,10 +215,6 @@ def read( """ - # verbose_mode = args.get("--verb") - # if verbose_mode: - # print("Using arguments:", args) - clust_args = { "struc_el": struc_el, "struc_size": struc_size, @@ -241,7 +237,6 @@ def read( posF1=y_ppm_column_name, posF2=x_ppm_column_name, ) - # peaks.adaptive_clusters(block_size=151,offset=0) case peaklist_format.a3: peaks = Peaklist( @@ -292,9 +287,6 @@ def read( if fuda: peaks.to_fuda() - # if verbose_mode: - # print(data.head()) - match outfmt.value: case "csv": outname = outname.with_suffix(".csv") @@ -1053,12 +1045,10 @@ def create_matplotlib_figure( # axes will appear inverted ax.view_init(30, 120) - # names = ",".join(plane.assignment) title = f"Plane={plot_data.plane_id},Cluster={plot_data.plane_lineshape_parameters.clustid.iloc[0]}" plt.title(title) print(f"[green]Plotting: {title}[/green]") out_str = "Volumes (Heights)\n===========\n" - # chi2s = [] for _, row in plot_data.plane_lineshape_parameters.iterrows(): out_str += f"{row.assignment} = {row.amp:.3e} ({row.height:.3e})\n" if label: @@ -1199,10 +1189,23 @@ def create_plotly_surfaces(plot_data: PlottingDataForPlane): def create_residual_contours(plot_data: PlottingDataForPlane): - data = [] - contours = go.Contour(x=plot_data.x_plot, y=plot_data.y_plot, z=plot_data.residual) - data.append(contours) - return data + contours = go.Contour( + x=plot_data.x_plot[0], y=plot_data.y_plot.T[0], z=plot_data.residual + ) + return contours + + +def create_residual_figure(plot_data: PlottingDataForPlane): + data = create_residual_contours(plot_data) + fig = go.Figure(data=data) + fig.update_layout( + title="Fit residuals", + xaxis_title=f"{plot_data.pseudo3D.f2_label} ppm", + yaxis_title=f"{plot_data.pseudo3D.f1_label} ppm", + xaxis=dict(range=[plot_data.x_plot.max(), plot_data.x_plot.min()]), + yaxis=dict(range=[plot_data.y_plot.max(), plot_data.y_plot.min()]), + ) + return fig def create_plotly_figure(plot_data: PlottingDataForPlane): @@ -1210,9 +1213,6 @@ def create_plotly_figure(plot_data: PlottingDataForPlane): surfaces = create_plotly_surfaces(plot_data) # residuals = create_residual_contours(plot_data) fig = go.Figure(data=lines + surfaces) - # layout = go.Layout(showlegend=True) - # fig.update_layout(layout) - # fig.update_traces(showlegend=True) fig = update_axis_ranges(fig, plot_data) return fig @@ -1222,6 +1222,8 @@ def update_axis_ranges(fig, plot_data: PlottingDataForPlane): scene=dict( xaxis=dict(range=[plot_data.x_plot.max(), plot_data.x_plot.min()]), yaxis=dict(range=[plot_data.y_plot.max(), plot_data.y_plot.min()]), + xaxis_title=f"{plot_data.pseudo3D.f2_label} ppm", + yaxis_title=f"{plot_data.pseudo3D.f1_label} ppm", annotations=make_annotations(plot_data), ) ) @@ -1474,41 +1476,19 @@ def check( # fig = create_plotly_figure(plot_data) if plotly: fig = create_plotly_figure(plot_data) - return fig + residual_fig = create_residual_figure(plot_data) + return fig, residual_fig else: plt = matplotlib.pyplot create_matplotlib_figure( plot_data, pdf, individual, label, ccpn_flag, show ) - # surf = pn.pane.plotly.Plotly(fig) - # app = pn.Column(surf) - # app.show(threaded=True) if first: break run_log() -@app.command(help="Interactive Bokeh dashboard for configuring fitting parameters") -def edit( - peaklist_path: Path, - data_path: Path, - test: bool = False, -): - from bokeh.util.browser import view - from bokeh.server.server import Server - from .edit import BokehScript - - run_log() - bs = BokehScript(peaklist_path=peaklist_path, data_path=data_path) - if not test: - server = Server({"/edit": bs.init}) - server.start() - print("[green]Opening peakipy: Edit fits on http://localhost:5006/edit[/green]") - server.io_loop.add_callback(server.show, "/edit") - server.io_loop.start() - - def make_yaml_file(name, yaml_file=yaml_file): if os.path.exists(name): print(f"Copying {name} to {name}.bak") diff --git a/peakipy/core.py b/peakipy/core.py index 4d4dad95..f5b2084f 100644 --- a/peakipy/core.py +++ b/peakipy/core.py @@ -23,7 +23,7 @@ import json from datetime import datetime from pathlib import Path -from typing import List, Optional +from typing import List from enum import Enum from dataclasses import dataclass, field @@ -1515,28 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"sha256:cc46fce86607580867bdc3361ad462bab9c222ef042d3da86f2fb333e1d916c5"}, + {file = "pydantic-2.6.4.tar.gz", hash = "sha256:b1704e0847db01817624a6b86766967f552dd9dbf3afba4004409f908dcc84e6"}, ] [package.dependencies] @@ -1814,13 +1814,13 @@ windows-terminal = ["colorama (>=0.4.6)"] [[package]] name = "pymdown-extensions" -version = "10.7" +version = "10.7.1" description = "Extension pack for Python Markdown." optional = false python-versions = ">=3.8" files = [ - {file = "pymdown_extensions-10.7-py3-none-any.whl", hash = "sha256:6ca215bc57bc12bf32b414887a68b810637d039124ed9b2e5bd3325cbb2c050c"}, - {file = "pymdown_extensions-10.7.tar.gz", hash = "sha256:c0d64d5cf62566f59e6b2b690a4095c931107c250a8c8e1351c1de5f6b036deb"}, + {file = "pymdown_extensions-10.7.1-py3-none-any.whl", hash = "sha256:f5cc7000d7ff0d1ce9395d216017fa4df3dde800afb1fb72d1c7d3fd35e710f4"}, + {file = "pymdown_extensions-10.7.1.tar.gz", hash = "sha256:c70e146bdd83c744ffc766b4671999796aba18842b268510a329f7f64700d584"}, ] [package.dependencies] @@ -1832,13 +1832,13 @@ extra = ["pygments (>=2.12)"] [[package]] name = "pyparsing" -version = "3.1.1" +version = "3.1.2" description = "pyparsing module - Classes and methods to define and execute parsing grammars" optional = false python-versions = ">=3.6.8" files = [ - {file = "pyparsing-3.1.1-py3-none-any.whl", hash = "sha256:32c7c0b711493c72ff18a981d24f28aaf9c1fb7ed5e9667c9e84e3db623bdbfb"}, - {file = "pyparsing-3.1.1.tar.gz", hash = "sha256:ede28a1a32462f5a9705e07aea48001a08f7cf81a021585011deba701581a0db"}, + {file = "pyparsing-3.1.2-py3-none-any.whl", hash = "sha256:f9db75911801ed778fe61bb643079ff86601aca99fcae6345aa67292038fb742"}, + {file = "pyparsing-3.1.2.tar.gz", hash = "sha256:a1bac0ce561155ecc3ed78ca94d3c9378656ad4c94c1270de543f621420f94ad"}, ] [package.extras] @@ -1868,13 +1868,13 @@ testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "no [[package]] name = "python-dateutil" -version = "2.8.2" +version = "2.9.0.post0" description = "Extensions to the standard Python datetime module" optional = false python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7" files = [ - {file = "python-dateutil-2.8.2.tar.gz", hash = "sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86"}, - {file = "python_dateutil-2.8.2-py2.py3-none-any.whl", hash = "sha256:961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9"}, + {file = "python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3"}, + {file = "python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427"}, ] [package.dependencies] @@ -2153,18 +2153,18 @@ test = ["asv", "gmpy2", "hypothesis", "mpmath", "pooch", "pytest", "pytest-cov", [[package]] name = "setuptools" -version = "69.1.1" +version = "69.2.0" description = "Easily download, build, install, upgrade, and uninstall Python packages" optional = false python-versions = ">=3.8" files = [ - {file = "setuptools-69.1.1-py3-none-any.whl", hash = "sha256:02fa291a0471b3a18b2b2481ed902af520c69e8ae0919c13da936542754b4c56"}, - {file = "setuptools-69.1.1.tar.gz", hash = "sha256:5c0806c7d9af348e6dd3777b4f4dbb42c7ad85b190104837488eab9a7c945cf8"}, + {file = "setuptools-69.2.0-py3-none-any.whl", hash = "sha256:c21c49fb1042386df081cb5d86759792ab89efca84cf114889191cd09aacc80c"}, + {file = "setuptools-69.2.0.tar.gz", hash = "sha256:0ff4183f8f42cd8fa3acea16c45205521a4ef28f73c6391d8a25e92893134f2e"}, ] [package.extras] docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"] -testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.2)", "pip (>=19.1)", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff (>=0.2.1)", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"] +testing = ["build[virtualenv]", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mypy (==1.9)", "packaging (>=23.2)", "pip (>=19.1)", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff (>=0.2.1)", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"] testing-integration = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.2)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"] [[package]] @@ -2330,18 +2330,18 @@ telegram = ["requests"] [[package]] name = "traitlets" -version = "5.14.1" +version = "5.14.2" description = "Traitlets Python configuration system" optional = false python-versions = ">=3.8" files = [ - {file = "traitlets-5.14.1-py3-none-any.whl", hash = "sha256:2e5a030e6eff91737c643231bfcf04a65b0132078dad75e4936700b213652e74"}, - {file = "traitlets-5.14.1.tar.gz", hash = "sha256:8585105b371a04b8316a43d5ce29c098575c2e477850b62b848b964f1444527e"}, + {file = "traitlets-5.14.2-py3-none-any.whl", hash = "sha256:fcdf85684a772ddeba87db2f398ce00b40ff550d1528c03c14dbf6a02003cd80"}, + {file = "traitlets-5.14.2.tar.gz", hash = "sha256:8cdd83c040dab7d1dee822678e5f5d100b514f7b72b01615b26fc5718916fdf9"}, ] [package.extras] docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"] -test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<7.5)", "pytest-mock", "pytest-mypy-testing"] +test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<8.1)", "pytest-mock", "pytest-mypy-testing"] [[package]] name = "typer" diff --git a/test/test_cli.py b/test/test_cli.py index 4b4b2062..95aec4b4 100644 --- a/test/test_cli.py +++ b/test/test_cli.py @@ -154,22 +154,3 @@ def test_check_main_with_pv_pv(protein_L): individual=True, ) peakipy.cli.main.check(**args) - - -# def test_edit_with_default(protein_L): -# args = dict( -# peaklist_path=protein_L / Path("peaks.csv"), -# data_path=protein_L / Path("test1.ft2"), -# test=True, -# ) -# peakipy.cli.main.edit(**args) - - -# if __name__ == "__main__": - -# unittest.TestLoader.sortTestMethodsUsing = None -# unittest.main(verbosity=2) -# to_clean = ["test.csv", "peakipy.config", "run_log.txt", "fits.csv"] -# for i in to_clean: -# print(f"Deleting: {i}") -# shutil.rmtree(i) From e5f02201b1cf0a7495ab81572487747b7cdf3d9f Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Wed, 20 Mar 2024 22:50:39 -0400 Subject: [PATCH 27/37] refactored config code and added tests --- peakipy/cli/edit.py | 4 +-- peakipy/cli/fit.py | 9 ++---- peakipy/cli/main.py | 39 ++++++++++--------------- peakipy/core.py | 42 ++++++++++++++++++++------- test/test_core.py | 71 +++++++++++++++++++++++++++++++++++++++++++++ 5 files changed, 122 insertions(+), 43 deletions(-) diff --git a/peakipy/cli/edit.py b/peakipy/cli/edit.py index 47fbe766..080f5b02 100644 --- a/peakipy/cli/edit.py +++ b/peakipy/cli/edit.py @@ -34,7 +34,7 @@ from bokeh.plotting.contour import contour_data from bokeh.palettes import PuBuGn9, Category20, Viridis256, RdGy11, Reds256, YlOrRd9 -from peakipy.core import LoadData, read_config, StrucEl +from peakipy.core import LoadData, update_args_with_values_from_config_file, StrucEl log_style = "overflow:scroll;" log_div = """
%s
""" @@ -44,7 +44,7 @@ class BokehScript: def __init__(self, peaklist_path: Path, data_path: Path): self._path = peaklist_path self._data_path = data_path - args, config = read_config({}) + args, config = update_args_with_values_from_config_file({}) self._dims = config.get("dims", [0, 1, 2]) self.thres = config.get("thres", 1e6) self._peakipy_data = LoadData( diff --git a/peakipy/cli/fit.py b/peakipy/cli/fit.py index 854d9301..8d370898 100644 --- a/peakipy/cli/fit.py +++ b/peakipy/cli/fit.py @@ -33,15 +33,12 @@ ) console = Console() -# some constants π = np.pi sqrt2 = np.sqrt(2.0) -# temp and log paths + tmp_path = Path("tmp") tmp_path.mkdir(exist_ok=True) log_path = Path("log.txt") -# for printing dataframes -column_selection = ["INDEX", "ASS", "X_PPM", "Y_PPM", "CLUSTID", "MEMCNT"] @dataclass @@ -376,8 +373,6 @@ def refit_peak_cluster_with_constraints( plane_number, ) ) - # fit_report = fit_result.out.fit_report() - # log.write( return fit_results @@ -461,7 +456,6 @@ def prepare_group_of_peaks_for_fitting(clustid, group, fit_peaks_input: FitPeaks XY_slices = np.array([X.copy()[mask], Y.copy()[mask]]) weights = 1.0 / np.array([fit_peaks_input.args.noise] * len(np.ravel(peak_slices))) - # weights = 1.0 / np.ravel(peak_slices) return FitPeakClusterInput( args=fit_peaks_input.args, data=fit_peaks_input.data, @@ -534,6 +528,7 @@ def fit_peak_clusters(peaks: pd.DataFrame, fit_input: FitPeaksInput) -> FitPeaks cluster_df = fit_cluster_of_peaks(data_for_fitting) cluster_dfs.append(cluster_df) df = pd.concat(cluster_dfs, ignore_index=True) + df["lineshape"] = fit_input.args.lineshape.value if fit_input.args.vclist: diff --git a/peakipy/cli/main.py b/peakipy/cli/main.py index 849ab610..46186429 100644 --- a/peakipy/cli/main.py +++ b/peakipy/cli/main.py @@ -54,7 +54,6 @@ Peaklist, run_log, LoadData, - read_config, pv_pv, pvoigt2d, voigt2d, @@ -68,6 +67,9 @@ PeaklistFormat, Lineshape, OutFmt, + write_config, + update_config_file, + update_args_with_values_from_config_file, get_limits_for_axis_in_points, deal_with_peaks_on_edge_of_spectrum, calculate_fwhm_for_voigt_lineshape, @@ -92,7 +94,7 @@ tmp_path.mkdir(exist_ok=True) log_path = Path("log.txt") # for printing dataframes -column_selection = ["INDEX", "ASS", "X_PPM", "Y_PPM", "CLUSTID", "MEMCNT"] +peaklist_columns_for_printing = ["INDEX", "ASS", "X_PPM", "Y_PPM", "CLUSTID", "MEMCNT"] bad_column_selection = [ "clustid", "amp", @@ -111,6 +113,8 @@ "red", "magenta", ] + + peaklist_path_help = "Path to peaklist" data_path_help = "Path to 2D or pseudo3D processed NMRPipe data (e.g. .ft2 or .ft3)" peaklist_format_help = "The format of your peaklist. This can be a2 for CCPN Analysis version 2 style, a3 for CCPN Analysis version 3, sparky, pipe for NMRPipe, or peakipy if you want to use a previously .csv peaklist from peakipy" @@ -296,7 +300,7 @@ def read( data.to_pickle(outname) # write config file - config_path = data_path.parent / Path("peakipy.config") + config_path = peaklist_path.parent / Path("peakipy.config") config_kvs = [ ("dims", dims), ("data_path", str(data_path)), @@ -306,28 +310,16 @@ def read( ("fit_method", "leastsq"), ] try: - if config_path.exists(): - with open(config_path) as opened_config: - config_dic = json.load(opened_config) - # update values in dict - config_dic.update(dict(config_kvs)) - - else: - # make a new config - config_dic = dict(config_kvs) + update_config_file(config_path, config_kvs) except json.decoder.JSONDecodeError: print( - f"Your {config_path} may be corrupted. Making new one (old one moved to {config_path}.bak)" + "\n" + + f"[yellow]Your {config_path} may be corrupted. Making new one (old one moved to {config_path}.bak)[/yellow]" ) shutil.copy(f"{config_path}", f"{config_path}.bak") config_dic = dict(config_kvs) - - with open(config_path, "w") as config: - # write json - # print(config_dic) - config.write(json.dumps(config_dic, sort_keys=True, indent=4)) - # json.dump(config_dic, fp=config, sort_keys=True, indent=4) + write_config(config_path, config_dic) run_log() @@ -506,7 +498,9 @@ def check_for_include_column_and_add_if_missing(peakipy_data): def remove_excluded_peaks(peakipy_data): if len(peakipy_data.df[peakipy_data.df.include != "yes"]) > 0: - excluded = peakipy_data.df[peakipy_data.df.include != "yes"][column_selection] + excluded = peakipy_data.df[peakipy_data.df.include != "yes"][ + peaklist_columns_for_printing + ] table = df_to_rich_table( excluded, title="[yellow] Excluded peaks [/yellow]", @@ -657,7 +651,7 @@ def fit( # read NMR data args = {} config = {} - args, config = read_config(args) + args, config = update_args_with_values_from_config_file(args) dims = config.get("dims", [0, 1, 2]) peakipy_data = LoadData(peaklist_path, data_path, dims=dims) peakipy_data = check_for_include_column_and_add_if_missing(peakipy_data) @@ -1211,7 +1205,6 @@ def create_residual_figure(plot_data: PlottingDataForPlane): def create_plotly_figure(plot_data: PlottingDataForPlane): lines = create_plotly_wireframe_lines(plot_data) surfaces = create_plotly_surfaces(plot_data) - # residuals = create_residual_contours(plot_data) fig = go.Figure(data=lines + surfaces) fig = update_axis_ranges(fig, plot_data) return fig @@ -1373,7 +1366,7 @@ def check( fits = validate_fit_dataframe(pd.read_csv(fits)) args = {} # get dims from config file - args, config = read_config(args, config_path) + args, config = update_args_with_values_from_config_file(args, config_path) dims = config.get("dims", (1, 2, 3)) ccpn_flag = ccpn diff --git a/peakipy/core.py b/peakipy/core.py index f5b2084f..5dc87fa2 100644 --- a/peakipy/core.py +++ b/peakipy/core.py @@ -1728,7 +1728,28 @@ def update_df(self): self.check_peak_bounds() -def read_config(args, config_path="peakipy.config"): +def load_config(config_path): + if config_path.exists(): + with open(config_path) as opened_config: + config_dic = json.load(opened_config) + return config_dic + else: + return {} + + +def write_config(config_path, config_dic): + with open(config_path, "w") as config: + config.write(json.dumps(config_dic, sort_keys=True, indent=4)) + + +def update_config_file(config_path, config_kvs): + config_dic = load_config(config_path) + config_dic.update(config_kvs) + write_config(config_path, config_dic) + return config_dic + + +def update_args_with_values_from_config_file(args, config_path="peakipy.config"): """read a peakipy config file, extract params and update args dict :param args: dict containing params extracted from docopt command line @@ -1746,17 +1767,16 @@ def read_config(args, config_path="peakipy.config"): config_path = Path(config_path) if config_path.exists(): try: - with open(config_path) as config_file: - config = json.load(config_file) - print( - f"[green]Using config file with dims [yellow]{config.get('dims')}[/yellow][/green]" - ) - args["dims"] = config.get("dims", [0, 1, 2]) - noise = config.get("noise") - if noise: - noise = float(noise) + config = load_config(config_path) + print( + f"[green]Using config file with dims [yellow]{config.get('dims')}[/yellow][/green]" + ) + args["dims"] = config.get("dims", (0, 1, 2)) + noise = config.get("noise") + if noise: + noise = float(noise) - colors = config.get("colors", ["#5e3c99", "#e66101"]) + colors = config.get("colors", ["#5e3c99", "#e66101"]) except json.decoder.JSONDecodeError: print( "[red]Your peakipy.config file is corrupted - maybe your JSON is not correct...[/red]" diff --git a/test/test_core.py b/test/test_core.py index 2a0612c9..083f9e25 100644 --- a/test/test_core.py +++ b/test/test_core.py @@ -1,6 +1,8 @@ import unittest from unittest.mock import patch from collections import namedtuple +from pathlib import Path +import json import numpy as np from numpy.testing import assert_array_equal @@ -26,6 +28,9 @@ select_planes_above_threshold_from_masked_data, slice_peaks_from_data_using_mask, estimate_amplitude, + load_config, + write_config, + update_config_file, ) @@ -402,5 +407,71 @@ def test_main(self, MockSpec): self.assertIsNotNone(spec) +def test_load_config_existing(): + config_path = Path("test_config.json") + # Create a dummy existing config file + with open(config_path, "w") as f: + json.dump({"key1": "value1"}, f) + + loaded_config = load_config(config_path) + + assert loaded_config == {"key1": "value1"} + + # Clean up + config_path.unlink() + + +def test_load_config_nonexistent(): + config_path = Path("test_config.json") + + loaded_config = load_config(config_path) + + assert loaded_config == {} + + +def test_write_config(): + config_path = Path("test_config.json") + config_kvs = {"key1": "value1", "key2": "value2"} + + write_config(config_path, config_kvs) + + # Check if the config file is created correctly + assert config_path.exists() + + # Check if the config file content is correct + with open(config_path) as f: + created_config = json.load(f) + assert created_config == {"key1": "value1", "key2": "value2"} + + # Clean up + config_path.unlink() + + +def test_update_config_file_existing(): + config_path = Path("test_config.json") + # Create a dummy existing config file + with open(config_path, "w") as f: + json.dump({"key1": "value1"}, f) + + config_kvs = {"key2": "value2", "key3": "value3"} + updated_config = update_config_file(config_path, config_kvs) + + assert updated_config == {"key1": "value1", "key2": "value2", "key3": "value3"} + + # Clean up + config_path.unlink() + + +def test_update_config_file_nonexistent(): + config_path = Path("test_config.json") + config_kvs = {"key1": "value1", "key2": "value2"} + updated_config = update_config_file(config_path, config_kvs) + + assert updated_config == {"key1": "value1", "key2": "value2"} + + # Clean up + config_path.unlink() + + if __name__ == "__main__": unittest.main(verbosity=2) From 2a1afe2a8eade62212a9efa86680a9638d65c3e2 Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Sun, 31 Mar 2024 23:23:24 -0400 Subject: [PATCH 28/37] fixed vclist bug and added test --- peakipy/cli/fit.py | 3 ++- test/test_cli.py | 10 ++++++++++ test/test_fit.py | 29 +++++++++++++++++++++++++++++ 3 files changed, 41 insertions(+), 1 deletion(-) diff --git a/peakipy/cli/fit.py b/peakipy/cli/fit.py index 8d370898..7beb8f0c 100644 --- a/peakipy/cli/fit.py +++ b/peakipy/cli/fit.py @@ -59,6 +59,7 @@ class FitPeaksArgs: jack_knife_sample_errors: bool = False mp: bool = (True,) verbose: bool = (False,) + vclist_data: Optional[np.array] = None @dataclass @@ -409,7 +410,7 @@ def rename_columns_for_compatibility(df): def add_vclist_to_df(fit_input: FitPeaksInput, df: pd.DataFrame): - vclist_data = fit_input.args.get("vclist_data") + vclist_data = fit_input.args.vclist_data df["vclist"] = df.plane.apply(lambda x: vclist_data[x]) return df diff --git a/test/test_cli.py b/test/test_cli.py index 95aec4b4..aec9c35c 100644 --- a/test/test_cli.py +++ b/test/test_cli.py @@ -51,6 +51,16 @@ def test_fit_main_with_default(protein_L): peakipy.cli.main.fit(**args) +def test_fit_main_with_vclist(protein_L): + args = dict( + peaklist_path=protein_L / Path("test.csv"), + data_path=protein_L / Path("test1.ft2"), + output_path=protein_L / Path("fits_PV.csv"), + vclist=protein_L / Path("vclist"), + ) + peakipy.cli.main.fit(**args) + + def test_fit_main_with_gaussian(protein_L): args = dict( peaklist_path=protein_L / Path("test.csv"), diff --git a/test/test_fit.py b/test/test_fit.py index 113b0339..b14816b0 100644 --- a/test/test_fit.py +++ b/test/test_fit.py @@ -19,6 +19,9 @@ create_parameter_dict, perform_initial_lineshape_fit_on_cluster_of_peaks, merge_unpacked_parameters_with_metadata, + add_vclist_to_df, + FitPeaksArgs, + FitPeaksInput, ) from peakipy.core import Lineshape, pvoigt2d @@ -344,6 +347,32 @@ def test_merge_unpacked_parameters_with_metadata(): assert expected_result.equals(actual_result) +def test_add_vclist_to_df(): + args = FitPeaksArgs( + noise=0, uc_dics={}, lineshape=Lineshape.PV, vclist_data=np.array([1, 2, 3]) + ) + fit_peaks_input = FitPeaksInput( + args=args, data=None, config=None, plane_numbers=None + ) + df = pd.DataFrame(dict(plane=[0, 1, 2])) + expected_df = pd.DataFrame(dict(plane=[0, 1, 2], vclist=[1, 2, 3])) + actual_df = add_vclist_to_df(fit_peaks_input, df) + assert actual_df.equals(expected_df) + + +def test_add_vclist_to_df_plane_order(): + args = FitPeaksArgs( + noise=0, uc_dics={}, lineshape=Lineshape.PV, vclist_data=np.array([1, 2, 3]) + ) + fit_peaks_input = FitPeaksInput( + args=args, data=None, config=None, plane_numbers=None + ) + df = pd.DataFrame(dict(plane=[2, 1, 0])) + expected_df = pd.DataFrame(dict(plane=[2, 1, 0], vclist=[3, 2, 1])) + actual_df = add_vclist_to_df(fit_peaks_input, df) + assert actual_df.equals(expected_df) + + # def test_perform_initial_lineshape_fit_on_cluster_of_peaks(pseudo_voigt_model_result): # expected_result = pseudo_voigt_model_result # actual_result = perform_initial_lineshape_fit_on_cluster_of_peaks() From bb64a9941851be445625b2c6c825e35861ff797b Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Sun, 5 May 2024 21:43:51 -0400 Subject: [PATCH 29/37] added more tests --- .github/workflows/ci.yml | 4 +- Makefile | 15 +++--- peakipy/core.py | 17 ++++++- test/test_core.py | 103 +++++++++++++++++++++++++++++++++++++-- test/test_fit.py | 81 ++++++++++++++++++++++++++++++ 5 files changed, 205 insertions(+), 15 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index dde8ed90..e283337a 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -17,7 +17,7 @@ jobs: python -m pip install --upgrade pip pip install . pip install pytest + pip install coverage - name: Run tests run: | - pytest test/test_core.py - pytest test/test_cli.py \ No newline at end of file + make coverage diff --git a/Makefile b/Makefile index 8cb28ad3..3a87d871 100644 --- a/Makefile +++ b/Makefile @@ -1,7 +1,10 @@ -.PHONY: test +.PHONY: coverage -test: - pytest test/test_core.py - pytest test/test_main.py - pytest test/test_fit.py - pytest test/test_cli.py +coverage: + coverage run -m pytest test/test_core.py test/test_main.py test/test_fit.py test/test_cli.py + +coverage-html: + coverage html + firefox htmlcov/index.html + +test: coverage coverage-html diff --git a/peakipy/core.py b/peakipy/core.py index 5dc87fa2..d0473a61 100644 --- a/peakipy/core.py +++ b/peakipy/core.py @@ -475,6 +475,17 @@ def get_params(params, name): @dataclass class PeakLimits: + """Given a peak position and linewidth in points determine + the limits based on the data + + Arguments + --------- + peak: pd.DataFrame + peak is a row from a pandas dataframe + data: np.array + 2D numpy array + """ + peak: pd.DataFrame data: np.array min_x: int = field(init=False) @@ -483,10 +494,12 @@ class PeakLimits: max_y: int = field(init=False) def __post_init__(self): - self.max_y = int(self.peak.Y_AXIS + self.peak.YW) + 1 + assert self.peak.Y_AXIS <= self.data.shape[0] + assert self.peak.X_AXIS <= self.data.shape[1] + self.max_y = int(np.ceil(self.peak.Y_AXIS + self.peak.YW)) + 1 if self.max_y > self.data.shape[0]: self.max_y = self.data.shape[0] - self.max_x = int(self.peak.X_AXIS + self.peak.XW) + 1 + self.max_x = int(np.ceil(self.peak.X_AXIS + self.peak.XW)) + 1 if self.max_x > self.data.shape[1]: self.max_x = self.data.shape[1] diff --git a/test/test_core.py b/test/test_core.py index 083f9e25..ed9dec4c 100644 --- a/test/test_core.py +++ b/test/test_core.py @@ -4,6 +4,7 @@ from pathlib import Path import json +import pytest import numpy as np from numpy.testing import assert_array_equal import pandas as pd @@ -14,16 +15,22 @@ from peakipy.core import ( make_mask, fix_params, + gaussian_lorentzian, + pv_g, + pv_l, + voigt2d, pvoigt2d, pv_pv, get_params, make_param_dict, to_prefix, make_models, + get_lineshape_function, Pseudo3D, Peaklist, Lineshape, PeaklistFormat, + PeakLimits, select_reference_planes_using_indices, select_planes_above_threshold_from_masked_data, slice_peaks_from_data_using_mask, @@ -34,6 +41,11 @@ ) +@pytest.fixture +def test_directory(): + return Path(__file__).parent + + def test_select_reference_planes_using_indices(): data = np.zeros((6, 100, 200)) indices = [] @@ -157,6 +169,9 @@ def test_estimate_amplitude_invalid_indices(): class TestCoreFunctions(unittest.TestCase): + test_directory = Path(__file__).parent + test_directory = "./" + def test_make_mask(self): data = np.ones((10, 10)) c_x = 5 @@ -268,6 +283,12 @@ def test_make_param_dict(self): self.assertEqual(params["_two_sigma_x"], 1.25) self.assertEqual(params["_two_sigma_y"], 1.25) + voigt_params = make_param_dict(peaks, data, Lineshape.V) + self.assertEqual( + voigt_params["_one_sigma_x"], 2.5 / (2.0 * np.sqrt(2.0 * np.log(2))) + ) + self.assertEqual(voigt_params["_one_gamma_x"], 2.5 / 2.0) + def test_to_prefix(self): names = [ (1, "_1_"), @@ -328,9 +349,9 @@ def test_make_models(self): def test_Pseudo3D(self): datasets = [ - ("test/test_protein_L/test1.ft2", [0, 1, 2]), - ("test/test_protein_L/test_tp.ft2", [2, 1, 0]), - ("test/test_protein_L/test_tp2.ft2", [1, 2, 0]), + (f"{self.test_directory}/test_protein_L/test1.ft2", [0, 1, 2]), + (f"{self.test_directory}/test_protein_L/test_tp.ft2", [2, 1, 0]), + (f"{self.test_directory}/test_protein_L/test_tp2.ft2", [1, 2, 0]), ] # expected shape @@ -377,6 +398,8 @@ def test_main(self, MockFit): class TestReadScript(unittest.TestCase): + test_directory = "./" + @patch("peakipy.cli.main.read") def test_main(self, MockRead): args = {"": "hello", "": "data"} @@ -385,8 +408,8 @@ def test_main(self, MockRead): def test_read_pipe_peaklist(self): args = { - "path": "test/test_pipe.tab", - "data_path": "test/test_pipe.ft2", + "path": f"{self.test_directory}/test_pipe.tab", + "data_path": f"{self.test_directory}/test_pipe.ft2", "dims": [0, 1, 2], "fmt": PeaklistFormat.pipe, } @@ -473,5 +496,75 @@ def test_update_config_file_nonexistent(): config_path.unlink() +@pytest.fixture +def sample_data(): + return np.zeros((10, 10)) + + +@pytest.fixture +def sample_peak(): + peak_data = {"X_AXIS": [5], "Y_AXIS": [5], "XW": [2], "YW": [2]} + return pd.DataFrame(peak_data).iloc[0] + + +def test_peak_limits_max_min(sample_peak, sample_data): + limits = PeakLimits(sample_peak, sample_data) + + assert limits.max_x == 8 + assert limits.max_y == 8 + assert limits.min_x == 3 + assert limits.min_y == 3 + + +def test_peak_limits_boundary(sample_data): + peak_data = {"X_AXIS": [8], "Y_AXIS": [8], "XW": [2], "YW": [2]} + peak = pd.DataFrame(peak_data).iloc[0] + limits = PeakLimits(peak, sample_data) + + assert limits.max_x == 10 + assert limits.max_y == 10 + assert limits.min_x == 6 + assert limits.min_y == 6 + + +def test_peak_limits_at_boundary(sample_data): + peak_data = {"X_AXIS": [0], "Y_AXIS": [0], "XW": [2], "YW": [2]} + peak = pd.DataFrame(peak_data).iloc[0] + limits = PeakLimits(peak, sample_data) + + assert limits.max_x == 3 + assert limits.max_y == 3 + assert limits.min_x == 0 + assert limits.min_y == 0 + + +def test_peak_limits_outside_boundary(sample_data): + peak_data = {"X_AXIS": [15], "Y_AXIS": [15], "XW": [2], "YW": [2]} + peak = pd.DataFrame(peak_data).iloc[0] + with pytest.raises(AssertionError): + limits = PeakLimits(peak, sample_data) + + +def test_peak_limits_1d_data(): + data = np.zeros(10) + peak_data = {"X_AXIS": [5], "Y_AXIS": [0], "XW": [2], "YW": [0]} + peak = pd.DataFrame(peak_data).iloc[0] + with pytest.raises(IndexError): + limits = PeakLimits(peak, data) + + +def test_get_lineshape_function(): + assert get_lineshape_function(Lineshape.PV) == pvoigt2d + assert get_lineshape_function(Lineshape.L) == pvoigt2d + assert get_lineshape_function(Lineshape.G) == pvoigt2d + assert get_lineshape_function(Lineshape.G_L) == gaussian_lorentzian + assert get_lineshape_function(Lineshape.PV_G) == pv_g + assert get_lineshape_function(Lineshape.PV_L) == pv_l + assert get_lineshape_function(Lineshape.PV_PV) == pv_pv + assert get_lineshape_function(Lineshape.V) == voigt2d + with pytest.raises(Exception): + get_lineshape_function("bla") + + if __name__ == "__main__": unittest.main(verbosity=2) diff --git a/test/test_fit.py b/test/test_fit.py index b14816b0..83512aed 100644 --- a/test/test_fit.py +++ b/test/test_fit.py @@ -20,6 +20,8 @@ perform_initial_lineshape_fit_on_cluster_of_peaks, merge_unpacked_parameters_with_metadata, add_vclist_to_df, + update_cluster_df_with_fit_statistics, + rename_columns_for_compatibility, FitPeaksArgs, FitPeaksInput, ) @@ -107,6 +109,85 @@ def test_set_parameters_to_fix_during_fit_3(): ) +def test_set_parameters_to_fix_during_fit_None(): + parameter_set = Parameters() + parameter_set.add("test1", vary=True) + parameter_set.add("test2", vary=True) + modified_parameter_set, float_str = set_parameters_to_fix_during_fit( + parameter_set, None + ) + assert ( + modified_parameter_set["test1"].vary + == modified_parameter_set["test2"].vary + == True + ) + + +def test_set_parameters_to_fix_during_fit_None_str(): + parameter_set = Parameters() + parameter_set.add("test1", vary=True) + parameter_set.add("test2", vary=True) + modified_parameter_set, float_str = set_parameters_to_fix_during_fit( + parameter_set, ["None"] + ) + assert ( + modified_parameter_set["test1"].vary + == modified_parameter_set["test2"].vary + == True + ) + + +def test_update_cluster_df_with_fit_statistics(): + result = ModelResult(Model(pvoigt2d), None, None) + result.aic = None + result.bic = None + data = [ + dict( + chisqr=None, + redchi=None, + residual_sum=None, + aic=None, + bic=None, + nfev=0, + ndata=0, + ) + ] + expected_cluster_df = pd.DataFrame(data) + actual_cluster_df = update_cluster_df_with_fit_statistics( + expected_cluster_df, result + ) + pd.testing.assert_frame_equal(actual_cluster_df, expected_cluster_df) + + +def test_rename_columns_for_compatibility(): + df = pd.DataFrame( + [ + dict( + amplitude=1, + amplitude_stderr=1, + X_AXIS=1, + Y_AXIS=1, + ASS="None", + MEMCNT=1, + X_RADIUS=1, + Y_RADIUS=1, + ) + ] + ) + expected_columns = [ + "amp", + "amp_err", + "init_center_x", + "init_center_y", + "assignment", + "memcnt", + "x_radius", + "y_radius", + ] + actual_columns = rename_columns_for_compatibility(df).columns + assert all([i == j for i, j in zip(actual_columns, expected_columns)]) + + def test_get_default_param_names_pseudo_voigt(): assert get_default_lineshape_param_names(Lineshape.PV) == [ "amplitude", From 09655d35c59cf19f554b9e27fafd507dc7e6c3f6 Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Sun, 5 May 2024 21:53:59 -0400 Subject: [PATCH 30/37] fixed dataclass factory --- test/test_main.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/test/test_main.py b/test/test_main.py index 366f934b..6e3bb4ce 100644 --- a/test/test_main.py +++ b/test/test_main.py @@ -29,8 +29,8 @@ def actual_vclist(): @dataclass class PeakipyData: - df: pd.DataFrame = pd.DataFrame() - data: np.array = np.zeros((4, 10, 20)) + df: pd.DataFrame = field(default_factory=lambda: pd.DataFrame()) + data: np.array = field(default_factory=lambda: np.zeros((4, 10, 20))) dims: list = field(default_factory=lambda: [0, 1, 2]) From 3ca2acd3128c8b26050298045481c3e2acb394a3 Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Sun, 12 May 2024 16:01:03 -0400 Subject: [PATCH 31/37] added tests and coverage --- .github/workflows/ci.yml | 4 ++ peakipy/cli/main.py | 26 ++---------- test/test_main.py | 87 ++++++++++++++++++++++++++++++++++++++++ 3 files changed, 95 insertions(+), 22 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index e283337a..a76fd9e7 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -21,3 +21,7 @@ jobs: - name: Run tests run: | make coverage + - name: Upload coverage reports to Codecov + uses: codecov/codecov-action@v4.0.1 + with: + token: ${{ secrets.CODECOV_TOKEN }} diff --git a/peakipy/cli/main.py b/peakipy/cli/main.py index 46186429..3a636632 100644 --- a/peakipy/cli/main.py +++ b/peakipy/cli/main.py @@ -441,28 +441,6 @@ def select_specified_planes(plane, peakipy_data): return plane_numbers, peakipy_data -def select_specified_planes(plane, peakipy_data): - plane_numbers = np.arange(peakipy_data.data.shape[peakipy_data.dims[0]]) - # only fit specified planes - if plane: - inds = [i for i in plane] - data_inds = [ - (i in inds) for i in range(peakipy_data.data.shape[peakipy_data.dims[0]]) - ] - plane_numbers = np.arange(peakipy_data.data.shape[peakipy_data.dims[0]])[ - data_inds - ] - peakipy_data.data = peakipy_data.data[data_inds] - print( - "[yellow]Using only planes {plane} data now has the following shape[/yellow]", - peakipy_data.data.shape, - ) - if peakipy_data.data.shape[peakipy_data.dims[0]] == 0: - print("[red]You have excluded all the data![/red]", peakipy_data.data.shape) - exit() - return plane_numbers, peakipy_data - - def exclude_specified_planes(exclude_plane, peakipy_data): plane_numbers = np.arange(peakipy_data.data.shape[peakipy_data.dims[0]]) # do not fit these planes @@ -566,6 +544,10 @@ def unpack_xy_bounds(xy_bounds, peakipy_data): xy_bounds = list(xy_bounds) xy_bounds[0] = xy_bounds[0] * peakipy_data.pt_per_ppm_f2 xy_bounds[1] = xy_bounds[1] * peakipy_data.pt_per_ppm_f1 + case _: + raise TypeError( + "xy_bounds should be a tuple (, )" + ) return xy_bounds diff --git a/test/test_main.py b/test/test_main.py index 6e3bb4ce..cf0f144e 100644 --- a/test/test_main.py +++ b/test/test_main.py @@ -9,8 +9,12 @@ from peakipy.cli.main import ( get_vclist, check_for_include_column_and_add_if_missing, + check_data_shape_is_consistent_with_dims, select_specified_planes, exclude_specified_planes, + remove_excluded_peaks, + warn_if_trying_to_fit_large_clusters, + unpack_xy_bounds, validate_plane_selection, validate_sample_count, unpack_plotting_colors, @@ -32,6 +36,8 @@ class PeakipyData: df: pd.DataFrame = field(default_factory=lambda: pd.DataFrame()) data: np.array = field(default_factory=lambda: np.zeros((4, 10, 20))) dims: list = field(default_factory=lambda: [0, 1, 2]) + pt_per_ppm_f2 = 10 + pt_per_ppm_f1 = 20 def test_get_vclist(actual_vclist): @@ -47,6 +53,13 @@ def test_get_vclist_none(): assert args == expected_args +def test_get_vclist_error(): + vclist = "vclist" + args = {} + with pytest.raises(Exception): + get_vclist(vclist, args) + + def test_check_for_include_column(): peakipy_data = PeakipyData(pd.DataFrame()) peakipy_data = check_for_include_column_and_add_if_missing(peakipy_data) @@ -68,6 +81,14 @@ def test_select_specified_planes_2(): assert peakipy_data.data.shape == (2, 10, 20) +def test_select_specified_planes_all_planes_excluded(capsys): + plane = [10] + with pytest.raises(SystemExit): + select_specified_planes(plane, PeakipyData()) + captured = capsys.readouterr() + assert "" in captured.err + + def test_exclude_specified_planes(): plane = None expected_plane_numbers = np.arange(4) @@ -83,6 +104,66 @@ def test_exclude_specified_planes_2(): assert peakipy_data.data.shape == (2, 10, 20) +def test_exclude_specified_planes_all_planes_excluded(capsys): + plane = [0, 1, 2, 3] + with pytest.raises(SystemExit): + exclude_specified_planes(plane, PeakipyData()) + captured = capsys.readouterr() + assert "" in captured.err + + +def test_remove_excluded_peaks(): + actual_dict = dict( + include=["yes", "yes", "no"], + peak=[1, 2, 3], + INDEX=[0, 1, 2], + ASS=["one", "two", "three"], + X_PPM=[1, 2, 3], + Y_PPM=[1, 2, 3], + CLUSTID=[1, 2, 3], + MEMCNT=[1, 1, 1], + ) + expected_dict = {k: v[:-1] for k, v in actual_dict.items()} + actual_df = pd.DataFrame(actual_dict) + expected_df = pd.DataFrame(expected_dict) + peakipy_data = PeakipyData(df=actual_df) + pd.testing.assert_frame_equal(remove_excluded_peaks(peakipy_data).df, expected_df) + + +def test_warn_if_trying_to_fit_large_clusters(): + max_cluster_size = 7 + df = pd.DataFrame(dict(MEMCNT=[1, 6], CLUSTID=[0, 1])) + peakipy_data = PeakipyData(df=df) + assert ( + warn_if_trying_to_fit_large_clusters(max_cluster_size, peakipy_data) + == max_cluster_size + ) + + +def test_warn_if_trying_to_fit_large_clusters_none(): + max_cluster_size = None + df = pd.DataFrame(dict(MEMCNT=[1, 12], CLUSTID=[0, 1])) + peakipy_data = PeakipyData(df=df) + assert warn_if_trying_to_fit_large_clusters(max_cluster_size, peakipy_data) == 12 + + +def test_unpack_xy_bounds_case_00(): + xy_bounds = (0, 0) + result = unpack_xy_bounds(xy_bounds, PeakipyData()) + assert result == None + + +def test_unpack_xy_bounds_case_xy(): + xy_bounds = (1, 2) + result = unpack_xy_bounds(xy_bounds, PeakipyData()) + assert result == [10, 40] + + +def test_unpack_xy_bounds_invalid_input(): + with pytest.raises(TypeError): + unpack_xy_bounds(None, PeakipyData()) + + class MockPseudo3D: def __init__(self, n_planes): self.n_planes = n_planes @@ -195,3 +276,9 @@ def test_invalid_clusters(): fits = pd.DataFrame({"clustid": [1, 2, 3]}) with pytest.raises(SystemExit): get_fit_data_for_selected_peak_clusters(fits, [4, 5, 6]) + + +def test_check_data_shape_is_consistent_with_dims(): + peakipy_data = PeakipyData(data=np.zeros((4, 10))) + with pytest.raises(SystemExit): + check_data_shape_is_consistent_with_dims(peakipy_data) From 5c33de287b0bf8d612f3c490080499d855fefbf4 Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Sat, 25 May 2024 23:12:19 -0400 Subject: [PATCH 32/37] major refactoring and increased test coverage --- .gitignore | 3 +- Makefile | 3 +- peakipy/cli/check_panel.py | 2 +- peakipy/cli/edit.py | 3 +- peakipy/cli/edit_panel.py | 5 +- peakipy/cli/fit.py | 8 +- peakipy/cli/main.py | 884 +++-------------------------------- peakipy/constants.py | 6 + peakipy/fitting.py | 645 ++++++++++++++++++++++++++ peakipy/io.py | 913 +++++++++++++++++++++++++++++++++++++ peakipy/lineshapes.py | 522 +++++++++++++++++++++ peakipy/plotting.py | 400 ++++++++++++++++ peakipy/utils.py | 239 ++++++++++ test/test_cli.py | 4 +- test/test_data.py | 13 +- test/test_edit.py | 1 + test/test_fit.py | 2 +- test/test_fitting.py | 692 ++++++++++++++++++++++++++++ test/test_io.py | 428 +++++++++++++++++ test/test_lineshapes.py | 353 ++++++++++++++ test/test_utils.py | 252 ++++++++++ 21 files changed, 4543 insertions(+), 835 deletions(-) create mode 100644 peakipy/constants.py create mode 100644 peakipy/fitting.py create mode 100644 peakipy/io.py create mode 100644 peakipy/lineshapes.py create mode 100644 peakipy/plotting.py create mode 100644 peakipy/utils.py create mode 100644 test/test_edit.py create mode 100644 test/test_fitting.py create mode 100644 test/test_io.py create mode 100644 test/test_lineshapes.py create mode 100644 test/test_utils.py diff --git a/.gitignore b/.gitignore index 44059f70..6f72b217 100644 --- a/.gitignore +++ b/.gitignore @@ -29,4 +29,5 @@ site/ # Tests test/test_protein_L/tmp/ ## pytest -.pytest_cache \ No newline at end of file +.pytest_cache +.coverage diff --git a/Makefile b/Makefile index 3a87d871..431618da 100644 --- a/Makefile +++ b/Makefile @@ -1,7 +1,8 @@ .PHONY: coverage coverage: - coverage run -m pytest test/test_core.py test/test_main.py test/test_fit.py test/test_cli.py + #coverage run -m pytest test/test_core.py test/test_main.py test/test_fit.py test/test_cli.py + coverage run -m pytest test/test_fitting.py test/test_lineshapes.py test/test_io.py test/test_utils.py test/test_main.py test/test_cli.py coverage-html: coverage html diff --git a/peakipy/cli/check_panel.py b/peakipy/cli/check_panel.py index 44bc00d4..2b11d131 100644 --- a/peakipy/cli/check_panel.py +++ b/peakipy/cli/check_panel.py @@ -69,7 +69,7 @@ def create_plotly_pane(cluster, plane): data_path=data.data_path, clusters=[cluster], plane=[plane], - config_path=data.config_path, + # config_path=data.config_path, plotly=True, ) diff --git a/peakipy/cli/edit.py b/peakipy/cli/edit.py index 080f5b02..3f4d31ef 100644 --- a/peakipy/cli/edit.py +++ b/peakipy/cli/edit.py @@ -34,7 +34,8 @@ from bokeh.plotting.contour import contour_data from bokeh.palettes import PuBuGn9, Category20, Viridis256, RdGy11, Reds256, YlOrRd9 -from peakipy.core import LoadData, update_args_with_values_from_config_file, StrucEl +from peakipy.io import LoadData, StrucEl +from peakipy.utils import update_args_with_values_from_config_file log_style = "overflow:scroll;" log_div = """
%s
""" diff --git a/peakipy/cli/edit_panel.py b/peakipy/cli/edit_panel.py index 04b853d0..f74f5b3e 100644 --- a/peakipy/cli/edit_panel.py +++ b/peakipy/cli/edit_panel.py @@ -102,9 +102,12 @@ def fit_peaks_button_click(event): button.on_click(fit_peaks_button_click) def update_source_selected_indices(event): - # print(event) # print(bs.tablulator_widget.selection) + # hack to make current selection however, only allows one selection + # at a time + bs.tablulator_widget._update_selection([event.value]) bs.source.selected.indices = bs.tablulator_widget.selection + # print(bs.tablulator_widget.selection) bs.tablulator_widget.on_click(update_source_selected_indices) bs.tablulator_widget.on_edit(update_peakipy_data_on_edit_of_table) diff --git a/peakipy/cli/fit.py b/peakipy/cli/fit.py index 7beb8f0c..8ffc73e1 100644 --- a/peakipy/cli/fit.py +++ b/peakipy/cli/fit.py @@ -14,15 +14,17 @@ from lmfit import Model, Parameter, Parameters from lmfit.model import ModelResult -from peakipy.core import ( - fix_params, +from peakipy.lineshapes import ( Lineshape, pvoigt2d, voigt2d, pv_pv, + get_lineshape_function, +) +from peakipy.fitting import ( + fix_params, to_prefix, get_limits_for_axis_in_points, - get_lineshape_function, deal_with_peaks_on_edge_of_spectrum, select_planes_above_threshold_from_masked_data, select_reference_planes_using_indices, diff --git a/peakipy/cli/main.py b/peakipy/cli/main.py index 3a636632..79613f42 100644 --- a/peakipy/cli/main.py +++ b/peakipy/cli/main.py @@ -1,23 +1,4 @@ #!/usr/bin/env python3 -""" - - peakipy - deconvolute overlapping NMR peaks - Copyright (C) 2019 Jacob Peter Brady - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see . - -""" import os import json import shutil @@ -42,77 +23,75 @@ from mpl_toolkits.mplot3d import axes3d from matplotlib import cm from matplotlib.backends.backend_pdf import PdfPages -from matplotlib.widgets import Button import yaml -import plotly.graph_objects as go import plotly.io as pio pio.templates.default = "plotly_dark" -from peakipy.core import ( +from peakipy.io import ( Peaklist, - run_log, LoadData, - pv_pv, - pvoigt2d, - voigt2d, - make_mask, - pv_g, - pv_l, - gaussian_lorentzian, Pseudo3D, - df_to_rich_table, StrucEl, PeaklistFormat, - Lineshape, OutFmt, + get_vclist, +) +from peakipy.utils import ( + run_log, + df_to_rich_table, write_config, update_config_file, update_args_with_values_from_config_file, - get_limits_for_axis_in_points, - deal_with_peaks_on_edge_of_spectrum, - calculate_fwhm_for_voigt_lineshape, - calculate_height_for_voigt_lineshape, - calculate_fwhm_for_pseudo_voigt_lineshape, - calculate_height_for_pseudo_voigt_lineshape, - calculate_height_for_gaussian_lineshape, - calculate_height_for_lorentzian_lineshape, - calculate_height_for_pv_pv_lineshape, + update_linewidths_from_hz_to_points, + update_peak_positions_from_ppm_to_points, + check_data_shape_is_consistent_with_dims, + check_for_include_column_and_add_if_missing, + remove_excluded_peaks, + warn_if_trying_to_fit_large_clusters, + save_data, +) + +from peakipy.lineshapes import ( + Lineshape, + calculate_lineshape_specific_height_and_fwhm, calculate_peak_centers_in_ppm, calculate_peak_linewidths_in_hz, ) +from peakipy.fitting import ( + get_limits_for_axis_in_points, + deal_with_peaks_on_edge_of_spectrum, + select_specified_planes, + exclude_specified_planes, + unpack_xy_bounds, + validate_plane_selection, + get_fit_data_for_selected_peak_clusters, + make_masks_from_plane_data, + simulate_lineshapes_from_fitted_peak_parameters, + simulate_pv_pv_lineshapes_from_fitted_peak_parameters, + validate_fit_dataframe, +) + from .fit import ( fit_peak_clusters, FitPeaksInput, FitPeaksArgs, ) +from peakipy.plotting import ( + PlottingDataForPlane, + validate_sample_count, + unpack_plotting_colors, + create_plotly_figure, + create_residual_figure, + create_matplotlib_figure, +) from .spec import yaml_file app = typer.Typer() tmp_path = Path("tmp") tmp_path.mkdir(exist_ok=True) log_path = Path("log.txt") -# for printing dataframes -peaklist_columns_for_printing = ["INDEX", "ASS", "X_PPM", "Y_PPM", "CLUSTID", "MEMCNT"] -bad_column_selection = [ - "clustid", - "amp", - "center_x_ppm", - "center_y_ppm", - "fwhm_x_hz", - "fwhm_y_hz", - "lineshape", -] -bad_color_selection = [ - "green", - "blue", - "yellow", - "red", - "yellow", - "red", - "magenta", -] peaklist_path_help = "Path to peaklist" @@ -367,225 +346,46 @@ def read( ) -def calculate_lineshape_specific_height_and_fwhm( - lineshape: Lineshape, df: pd.DataFrame -): - match lineshape: - case lineshape.V: - df = calculate_height_for_voigt_lineshape(df) - df = calculate_fwhm_for_voigt_lineshape(df) - - case lineshape.PV: - df = calculate_height_for_pseudo_voigt_lineshape(df) - df = calculate_fwhm_for_pseudo_voigt_lineshape(df) - - case lineshape.G: - df = calculate_height_for_gaussian_lineshape(df) - df = calculate_fwhm_for_pseudo_voigt_lineshape(df) - - case lineshape.L: - df = calculate_height_for_lorentzian_lineshape(df) - df = calculate_fwhm_for_pseudo_voigt_lineshape(df) - - case lineshape.PV_PV: - df = calculate_height_for_pv_pv_lineshape(df) - df = calculate_fwhm_for_pseudo_voigt_lineshape(df) - case _: - df = calculate_fwhm_for_pseudo_voigt_lineshape(df) - return df - - -def get_vclist(vclist, args): - # read vclist - if vclist is None: - vclist = False - elif vclist.exists(): - vclist_data = np.genfromtxt(vclist) - args["vclist_data"] = vclist_data - vclist = True - else: - raise Exception("vclist not found...") - - args["vclist"] = vclist - return args - - -def check_data_shape_is_consistent_with_dims(peakipy_data): - # check data shape is consistent with dims - if len(peakipy_data.dims) != len(peakipy_data.data.shape): - print( - f"Dims are {peakipy_data.dims} while data shape is {peakipy_data.data.shape}?" - ) - exit() - - -def select_specified_planes(plane, peakipy_data): - plane_numbers = np.arange(peakipy_data.data.shape[peakipy_data.dims[0]]) - # only fit specified planes - if plane: - inds = [i for i in plane] - data_inds = [ - (i in inds) for i in range(peakipy_data.data.shape[peakipy_data.dims[0]]) - ] - plane_numbers = np.arange(peakipy_data.data.shape[peakipy_data.dims[0]])[ - data_inds - ] - peakipy_data.data = peakipy_data.data[data_inds] - print( - "[yellow]Using only planes {plane} data now has the following shape[/yellow]", - peakipy_data.data.shape, - ) - if peakipy_data.data.shape[peakipy_data.dims[0]] == 0: - print("[red]You have excluded all the data![/red]", peakipy_data.data.shape) - exit() - return plane_numbers, peakipy_data - - -def exclude_specified_planes(exclude_plane, peakipy_data): - plane_numbers = np.arange(peakipy_data.data.shape[peakipy_data.dims[0]]) - # do not fit these planes - if exclude_plane: - inds = [i for i in exclude_plane] - data_inds = [ - (i not in inds) - for i in range(peakipy_data.data.shape[peakipy_data.dims[0]]) - ] - plane_numbers = np.arange(peakipy_data.data.shape[peakipy_data.dims[0]])[ - data_inds - ] - peakipy_data.data = peakipy_data.data[data_inds] - print( - f"[yellow]Excluding planes {exclude_plane} data now has the following shape[/yellow]", - peakipy_data.data.shape, - ) - if peakipy_data.data.shape[peakipy_data.dims[0]] == 0: - print("[red]You have excluded all the data![/red]", peakipy_data.data.shape) - exit() - return plane_numbers, peakipy_data - - -def check_for_include_column_and_add_if_missing(peakipy_data): - # only include peaks with 'include' - if "include" in peakipy_data.df.columns: - pass - else: - # for compatibility - peakipy_data.df["include"] = peakipy_data.df.apply(lambda _: "yes", axis=1) - return peakipy_data - - -def remove_excluded_peaks(peakipy_data): - if len(peakipy_data.df[peakipy_data.df.include != "yes"]) > 0: - excluded = peakipy_data.df[peakipy_data.df.include != "yes"][ - peaklist_columns_for_printing - ] - table = df_to_rich_table( - excluded, - title="[yellow] Excluded peaks [/yellow]", - columns=excluded.columns, - styles=["yellow" for i in excluded.columns], - ) - print(table) - peakipy_data.df = peakipy_data.df[peakipy_data.df.include == "yes"] - return peakipy_data - - -def warn_if_trying_to_fit_large_clusters(max_cluster_size, peakipy_data): - if max_cluster_size is None: - max_cluster_size = peakipy_data.df.MEMCNT.max() - if peakipy_data.df.MEMCNT.max() > 10: - print( - f"""[red] - ################################################################## - You have some clusters of as many as {max_cluster_size} peaks. - You may want to consider reducing the size of your clusters as the - fits will struggle. - - Otherwise you can use the --max-cluster-size flag to exclude large - clusters - ################################################################## - [/red]""" - ) - else: - max_cluster_size = max_cluster_size - return max_cluster_size - - -def update_linewidths_from_hz_to_points(peakipy_data): - """in case they were adjusted when running edit.py""" - peakipy_data.df["XW"] = peakipy_data.df.XW_HZ * peakipy_data.pt_per_hz_f2 - peakipy_data.df["YW"] = peakipy_data.df.YW_HZ * peakipy_data.pt_per_hz_f1 - return peakipy_data - - -def update_peak_positions_from_ppm_to_points(peakipy_data): - # convert peak positions from ppm to points in case they were adjusted running edit.py - peakipy_data.df["X_AXIS"] = peakipy_data.df.X_PPM.apply( - lambda x: peakipy_data.uc_f2(x, "PPM") - ) - peakipy_data.df["Y_AXIS"] = peakipy_data.df.Y_PPM.apply( - lambda x: peakipy_data.uc_f1(x, "PPM") - ) - peakipy_data.df["X_AXISf"] = peakipy_data.df.X_PPM.apply( - lambda x: peakipy_data.uc_f2.f(x, "PPM") - ) - peakipy_data.df["Y_AXISf"] = peakipy_data.df.Y_PPM.apply( - lambda x: peakipy_data.uc_f1.f(x, "PPM") - ) - return peakipy_data - - -def unpack_xy_bounds(xy_bounds, peakipy_data): - match xy_bounds: - case (0, 0): - xy_bounds = None - case (x, y): - # convert ppm to points - xy_bounds = list(xy_bounds) - xy_bounds[0] = xy_bounds[0] * peakipy_data.pt_per_ppm_f2 - xy_bounds[1] = xy_bounds[1] * peakipy_data.pt_per_ppm_f1 - case _: - raise TypeError( - "xy_bounds should be a tuple (, )" - ) - return xy_bounds - - -def save_data(df, output_name): - suffix = output_name.suffix - if suffix == ".csv": - df.to_csv(output_name, float_format="%.4f", index=False) - - elif suffix == ".tab": - df.to_csv(output_name, sep="\t", float_format="%.4f", index=False) - - else: - df.to_pickle(output_name) - - +fix_help = "Set parameters to fix after initial lineshape fit (see docs)" +xy_bounds_help = ( + "Restrict fitted peak centre within +/- x and y from initial picked position" +) reference_plane_index_help = ( - "Select planes to use for initial estimation of lineshape parameters" + "Select plane(s) to use for initial estimation of lineshape parameters" ) +mp_help = "Use multiprocessing" +vclist_help = "Provide a vclist style file" +plane_help = "Select individual planes for fitting" +exclude_plane_help = "Exclude individual planes from fitting" @app.command(help="Fit NMR data to lineshape models and deconvolute overlapping peaks") def fit( - peaklist_path: Path, - data_path: Path, + peaklist_path: Annotated[Path, typer.Argument(help=peaklist_path_help)], + data_path: Annotated[Path, typer.Argument(help=data_path_help)], output_path: Path, max_cluster_size: Optional[int] = None, lineshape: Lineshape = Lineshape.PV, - fix: List[str] = ["fraction", "sigma", "center"], - xy_bounds: Tuple[float, float] = (0, 0), - vclist: Optional[Path] = None, - plane: Optional[List[int]] = None, - exclude_plane: Optional[List[int]] = None, + fix: Annotated[List[str], typer.Option(help=fix_help)] = [ + "fraction", + "sigma", + "center", + ], + xy_bounds: Annotated[Tuple[float, float], typer.Option(help=xy_bounds_help)] = ( + 0, + 0, + ), + vclist: Annotated[Optional[Path], typer.Option(help=vclist_help)] = None, + plane: Annotated[Optional[List[int]], typer.Option(help=plane_help)] = None, + exclude_plane: Annotated[ + Optional[List[int]], typer.Option(help=exclude_plane_help) + ] = None, reference_plane_index: Annotated[ List[int], typer.Option(help=reference_plane_index_help) ] = [], initial_fit_threshold: Optional[float] = None, jack_knife_sample_errors: bool = False, - mp: bool = True, + mp: Annotated[bool, typer.Option(help=mp_help)] = True, verbose: bool = False, ): """Fit NMR data to lineshape models and deconvolute overlapping peaks @@ -633,7 +433,10 @@ def fit( # read NMR data args = {} config = {} - args, config = update_args_with_values_from_config_file(args) + data_dir = peaklist_path.parent + args, config = update_args_with_values_from_config_file( + args, config_path=data_dir / "peakipy.config" + ) dims = config.get("dims", [0, 1, 2]) peakipy_data = LoadData(peaklist_path, data_path, dims=dims) peakipy_data = check_for_include_column_and_add_if_missing(peakipy_data) @@ -733,551 +536,6 @@ def fit( run_log() -def validate_plane_selection(plane, pseudo3D): - if (plane == []) or (plane == None): - plane = list(range(pseudo3D.n_planes)) - - elif max(plane) > (pseudo3D.n_planes - 1): - raise ValueError( - f"[red]There are {pseudo3D.n_planes} planes in your data you selected --plane {max(plane)}...[red]" - f"plane numbering starts from 0." - ) - elif min(plane) < 0: - raise ValueError( - f"[red]Plane number can not be negative; you selected --plane {min(plane)}...[/red]" - ) - else: - plane = sorted(plane) - - return plane - - -def validate_sample_count(sample_count): - if type(sample_count) == int: - sample_count = sample_count - else: - raise TypeError("Sample count (ccount, rcount) should be an integer") - return sample_count - - -def unpack_plotting_colors(colors): - match colors: - case (data_color, fit_color): - data_color, fit_color = colors - case _: - data_color, fit_color = "green", "blue" - return data_color, fit_color - - -def get_fit_data_for_selected_peak_clusters(fits, clusters): - match clusters: - case None | []: - pass - case _: - # only use these clusters - fits = fits[fits.clustid.isin(clusters)] - if len(fits) < 1: - exit(f"Are you sure clusters {clusters} exist?") - return fits - - -def make_masks_from_plane_data(empty_mask_array, plane_data): - # make masks - individual_masks = [] - for cx, cy, rx, ry, name in zip( - plane_data.center_x, - plane_data.center_y, - plane_data.x_radius, - plane_data.y_radius, - plane_data.assignment, - ): - tmp_mask = make_mask(empty_mask_array, cx, cy, rx, ry) - empty_mask_array += tmp_mask - individual_masks.append(tmp_mask) - filled_mask_array = empty_mask_array - return individual_masks, filled_mask_array - - -def simulate_pv_pv_lineshapes_from_fitted_peak_parameters( - peak_parameters, XY, sim_data, sim_data_singles -): - for amp, c_x, c_y, s_x, s_y, frac_x, frac_y, ls in zip( - peak_parameters.amp, - peak_parameters.center_x, - peak_parameters.center_y, - peak_parameters.sigma_x, - peak_parameters.sigma_y, - peak_parameters.fraction_x, - peak_parameters.fraction_y, - peak_parameters.lineshape, - ): - sim_data_i = pv_pv(XY, amp, c_x, c_y, s_x, s_y, frac_x, frac_y).reshape( - sim_data.shape - ) - sim_data += sim_data_i - sim_data_singles.append(sim_data_i) - return sim_data, sim_data_singles - - -def simulate_lineshapes_from_fitted_peak_parameters( - peak_parameters, XY, sim_data, sim_data_singles -): - shape = sim_data.shape - for amp, c_x, c_y, s_x, s_y, frac, lineshape in zip( - peak_parameters.amp, - peak_parameters.center_x, - peak_parameters.center_y, - peak_parameters.sigma_x, - peak_parameters.sigma_y, - peak_parameters.fraction, - peak_parameters.lineshape, - ): - # print(amp) - match lineshape: - case "G" | "L" | "PV": - sim_data_i = pvoigt2d(XY, amp, c_x, c_y, s_x, s_y, frac).reshape(shape) - case "PV_L": - sim_data_i = pv_l(XY, amp, c_x, c_y, s_x, s_y, frac).reshape(shape) - - case "PV_G": - sim_data_i = pv_g(XY, amp, c_x, c_y, s_x, s_y, frac).reshape(shape) - - case "G_L": - sim_data_i = gaussian_lorentzian( - XY, amp, c_x, c_y, s_x, s_y, frac - ).reshape(shape) - - case "V": - sim_data_i = voigt2d(XY, amp, c_x, c_y, s_x, s_y, frac).reshape(shape) - sim_data += sim_data_i - sim_data_singles.append(sim_data_i) - return sim_data, sim_data_singles - - -@dataclass -class PlottingDataForPlane: - pseudo3D: Pseudo3D - plane_id: int - plane_lineshape_parameters: pd.DataFrame - X: np.array - Y: np.array - mask: np.array - individual_masks: List[np.array] - sim_data: np.array - sim_data_singles: List[np.array] - min_x: int - max_x: int - min_y: int - max_y: int - fit_color: str - data_color: str - rcount: int - ccount: int - - x_plot: np.array = field(init=False) - y_plot: np.array = field(init=False) - masked_data: np.array = field(init=False) - masked_sim_data: np.array = field(init=False) - residual: np.array = field(init=False) - single_colors: List = field(init=False) - - def __post_init__(self): - self.plane_data = self.pseudo3D.data[self.plane_id] - self.masked_data = self.plane_data.copy() - self.masked_sim_data = self.sim_data.copy() - self.masked_data[~self.mask] = np.nan - self.masked_sim_data[~self.mask] = np.nan - - self.x_plot = self.pseudo3D.uc_f2.ppm( - self.X[self.min_y : self.max_y, self.min_x : self.max_x] - ) - self.y_plot = self.pseudo3D.uc_f1.ppm( - self.Y[self.min_y : self.max_y, self.min_x : self.max_x] - ) - self.masked_data = self.masked_data[ - self.min_y : self.max_y, self.min_x : self.max_x - ] - self.sim_plot = self.masked_sim_data[ - self.min_y : self.max_y, self.min_x : self.max_x - ] - self.residual = self.masked_data - self.sim_plot - - for single_mask, single in zip(self.individual_masks, self.sim_data_singles): - single[~single_mask] = np.nan - self.sim_data_singles = [ - sim_data_single[self.min_y : self.max_y, self.min_x : self.max_x] - for sim_data_single in self.sim_data_singles - ] - self.single_colors = [ - cm.viridis(i) for i in np.linspace(0, 1, len(self.sim_data_singles)) - ] - - -def plot_data_is_valid(plot_data: PlottingDataForPlane) -> bool: - if len(plot_data.x_plot) < 1 or len(plot_data.y_plot) < 1: - print( - f"[red]Nothing to plot for cluster {int(plot_data.plane_lineshape_parameters.clustid)}[/red]" - ) - print(f"[red]x={plot_data.x_plot},y={plot_data.y_plot}[/red]") - print( - df_to_rich_table( - plot_data.plane_lineshape_parameters, - title="", - columns=bad_column_selection, - styles=bad_color_selection, - ) - ) - plt.close() - validated = False - # print(Fore.RED + "Maybe your F1/F2 radii for fitting were too small...") - elif plot_data.masked_data.shape[0] == 0 or plot_data.masked_data.shape[1] == 0: - print(f"[red]Nothing to plot for cluster {int(plot_data.plane.clustid)}[/red]") - print( - df_to_rich_table( - plot_data.plane_lineshape_parameters, - title="Bad plane", - columns=bad_column_selection, - styles=bad_color_selection, - ) - ) - spec_lim_f1 = " - ".join( - ["%8.3f" % i for i in plot_data.pseudo3D.f1_ppm_limits] - ) - spec_lim_f2 = " - ".join( - ["%8.3f" % i for i in plot_data.pseudo3D.f2_ppm_limits] - ) - print(f"Spectrum limits are {plot_data.pseudo3D.f2_label:4s}:{spec_lim_f2} ppm") - print(f" {plot_data.pseudo3D.f1_label:4s}:{spec_lim_f1} ppm") - plt.close() - validated = False - else: - validated = True - return validated - - -def create_matplotlib_figure( - plot_data: PlottingDataForPlane, - pdf: PdfPages, - individual=False, - label=False, - ccpn_flag=False, - show=True, -): - fig = plt.figure(figsize=(10, 6)) - ax = fig.add_subplot(projection="3d") - if plot_data_is_valid(plot_data): - cset = ax.contourf( - plot_data.x_plot, - plot_data.y_plot, - plot_data.residual, - zdir="z", - offset=np.nanmin(plot_data.masked_data) * 1.1, - alpha=0.5, - cmap=cm.coolwarm, - ) - cbl = fig.colorbar(cset, ax=ax, shrink=0.5, format="%.2e") - cbl.ax.set_title("Residual", pad=20) - - if individual: - #  for plotting single fit surfaces - single_colors = [ - cm.viridis(i) - for i in np.linspace(0, 1, len(plot_data.sim_data_singles)) - ] - [ - ax.plot_surface( - plot_data.x_plot, - plot_data.y_plot, - z_single, - color=c, - alpha=0.5, - ) - for c, z_single in zip(single_colors, plot_data.sim_data_singles) - ] - ax.plot_wireframe( - plot_data.x_plot, - plot_data.y_plot, - plot_data.sim_plot, - # colors=[cm.coolwarm(i) for i in np.ravel(residual)], - colors=plot_data.fit_color, - linestyle="--", - label="fit", - rcount=plot_data.rcount, - ccount=plot_data.ccount, - ) - ax.plot_wireframe( - plot_data.x_plot, - plot_data.y_plot, - plot_data.masked_data, - colors=plot_data.data_color, - linestyle="-", - label="data", - rcount=plot_data.rcount, - ccount=plot_data.ccount, - ) - ax.set_ylabel(plot_data.pseudo3D.f1_label) - ax.set_xlabel(plot_data.pseudo3D.f2_label) - - # axes will appear inverted - ax.view_init(30, 120) - - title = f"Plane={plot_data.plane_id},Cluster={plot_data.plane_lineshape_parameters.clustid.iloc[0]}" - plt.title(title) - print(f"[green]Plotting: {title}[/green]") - out_str = "Volumes (Heights)\n===========\n" - for _, row in plot_data.plane_lineshape_parameters.iterrows(): - out_str += f"{row.assignment} = {row.amp:.3e} ({row.height:.3e})\n" - if label: - ax.text( - row.center_x_ppm, - row.center_y_ppm, - row.height * 1.2, - row.assignment, - (1, 1, 1), - ) - - ax.text2D( - -0.5, - 1.0, - out_str, - transform=ax.transAxes, - fontsize=10, - fontfamily="sans-serif", - va="top", - bbox=dict(boxstyle="round", ec="k", fc="k", alpha=0.5), - ) - - ax.legend() - - if show: - - def exit_program(event): - exit() - - def next_plot(event): - plt.close() - - axexit = plt.axes([0.81, 0.05, 0.1, 0.075]) - bnexit = Button(axexit, "Exit") - bnexit.on_clicked(exit_program) - axnext = plt.axes([0.71, 0.05, 0.1, 0.075]) - bnnext = Button(axnext, "Next") - bnnext.on_clicked(next_plot) - if ccpn_flag: - plt.show(windowTitle="", size=(1000, 500)) - else: - plt.show() - else: - pdf.savefig() - - plt.close() - - -def create_plotly_wireframe_lines(plot_data: PlottingDataForPlane): - lines = [] - show_legend = lambda x: x < 1 - showlegend = False - # make simulated data wireframe - line_marker = dict(color=plot_data.fit_color, width=4) - counter = 0 - for i, j, k in zip(plot_data.x_plot, plot_data.y_plot, plot_data.sim_plot): - showlegend = show_legend(counter) - lines.append( - go.Scatter3d( - x=i, - y=j, - z=k, - mode="lines", - line=line_marker, - name="fit", - showlegend=showlegend, - ) - ) - counter += 1 - for i, j, k in zip(plot_data.x_plot.T, plot_data.y_plot.T, plot_data.sim_plot.T): - lines.append( - go.Scatter3d( - x=i, y=j, z=k, mode="lines", line=line_marker, showlegend=showlegend - ) - ) - # make experimental data wireframe - line_marker = dict(color=plot_data.data_color, width=4) - counter = 0 - for i, j, k in zip(plot_data.x_plot, plot_data.y_plot, plot_data.masked_data): - showlegend = show_legend(counter) - lines.append( - go.Scatter3d( - x=i, - y=j, - z=k, - mode="lines", - name="data", - line=line_marker, - showlegend=showlegend, - ) - ) - counter += 1 - for i, j, k in zip(plot_data.x_plot.T, plot_data.y_plot.T, plot_data.masked_data.T): - lines.append( - go.Scatter3d( - x=i, y=j, z=k, mode="lines", line=line_marker, showlegend=showlegend - ) - ) - - return lines - - -def construct_surface_legend_string(row): - surface_legend = "" - surface_legend += row.assignment - return surface_legend - - -def create_plotly_surfaces(plot_data: PlottingDataForPlane): - data = [] - color_scale_values = np.linspace(0, 1, len(plot_data.single_colors)) - color_scale = [ - [val, f"rgb({', '.join('%d'%(i*255) for i in c[0:3])})"] - for val, c in zip(color_scale_values, plot_data.single_colors) - ] - for val, individual_peak, row in zip( - color_scale_values, - plot_data.sim_data_singles, - plot_data.plane_lineshape_parameters.itertuples(), - ): - name = construct_surface_legend_string(row) - colors = np.zeros(shape=individual_peak.shape) + val - data.append( - go.Surface( - z=individual_peak, - x=plot_data.x_plot, - y=plot_data.y_plot, - opacity=0.5, - surfacecolor=colors, - colorscale=color_scale, - showscale=False, - cmin=0, - cmax=1, - name=name, - ) - ) - return data - - -def create_residual_contours(plot_data: PlottingDataForPlane): - contours = go.Contour( - x=plot_data.x_plot[0], y=plot_data.y_plot.T[0], z=plot_data.residual - ) - return contours - - -def create_residual_figure(plot_data: PlottingDataForPlane): - data = create_residual_contours(plot_data) - fig = go.Figure(data=data) - fig.update_layout( - title="Fit residuals", - xaxis_title=f"{plot_data.pseudo3D.f2_label} ppm", - yaxis_title=f"{plot_data.pseudo3D.f1_label} ppm", - xaxis=dict(range=[plot_data.x_plot.max(), plot_data.x_plot.min()]), - yaxis=dict(range=[plot_data.y_plot.max(), plot_data.y_plot.min()]), - ) - return fig - - -def create_plotly_figure(plot_data: PlottingDataForPlane): - lines = create_plotly_wireframe_lines(plot_data) - surfaces = create_plotly_surfaces(plot_data) - fig = go.Figure(data=lines + surfaces) - fig = update_axis_ranges(fig, plot_data) - return fig - - -def update_axis_ranges(fig, plot_data: PlottingDataForPlane): - fig.update_layout( - scene=dict( - xaxis=dict(range=[plot_data.x_plot.max(), plot_data.x_plot.min()]), - yaxis=dict(range=[plot_data.y_plot.max(), plot_data.y_plot.min()]), - xaxis_title=f"{plot_data.pseudo3D.f2_label} ppm", - yaxis_title=f"{plot_data.pseudo3D.f1_label} ppm", - annotations=make_annotations(plot_data), - ) - ) - return fig - - -def make_annotations(plot_data: PlottingDataForPlane): - annotations = [] - for row in plot_data.plane_lineshape_parameters.itertuples(): - annotations.append( - dict( - showarrow=True, - x=row.center_x_ppm, - y=row.center_y_ppm, - z=row.height * 1.0, - text=row.assignment, - opacity=0.8, - textangle=0, - arrowsize=1, - ) - ) - return annotations - - -class FitDataModel(BaseModel): - plane: int - clustid: int - assignment: str - memcnt: int - amp: float - height: float - center_x_ppm: float - center_y_ppm: float - fwhm_x_hz: float - fwhm_y_hz: float - lineshape: str - x_radius: float - y_radius: float - center_x: float - center_y: float - sigma_x: float - sigma_y: float - - -class FitDataModelPVGL(FitDataModel): - fraction: float - - -class FitDataModelVoigt(FitDataModel): - fraction: float - gamma_x: float - gamma_y: float - - -class FitDataModelPVPV(FitDataModel): - fraction_x: float - fraction_y: float - - -def validate_fit_data(dict): - lineshape = dict.get("lineshape") - if lineshape in ["PV", "G", "L"]: - fit_data = FitDataModelPVGL(**dict) - elif lineshape == "V": - fit_data = FitDataModelVoigt(**dict) - else: - fit_data = FitDataModelPVPV(**dict) - - return fit_data.model_dump() - - -def validate_fit_dataframe(df): - validated_fit_data = [] - for _, row in df.iterrows(): - fit_data = validate_fit_data(row.to_dict()) - validated_fit_data.append(fit_data) - return pd.DataFrame(validated_fit_data) - - @app.command(help="Interactive plots for checking fits") def check( fits: Path, @@ -1295,7 +553,6 @@ def check( colors: Tuple[str, str] = ("#5e3c99", "#e66101"), verb: bool = False, plotly: bool = False, - config_path: Path = Path("peakipy.config"), ): """Interactive plots for checking fits @@ -1348,6 +605,7 @@ def check( fits = validate_fit_dataframe(pd.read_csv(fits)) args = {} # get dims from config file + config_path = data_path.parent / "peakipy.config" args, config = update_args_with_values_from_config_file(args, config_path) dims = config.get("dims", (1, 2, 3)) diff --git a/peakipy/constants.py b/peakipy/constants.py new file mode 100644 index 00000000..04039817 --- /dev/null +++ b/peakipy/constants.py @@ -0,0 +1,6 @@ +from numpy import log, pi, finfo + + +log2 = log(2) +π = pi +tiny = finfo(float).eps diff --git a/peakipy/fitting.py b/peakipy/fitting.py new file mode 100644 index 00000000..915ebfae --- /dev/null +++ b/peakipy/fitting.py @@ -0,0 +1,645 @@ +from dataclasses import dataclass, field +from typing import List + +import numpy as np +from numpy import sqrt +import pandas as pd +from lmfit import Model +from pydantic import BaseModel + +from peakipy.lineshapes import Lineshape, pvoigt2d, pv_pv, pv_g, pv_l, voigt2d +from peakipy.constants import log2 + + +class FitDataModel(BaseModel): + plane: int + clustid: int + assignment: str + memcnt: int + amp: float + height: float + center_x_ppm: float + center_y_ppm: float + fwhm_x_hz: float + fwhm_y_hz: float + lineshape: str + x_radius: float + y_radius: float + center_x: float + center_y: float + sigma_x: float + sigma_y: float + + +class FitDataModelPVGL(FitDataModel): + fraction: float + + +class FitDataModelVoigt(FitDataModel): + fraction: float + gamma_x: float + gamma_y: float + + +class FitDataModelPVPV(FitDataModel): + fraction_x: float + fraction_y: float + + +def validate_fit_data(dict): + lineshape = dict.get("lineshape") + if lineshape in ["PV", "G", "L"]: + fit_data = FitDataModelPVGL(**dict) + elif lineshape == "V": + fit_data = FitDataModelVoigt(**dict) + else: + fit_data = FitDataModelPVPV(**dict) + + return fit_data.model_dump() + + +def validate_fit_dataframe(df): + validated_fit_data = [] + for _, row in df.iterrows(): + fit_data = validate_fit_data(row.to_dict()) + validated_fit_data.append(fit_data) + return pd.DataFrame(validated_fit_data) + + +def make_mask(data, c_x, c_y, r_x, r_y): + """Create and elliptical mask + + Generate an elliptical boolean mask with center c_x/c_y in points + with radii r_x and r_y. Used to generate fit mask + + :param data: 2D array + :type data: np.array + + :param c_x: x center + :type c_x: float + + :param c_y: y center + :type c_y: float + + :param r_x: radius in x + :type r_x: float + + :param r_y: radius in y + :type r_y: float + + :return: boolean mask of data.shape + :rtype: numpy.array + + """ + a, b = c_y, c_x + n_y, n_x = data.shape + y, x = np.ogrid[-a : n_y - a, -b : n_x - b] + mask = x**2.0 / r_x**2.0 + y**2.0 / r_y**2.0 <= 1.0 + return mask + + +def fix_params(params, to_fix): + """Set parameters to fix + + + :param params: lmfit parameters + :type params: lmfit.Parameters + + :param to_fix: list of parameter name to fix + :type to_fix: list + + :return: updated parameter object + :rtype: lmfit.Parameters + + """ + for k in params: + for p in to_fix: + if p in k: + params[k].vary = False + + return params + + +def get_params(params, name): + ps = [] + ps_err = [] + names = [] + prefixes = [] + for k in params: + if name in k: + ps.append(params[k].value) + ps_err.append(params[k].stderr) + names.append(k) + prefixes.append(k.split(name)[0]) + return ps, ps_err, names, prefixes + + +@dataclass +class PeakLimits: + """Given a peak position and linewidth in points determine + the limits based on the data + + Arguments + --------- + peak: pd.DataFrame + peak is a row from a pandas dataframe + data: np.array + 2D numpy array + """ + + peak: pd.DataFrame + data: np.array + min_x: int = field(init=False) + max_x: int = field(init=False) + min_y: int = field(init=False) + max_y: int = field(init=False) + + def __post_init__(self): + assert self.peak.Y_AXIS <= self.data.shape[0] + assert self.peak.X_AXIS <= self.data.shape[1] + self.max_y = int(np.ceil(self.peak.Y_AXIS + self.peak.YW)) + 1 + if self.max_y > self.data.shape[0]: + self.max_y = self.data.shape[0] + self.max_x = int(np.ceil(self.peak.X_AXIS + self.peak.XW)) + 1 + if self.max_x > self.data.shape[1]: + self.max_x = self.data.shape[1] + + self.min_y = int(self.peak.Y_AXIS - self.peak.YW) + if self.min_y < 0: + self.min_y = 0 + self.min_x = int(self.peak.X_AXIS - self.peak.XW) + if self.min_x < 0: + self.min_x = 0 + + +def estimate_amplitude(peak, data): + assert len(data.shape) == 2 + limits = PeakLimits(peak, data) + amplitude_est = data[limits.min_y : limits.max_y, limits.min_x : limits.max_x].sum() + return amplitude_est + + +def make_param_dict(peaks, data, lineshape: Lineshape = Lineshape.PV): + """Make dict of parameter names using prefix""" + + param_dict = {} + + for _, peak in peaks.iterrows(): + str_form = lambda x: "%s%s" % (to_prefix(peak.ASS), x) + # using exact value of points (i.e decimal) + param_dict[str_form("center_x")] = peak.X_AXISf + param_dict[str_form("center_y")] = peak.Y_AXISf + # estimate peak volume + amplitude_est = estimate_amplitude(peak, data) + param_dict[str_form("amplitude")] = amplitude_est + # sigma linewidth esimate + param_dict[str_form("sigma_x")] = peak.XW / 2.0 + param_dict[str_form("sigma_y")] = peak.YW / 2.0 + + match lineshape: + case lineshape.V: + #  Voigt G sigma from linewidth esimate + param_dict[str_form("sigma_x")] = peak.XW / ( + 2.0 * sqrt(2.0 * log2) + ) # 3.6013 + param_dict[str_form("sigma_y")] = peak.YW / ( + 2.0 * sqrt(2.0 * log2) + ) # 3.6013 + #  Voigt L gamma from linewidth esimate + param_dict[str_form("gamma_x")] = peak.XW / 2.0 + param_dict[str_form("gamma_y")] = peak.YW / 2.0 + # height + # add height here + + case lineshape.G: + param_dict[str_form("fraction")] = 0.0 + case lineshape.L: + param_dict[str_form("fraction")] = 1.0 + case lineshape.PV_PV: + param_dict[str_form("fraction_x")] = 0.5 + param_dict[str_form("fraction_y")] = 0.5 + case _: + param_dict[str_form("fraction")] = 0.5 + + return param_dict + + +def to_prefix(x): + """ + Peak assignments with characters that are not compatible lmfit model naming + are converted to lmfit "safe" names. + + :param x: Peak assignment to be used as prefix for lmfit model + :type x: str + + :returns: lmfit model prefix (_Peak_assignment_) + :rtype: str + + """ + # must be string + if type(x) != str: + x = str(x) + + prefix = "_" + x + to_replace = [ + [".", "_"], + [" ", ""], + ["{", "_"], + ["}", "_"], + ["[", "_"], + ["]", "_"], + ["-", ""], + ["/", "or"], + ["?", "maybe"], + ["\\", ""], + ["(", "_"], + [")", "_"], + ["@", "_at_"], + ] + for p in to_replace: + prefix = prefix.replace(*p) + return prefix + "_" + + +def make_models( + model, + peaks, + data, + lineshape: Lineshape = Lineshape.PV, + xy_bounds=None, +): + """Make composite models for multiple peaks + + :param model: lineshape function + :type model: function + + :param peaks: instance of pandas.df.groupby("CLUSTID") + :type peaks: pandas.df.groupby("CLUSTID") + + :param data: NMR data + :type data: numpy.array + + :param lineshape: lineshape to use for fit (PV/G/L/PV_PV) + :type lineshape: str + + :param xy_bounds: bounds for peak centers (+/-x, +/-y) + :type xy_bounds: tuple + + :return mod: Composite lmfit model containing all peaks + :rtype mod: lmfit.CompositeModel + + :return p_guess: params for composite model with starting values + :rtype p_guess: lmfit.Parameters + + """ + if len(peaks) == 1: + # make model for first peak + mod = Model(model, prefix="%s" % to_prefix(peaks.ASS.iloc[0])) + # add parameters + param_dict = make_param_dict( + peaks, + data, + lineshape=lineshape, + ) + p_guess = mod.make_params(**param_dict) + + elif len(peaks) > 1: + # make model for first peak + first_peak, *remaining_peaks = peaks.iterrows() + mod = Model(model, prefix="%s" % to_prefix(first_peak[1].ASS)) + for _, peak in remaining_peaks: + mod += Model(model, prefix="%s" % to_prefix(peak.ASS)) + + param_dict = make_param_dict( + peaks, + data, + lineshape=lineshape, + ) + p_guess = mod.make_params(**param_dict) + # add Peak params to p_guess + + update_params(p_guess, param_dict, lineshape=lineshape, xy_bounds=xy_bounds) + + return mod, p_guess + + +def update_params( + params, param_dict, lineshape: Lineshape = Lineshape.PV, xy_bounds=None +): + """Update lmfit parameters with values from Peak + + :param params: lmfit parameters + :type params: lmfit.Parameters object + :param param_dict: parameters corresponding to each peak in fit + :type param_dict: dict + :param lineshape: Lineshape (PV, G, L, PV_PV etc.) + :type lineshape: Lineshape + :param xy_bounds: bounds on xy peak positions + :type xy_bounds: tuple + + :returns: None + :rtype: None + + ToDo + -- deal with boundaries + -- currently positions in points + + """ + for k, v in param_dict.items(): + params[k].value = v + # print("update", k, v) + if "center" in k: + if xy_bounds == None: + # no bounds set + pass + else: + if "center_x" in k: + # set x bounds + x_bound = xy_bounds[0] + params[k].min = v - x_bound + params[k].max = v + x_bound + elif "center_y" in k: + # set y bounds + y_bound = xy_bounds[1] + params[k].min = v - y_bound + params[k].max = v + y_bound + # pass + # print( + # "setting limit of %s, min = %.3e, max = %.3e" + # % (k, params[k].min, params[k].max) + # ) + elif "sigma" in k: + params[k].min = 0.0 + params[k].max = 1e4 + + elif "gamma" in k: + params[k].min = 0.0 + params[k].max = 1e4 + # print( + # "setting limit of %s, min = %.3e, max = %.3e" + # % (k, params[k].min, params[k].max) + # ) + elif "fraction" in k: + # fix weighting between 0 and 1 + params[k].min = 0.0 + params[k].max = 1.0 + + #  fix fraction of G or L + match lineshape: + case lineshape.G | lineshape.L: + params[k].vary = False + case lineshape.PV | lineshape.PV_PV: + params[k].vary = True + case _: + pass + + # return params + + +def make_mask_from_peak_cluster(group, data): + mask = np.zeros(data.shape, dtype=bool) + for _, peak in group.iterrows(): + mask += make_mask( + data, peak.X_AXISf, peak.Y_AXISf, peak.X_RADIUS, peak.Y_RADIUS + ) + return mask, peak + + +def select_reference_planes_using_indices(data, indices: List[int]): + n_planes = data.shape[0] + if indices == []: + return data + + max_index = max(indices) + min_index = min(indices) + + if max_index >= n_planes: + raise IndexError( + f"Your data has {n_planes}. You selected plane {max_index} (allowed indices between 0 and {n_planes-1})" + ) + elif min_index < (-1 * n_planes): + raise IndexError( + f"Your data has {n_planes}. You selected plane {min_index} (allowed indices between -{n_planes} and {n_planes-1})" + ) + else: + data = data[indices] + return data + + +def select_planes_above_threshold_from_masked_data(data, threshold=None): + """This function returns planes with data above the threshold. + + It currently uses absolute intensity values. + Negative thresholds just result in return of the orignal data. + + """ + if threshold == None: + selected_data = data + else: + selected_data = data[np.abs(data).max(axis=1) > threshold] + + if selected_data.shape[0] == 0: + selected_data = data + + return selected_data + + +def validate_plane_selection(plane, pseudo3D): + if (plane == []) or (plane == None): + plane = list(range(pseudo3D.n_planes)) + + elif max(plane) > (pseudo3D.n_planes - 1): + raise ValueError( + f"[red]There are {pseudo3D.n_planes} planes in your data you selected --plane {max(plane)}...[red]" + f"plane numbering starts from 0." + ) + elif min(plane) < 0: + raise ValueError( + f"[red]Plane number can not be negative; you selected --plane {min(plane)}...[/red]" + ) + else: + plane = sorted(plane) + + return plane + + +def slice_peaks_from_data_using_mask(data, mask): + peak_slices = np.array([d[mask] for d in data]) + return peak_slices + + +def get_limits_for_axis_in_points(group_axis_points, mask_radius_in_points): + max_point, min_point = ( + int(np.ceil(max(group_axis_points) + mask_radius_in_points + 1)), + int(np.floor(min(group_axis_points) - mask_radius_in_points)), + ) + return max_point, min_point + + +def deal_with_peaks_on_edge_of_spectrum(data_shape, max_x, min_x, max_y, min_y): + if min_y < 0: + min_y = 0 + + if min_x < 0: + min_x = 0 + + if max_y > data_shape[-2]: + max_y = data_shape[-2] + + if max_x > data_shape[-1]: + max_x = data_shape[-1] + return max_x, min_x, max_y, min_y + + +def make_meshgrid(data_shape): + # must be a better way to make the meshgrid + x = np.arange(data_shape[-1]) + y = np.arange(data_shape[-2]) + XY = np.meshgrid(x, y) + return XY + + +def unpack_xy_bounds(xy_bounds, peakipy_data): + match xy_bounds: + case (0, 0): + xy_bounds = None + case (x, y): + # convert ppm to points + xy_bounds = list(xy_bounds) + xy_bounds[0] = xy_bounds[0] * peakipy_data.pt_per_ppm_f2 + xy_bounds[1] = xy_bounds[1] * peakipy_data.pt_per_ppm_f1 + case _: + raise TypeError( + "xy_bounds should be a tuple (, )" + ) + return xy_bounds + + +def select_specified_planes(plane, peakipy_data): + plane_numbers = np.arange(peakipy_data.data.shape[peakipy_data.dims[0]]) + # only fit specified planes + if plane: + inds = [i for i in plane] + data_inds = [ + (i in inds) for i in range(peakipy_data.data.shape[peakipy_data.dims[0]]) + ] + plane_numbers = np.arange(peakipy_data.data.shape[peakipy_data.dims[0]])[ + data_inds + ] + peakipy_data.data = peakipy_data.data[data_inds] + print( + "[yellow]Using only planes {plane} data now has the following shape[/yellow]", + peakipy_data.data.shape, + ) + if peakipy_data.data.shape[peakipy_data.dims[0]] == 0: + print("[red]You have excluded all the data![/red]", peakipy_data.data.shape) + exit() + return plane_numbers, peakipy_data + + +def exclude_specified_planes(exclude_plane, peakipy_data): + plane_numbers = np.arange(peakipy_data.data.shape[peakipy_data.dims[0]]) + # do not fit these planes + if exclude_plane: + inds = [i for i in exclude_plane] + data_inds = [ + (i not in inds) + for i in range(peakipy_data.data.shape[peakipy_data.dims[0]]) + ] + plane_numbers = np.arange(peakipy_data.data.shape[peakipy_data.dims[0]])[ + data_inds + ] + peakipy_data.data = peakipy_data.data[data_inds] + print( + f"[yellow]Excluding planes {exclude_plane} data now has the following shape[/yellow]", + peakipy_data.data.shape, + ) + if peakipy_data.data.shape[peakipy_data.dims[0]] == 0: + print("[red]You have excluded all the data![/red]", peakipy_data.data.shape) + exit() + return plane_numbers, peakipy_data + + +def get_fit_data_for_selected_peak_clusters(fits, clusters): + match clusters: + case None | []: + pass + case _: + # only use these clusters + fits = fits[fits.clustid.isin(clusters)] + if len(fits) < 1: + exit(f"Are you sure clusters {clusters} exist?") + return fits + + +def make_masks_from_plane_data(empty_mask_array, plane_data): + # make masks + individual_masks = [] + for cx, cy, rx, ry, name in zip( + plane_data.center_x, + plane_data.center_y, + plane_data.x_radius, + plane_data.y_radius, + plane_data.assignment, + ): + tmp_mask = make_mask(empty_mask_array, cx, cy, rx, ry) + empty_mask_array += tmp_mask + individual_masks.append(tmp_mask) + filled_mask_array = empty_mask_array + return individual_masks, filled_mask_array + + +def simulate_pv_pv_lineshapes_from_fitted_peak_parameters( + peak_parameters, XY, sim_data, sim_data_singles +): + for amp, c_x, c_y, s_x, s_y, frac_x, frac_y, ls in zip( + peak_parameters.amp, + peak_parameters.center_x, + peak_parameters.center_y, + peak_parameters.sigma_x, + peak_parameters.sigma_y, + peak_parameters.fraction_x, + peak_parameters.fraction_y, + peak_parameters.lineshape, + ): + sim_data_i = pv_pv(XY, amp, c_x, c_y, s_x, s_y, frac_x, frac_y).reshape( + sim_data.shape + ) + sim_data += sim_data_i + sim_data_singles.append(sim_data_i) + return sim_data, sim_data_singles + + +def simulate_lineshapes_from_fitted_peak_parameters( + peak_parameters, XY, sim_data, sim_data_singles +): + shape = sim_data.shape + for amp, c_x, c_y, s_x, s_y, frac, lineshape in zip( + peak_parameters.amp, + peak_parameters.center_x, + peak_parameters.center_y, + peak_parameters.sigma_x, + peak_parameters.sigma_y, + peak_parameters.fraction, + peak_parameters.lineshape, + ): + # print(amp) + match lineshape: + case "G" | "L" | "PV": + sim_data_i = pvoigt2d(XY, amp, c_x, c_y, s_x, s_y, frac).reshape(shape) + case "PV_L": + sim_data_i = pv_l(XY, amp, c_x, c_y, s_x, s_y, frac).reshape(shape) + + case "PV_G": + sim_data_i = pv_g(XY, amp, c_x, c_y, s_x, s_y, frac).reshape(shape) + + case "G_L": + sim_data_i = gaussian_lorentzian( + XY, amp, c_x, c_y, s_x, s_y, frac + ).reshape(shape) + + case "V": + sim_data_i = voigt2d(XY, amp, c_x, c_y, s_x, s_y, frac).reshape(shape) + sim_data += sim_data_i + sim_data_singles.append(sim_data_i) + return sim_data, sim_data_singles diff --git a/peakipy/io.py b/peakipy/io.py new file mode 100644 index 00000000..88ed5be8 --- /dev/null +++ b/peakipy/io.py @@ -0,0 +1,913 @@ +import sys +from pathlib import Path +from enum import Enum + +import numpy as np +import nmrglue as ng +import pandas as pd +import textwrap +from rich import print +from rich.console import Console + + +from bokeh.palettes import Category20 +from scipy import ndimage +from skimage.morphology import square, binary_closing, disk, rectangle +from skimage.filters import threshold_otsu + +from peakipy.utils import df_to_rich_table +from peakipy.fitting import make_mask + +console = Console() + + +class StrucEl(str, Enum): + square = "square" + disk = "disk" + rectangle = "rectangle" + mask_method = "mask_method" + + +class PeaklistFormat(str, Enum): + a2 = "a2" + a3 = "a3" + sparky = "sparky" + pipe = "pipe" + peakipy = "peakipy" + + +class OutFmt(str, Enum): + csv = "csv" + pkl = "pkl" + + +class Pseudo3D: + """Read dic, data from NMRGlue and dims from input to create a Pseudo3D dataset + + :param dic: from nmrglue.pipe.read + :type dic: dict + + :param data: data from nmrglue.pipe.read + :type data: numpy.array + + :param dims: dimension order i.e [0,1,2] where 0 = planes, 1 = f1, 2 = f2 + :type dims: list + """ + + def __init__(self, dic, data, dims): + # check dimensions + self._udic = ng.pipe.guess_udic(dic, data) + self._ndim = self._udic["ndim"] + + if self._ndim == 1: + err = f"""[red] + ########################################## + NMR Data should be either 2D or 3D + ########################################## + [/red]""" + # raise TypeError(err) + sys.exit(err) + + # check that spectrum has correct number of dims + elif self._ndim != len(dims): + err = f"""[red] + ################################################################# + Your spectrum has {self._ndim} dimensions with shape {data.shape} + but you have given a dimension order of {dims}... + ################################################################# + [/red]""" + # raise ValueError(err) + sys.exit(err) + + elif (self._ndim == 2) and (len(dims) == 2): + self._f1_dim, self._f2_dim = dims + self._planes = 0 + self._uc_f1 = ng.pipe.make_uc(dic, data, dim=self._f1_dim) + self._uc_f2 = ng.pipe.make_uc(dic, data, dim=self._f2_dim) + # make data pseudo3d + self._data = data.reshape((1, data.shape[0], data.shape[1])) + self._dims = [self._planes, self._f1_dim + 1, self._f2_dim + 1] + + else: + self._planes, self._f1_dim, self._f2_dim = dims + self._dims = dims + self._data = data + # make unit conversion dicts + self._uc_f2 = ng.pipe.make_uc(dic, data, dim=self._f2_dim) + self._uc_f1 = ng.pipe.make_uc(dic, data, dim=self._f1_dim) + + #  rearrange data if dims not in standard order + if self._dims != [0, 1, 2]: + # np.argsort returns indices of array for order 0,1,2 to transpose data correctly + # self._dims = np.argsort(self._dims) + self._data = np.transpose(data, self._dims) + + self._dic = dic + + self._f1_label = self._udic[self._f1_dim]["label"] + self._f2_label = self._udic[self._f2_dim]["label"] + + @property + def uc_f1(self): + """Return unit conversion dict for F1""" + return self._uc_f1 + + @property + def uc_f2(self): + """Return unit conversion dict for F2""" + return self._uc_f2 + + @property + def dims(self): + """Return dimension order""" + return self._dims + + @property + def data(self): + """Return array containing data""" + return self._data + + @data.setter + def data(self, data): + self._data = data + + @property + def dic(self): + return self._dic + + @property + def udic(self): + return self._udic + + @property + def ndim(self): + return self._ndim + + @property + def f1_label(self): + # dim label + return self._f1_label + + @property + def f2_label(self): + # dim label + return self._f2_label + + @property + def planes(self): + return self.dims[0] + + @property + def n_planes(self): + return self.data.shape[self.planes] + + @property + def f1(self): + return self.dims[1] + + @property + def f2(self): + return self.dims[2] + + # size of f1 and f2 in points + @property + def f2_size(self): + """Return size of f2 dimension in points""" + return self._udic[self._f2_dim]["size"] + + @property + def f1_size(self): + """Return size of f1 dimension in points""" + return self._udic[self._f1_dim]["size"] + + # points per ppm + @property + def pt_per_ppm_f1(self): + return self.f1_size / ( + self._udic[self._f1_dim]["sw"] / self._udic[self._f1_dim]["obs"] + ) + + @property + def pt_per_ppm_f2(self): + return self.f2_size / ( + self._udic[self._f2_dim]["sw"] / self._udic[self._f2_dim]["obs"] + ) + + # points per hz + @property + def pt_per_hz_f1(self): + return self.f1_size / self._udic[self._f1_dim]["sw"] + + @property + def pt_per_hz_f2(self): + return self.f2_size / self._udic[self._f2_dim]["sw"] + + # hz per point + @property + def hz_per_pt_f1(self): + return 1.0 / self.pt_per_hz_f1 + + @property + def hz_per_pt_f2(self): + return 1.0 / self.pt_per_hz_f2 + + # ppm per point + @property + def ppm_per_pt_f1(self): + return 1.0 / self.pt_per_ppm_f1 + + @property + def ppm_per_pt_f2(self): + return 1.0 / self.pt_per_ppm_f2 + + # get ppm limits for ppm scales + @property + def f2_ppm_scale(self): + return self.uc_f2.ppm_scale() + + @property + def f1_ppm_scale(self): + return self.uc_f1.ppm_scale() + + @property + def f2_ppm_limits(self): + return self.uc_f2.ppm_limits() + + @property + def f1_ppm_limits(self): + return self.uc_f1.ppm_limits() + + @property + def f1_ppm_max(self): + return max(self.f1_ppm_limits) + + @property + def f1_ppm_min(self): + return min(self.f1_ppm_limits) + + @property + def f2_ppm_max(self): + return max(self.f2_ppm_limits) + + @property + def f2_ppm_min(self): + return min(self.f2_ppm_limits) + + @property + def f2_ppm_0(self): + return self.f2_ppm_limits[0] + + @property + def f2_ppm_1(self): + return self.f2_ppm_limits[1] + + @property + def f1_ppm_0(self): + return self.f1_ppm_limits[0] + + @property + def f1_ppm_1(self): + return self.f1_ppm_limits[1] + + +class UnknownFormat(Exception): + pass + + +class Peaklist(Pseudo3D): + """Read analysis, sparky or NMRPipe peak list and convert to NMRPipe-ish format also find peak clusters + + Parameters + ---------- + path : path-like or str + path to peaklist + data_path : ndarray + NMRPipe format data + fmt : str + a2|a3|sparky|pipe + dims: list + [planes,y,x] + radii: list + [x,y] Mask radii in ppm + + + Methods + ------- + + clusters : + mask_method : + adaptive_clusters : + + Returns + ------- + df : pandas DataFrame + dataframe containing peaklist + + """ + + def __init__( + self, + path, + data_path, + fmt: PeaklistFormat = PeaklistFormat.a2, + dims=[0, 1, 2], + radii=[0.04, 0.4], + posF1="Position F2", + posF2="Position F1", + verbose=False, + ): + dic, data = ng.pipe.read(data_path) + Pseudo3D.__init__(self, dic, data, dims) + self.fmt = fmt + self.peaklist_path = path + self.data_path = data_path + self.verbose = verbose + self._radii = radii + self._thres = None + if self.verbose: + print( + "Points per hz f1 = %.3f, f2 = %.3f" + % (self.pt_per_hz_f1, self.pt_per_hz_f2) + ) + + self._analysis_to_pipe_dic = { + "#": "INDEX", + "Position F1": "X_PPM", + "Position F2": "Y_PPM", + "Line Width F1 (Hz)": "XW_HZ", + "Line Width F2 (Hz)": "YW_HZ", + "Height": "HEIGHT", + "Volume": "VOL", + } + self._assign_to_pipe_dic = { + "#": "INDEX", + "Pos F1": "X_PPM", + "Pos F2": "Y_PPM", + "LW F1 (Hz)": "XW_HZ", + "LW F2 (Hz)": "YW_HZ", + "Height": "HEIGHT", + "Volume": "VOL", + } + + self._sparky_to_pipe_dic = { + "index": "INDEX", + "w1": "X_PPM", + "w2": "Y_PPM", + "lw1 (hz)": "XW_HZ", + "lw2 (hz)": "YW_HZ", + "Height": "HEIGHT", + "Volume": "VOL", + "Assignment": "ASS", + } + + self._analysis_to_pipe_dic[posF1] = "Y_PPM" + self._analysis_to_pipe_dic[posF2] = "X_PPM" + + self._df = self.read_peaklist() + + def read_peaklist(self): + match self.fmt: + case self.fmt.a2: + self._df = self._read_analysis() + + case self.fmt.a3: + self._df = self._read_assign() + + case self.fmt.sparky: + self._df = self._read_sparky() + + case self.fmt.pipe: + self._df = self._read_pipe() + + case _: + raise UnknownFormat("I don't know this format: {self.fmt}") + + return self._df + + @property + def df(self): + return self._df + + @df.setter + def df(self, df): + self._df = df + return self._df + + @property + def radii(self): + return self._radii + + @property + def f2_radius(self): + """radius for fitting mask in f2""" + return self.radii[0] + + @property + def f1_radius(self): + """radius for fitting mask in f1""" + return self.radii[1] + + @property + def analysis_to_pipe_dic(self): + return self._analysis_to_pipe_dic + + @property + def assign_to_pipe_dic(self): + return self._assign_to_pipe_dic + + @property + def sparky_to_pipe_dic(self): + return self._sparky_to_pipe_dic + + @property + def thres(self): + if self._thres == None: + self._thres = abs(threshold_otsu(self.data[0])) + return self._thres + else: + return self._thres + + def update_df(self): + # int point value + self.df["X_AXIS"] = self.df.X_PPM.apply(lambda x: self.uc_f2(x, "ppm")) + self.df["Y_AXIS"] = self.df.Y_PPM.apply(lambda x: self.uc_f1(x, "ppm")) + # decimal point value + self.df["X_AXISf"] = self.df.X_PPM.apply(lambda x: self.uc_f2.f(x, "ppm")) + self.df["Y_AXISf"] = self.df.Y_PPM.apply(lambda x: self.uc_f1.f(x, "ppm")) + # in case of missing values (should estimate though) + self.df["XW_HZ"] = self.df.XW_HZ.replace("None", "20.0") + self.df["YW_HZ"] = self.df.YW_HZ.replace("None", "20.0") + self.df["XW_HZ"] = self.df.XW_HZ.replace(np.NaN, "20.0") + self.df["YW_HZ"] = self.df.YW_HZ.replace(np.NaN, "20.0") + # convert linewidths to float + self.df["XW_HZ"] = self.df.XW_HZ.apply(lambda x: float(x)) + self.df["YW_HZ"] = self.df.YW_HZ.apply(lambda x: float(x)) + # convert Hz lw to points + self.df["XW"] = self.df.XW_HZ.apply(lambda x: x * self.pt_per_hz_f2) + self.df["YW"] = self.df.YW_HZ.apply(lambda x: x * self.pt_per_hz_f1) + # makes an assignment column from Assign F1 and Assign F2 columns + # in analysis2.x and ccpnmr v3 assign peak lists + if self.fmt in [PeaklistFormat.a2, PeaklistFormat.a3]: + self.df["ASS"] = self.df.apply( + # lambda i: "".join([i["Assign F1"], i["Assign F2"]]), axis=1 + lambda i: f"{i['Assign F1']}_{i['Assign F2']}", + axis=1, + ) + + # make default values for X and Y radii for fit masks + self.df["X_RADIUS_PPM"] = np.zeros(len(self.df)) + self.f2_radius + self.df["Y_RADIUS_PPM"] = np.zeros(len(self.df)) + self.f1_radius + self.df["X_RADIUS"] = self.df.X_RADIUS_PPM.apply( + lambda x: x * self.pt_per_ppm_f2 + ) + self.df["Y_RADIUS"] = self.df.Y_RADIUS_PPM.apply( + lambda x: x * self.pt_per_ppm_f1 + ) + # add include column + if "include" in self.df.columns: + pass + else: + self.df["include"] = self.df.apply(lambda x: "yes", axis=1) + + # check assignments for duplicates + self.check_assignments() + # check that peaks are within the bounds of the data + self.check_peak_bounds() + + def add_fix_bound_columns(self): + """add columns containing parameter bounds (param_upper/param_lower) + and whether or not parameter should be fixed (yes/no) + + For parameter bounding: + + Column names are _upper and _lower for upper and lower bounds respectively. + Values are given as floating point. Value of 0.0 indicates that parameter is unbounded + X/Y positions are given in ppm + Linewidths are given in Hz + + For parameter fixing: + + Column names are _fix. + Values are given as a string 'yes' or 'no' + + """ + pass + + def _read_analysis(self): + df = pd.read_csv(self.peaklist_path, delimiter="\t") + new_columns = [self.analysis_to_pipe_dic.get(i, i) for i in df.columns] + pipe_columns = dict(zip(df.columns, new_columns)) + df = df.rename(index=str, columns=pipe_columns) + + return df + + def _read_assign(self): + df = pd.read_csv(self.peaklist_path, delimiter="\t") + new_columns = [self.assign_to_pipe_dic.get(i, i) for i in df.columns] + pipe_columns = dict(zip(df.columns, new_columns)) + df = df.rename(index=str, columns=pipe_columns) + + return df + + def _read_sparky(self): + df = pd.read_csv( + self.peaklist_path, + skiprows=1, + sep=r"\s+", + names=["ASS", "Y_PPM", "X_PPM", "VOLUME", "HEIGHT", "YW_HZ", "XW_HZ"], + ) + df["INDEX"] = df.index + + return df + + def _read_pipe(self): + to_skip = 0 + with open(self.peaklist_path) as f: + lines = f.readlines() + for line in lines: + if line.startswith("VARS"): + columns = line.strip().split()[1:] + elif line[:5].strip(" ").isdigit(): + break + else: + to_skip += 1 + df = pd.read_csv( + self.peaklist_path, skiprows=to_skip, names=columns, sep=r"\s+" + ) + return df + + def check_assignments(self): + # self.df["ASS"] = self.df. + self.df["ASS"] = self.df.ASS.astype(object) + self.df.loc[self.df["ASS"].isnull(), "ASS"] = "None_dummy_0" + self.df["ASS"] = self.df.ASS.astype(str) + duplicates_bool = self.df.ASS.duplicated() + duplicates = self.df.ASS[duplicates_bool] + if len(duplicates) > 0: + console.print( + textwrap.dedent( + """ + ############################################################################# + You have duplicated assignments in your list... + Currently each peak needs a unique assignment. Sorry about that buddy... + ############################################################################# + """ + ), + style="yellow", + ) + self.df.loc[duplicates_bool, "ASS"] = [ + f"{i}_dummy_{num+1}" for num, i in enumerate(duplicates) + ] + if self.verbose: + print("Here are the duplicates") + print(duplicates) + print(self.df.ASS) + + print( + textwrap.dedent( + """ + Creating dummy assignments for duplicates + + """ + ) + ) + + def check_peak_bounds(self): + columns_to_print = ["INDEX", "ASS", "X_AXIS", "Y_AXIS", "X_PPM", "Y_PPM"] + # check that peaks are within the bounds of spectrum + within_x = (self.df.X_PPM < self.f2_ppm_max) & (self.df.X_PPM > self.f2_ppm_min) + within_y = (self.df.Y_PPM < self.f1_ppm_max) & (self.df.Y_PPM > self.f1_ppm_min) + self.excluded = self.df[~(within_x & within_y)] + self.df = self.df[within_x & within_y] + if len(self.excluded) > 0: + print( + textwrap.dedent( + f"""[red] + ################################################################################# + + Excluding the following peaks as they are not within the spectrum which has shape + + {self.data.shape} + [/red]""" + ) + ) + table_to_print = df_to_rich_table( + self.excluded, + title="Excluded", + columns=columns_to_print, + styles=["red" for i in columns_to_print], + ) + print(table_to_print) + print( + "[red]#################################################################################[/red]" + ) + + def clusters( + self, + thres=None, + struc_el: StrucEl = StrucEl.disk, + struc_size=(3,), + l_struc=None, + ): + """Find clusters of peaks + + :param thres: threshold for positive signals above which clusters are selected. If None then threshold_otsu is used + :type thres: float + + :param struc_el: 'square'|'disk'|'rectangle' + structuring element for binary_closing of thresholded data can be square, disc or rectangle + :type struc_el: str + + :param struc_size: size/dimensions of structuring element + for square and disk first element of tuple is used (for disk value corresponds to radius) + for rectangle, tuple corresponds to (width,height). + :type struc_size: tuple + + + """ + peaks = [[y, x] for y, x in zip(self.df.Y_AXIS, self.df.X_AXIS)] + + if thres == None: + thres = self.thres + self._thres = abs(threshold_otsu(self.data[0])) + else: + self._thres = thres + + # get positive and negative + thresh_data = np.bitwise_or( + self.data[0] < (self._thres * -1.0), self.data[0] > self._thres + ) + + match struc_el: + case struc_el.disk: + radius = struc_size[0] + if self.verbose: + print(f"using disk with {radius}") + closed_data = binary_closing(thresh_data, disk(int(radius))) + + case struc_el.square: + width = struc_size[0] + if self.verbose: + print(f"using square with {width}") + closed_data = binary_closing(thresh_data, square(int(width))) + + case struc_el.rectangle: + width, height = struc_size + if self.verbose: + print(f"using rectangle with {width} and {height}") + closed_data = binary_closing( + thresh_data, rectangle(int(width), int(height)) + ) + + case _: + if self.verbose: + print(f"Not using any closing function") + closed_data = thresh_data + + labeled_array, num_features = ndimage.label(closed_data, l_struc) + + self.df.loc[:, "CLUSTID"] = [labeled_array[i[0], i[1]] for i in peaks] + + #  renumber "0" clusters + max_clustid = self.df["CLUSTID"].max() + n_of_zeros = len(self.df[self.df["CLUSTID"] == 0]["CLUSTID"]) + self.df.loc[self.df[self.df["CLUSTID"] == 0].index, "CLUSTID"] = np.arange( + max_clustid + 1, n_of_zeros + max_clustid + 1, dtype=int + ) + + # count how many peaks per cluster + for ind, group in self.df.groupby("CLUSTID"): + self.df.loc[group.index, "MEMCNT"] = len(group) + + self.df.loc[:, "color"] = self.df.apply( + lambda x: Category20[20][int(x.CLUSTID) % 20] if x.MEMCNT > 1 else "black", + axis=1, + ) + return ClustersResult(labeled_array, num_features, closed_data, peaks) + + def mask_method(self, overlap=1.0, l_struc=None): + """connect clusters based on overlap of fitting masks + + :param overlap: fraction of mask for which overlaps are calculated + :type overlap: float + + :returns ClusterResult: Instance of ClusterResult + :rtype: ClustersResult + """ + # overlap is positive + overlap = abs(overlap) + + self._thres = threshold_otsu(self.data[0]) + + mask = np.zeros(self.data[0].shape, dtype=bool) + + for ind, peak in self.df.iterrows(): + mask += make_mask( + self.data[0], + peak.X_AXISf, + peak.Y_AXISf, + peak.X_RADIUS * overlap, + peak.Y_RADIUS * overlap, + ) + + peaks = [[y, x] for y, x in zip(self.df.Y_AXIS, self.df.X_AXIS)] + labeled_array, num_features = ndimage.label(mask, l_struc) + + self.df.loc[:, "CLUSTID"] = [labeled_array[i[0], i[1]] for i in peaks] + + #  renumber "0" clusters + max_clustid = self.df["CLUSTID"].max() + n_of_zeros = len(self.df[self.df["CLUSTID"] == 0]["CLUSTID"]) + self.df.loc[self.df[self.df["CLUSTID"] == 0].index, "CLUSTID"] = np.arange( + max_clustid + 1, n_of_zeros + max_clustid + 1, dtype=int + ) + + # count how many peaks per cluster + for ind, group in self.df.groupby("CLUSTID"): + self.df.loc[group.index, "MEMCNT"] = len(group) + + self.df.loc[:, "color"] = self.df.apply( + lambda x: Category20[20][int(x.CLUSTID) % 20] if x.MEMCNT > 1 else "black", + axis=1, + ) + + return ClustersResult(labeled_array, num_features, mask, peaks) + + def to_fuda(self, fname="params.fuda"): + with open("peaks.fuda", "w") as peaks_fuda: + for ass, f1_ppm, f2_ppm in zip(self.df.ASS, self.df.Y_PPM, self.df.X_PPM): + peaks_fuda.write(f"{ass}\t{f1_ppm:.3f}\t{f2_ppm:.3f}\n") + groups = self.df.groupby("CLUSTID") + fuda_params = Path(fname) + overlap_peaks = "" + + for ind, group in groups: + if len(group) > 1: + overlap_peaks_str = ";".join(group.ASS) + overlap_peaks += f"OVERLAP_PEAKS=({overlap_peaks_str})\n" + + fuda_file = textwrap.dedent( + f"""\ + +# Read peaklist and spectrum info +PEAKLIST=peaks.fuda +SPECFILE={self.data_path} +PARAMETERFILE=(bruker;vclist) +ZCORR=ncyc +NOISE={self.thres} # you'll need to adjust this +BASELINE=N +VERBOSELEVEL=5 +PRINTDATA=Y +LM=(MAXFEV=250;TOL=1e-5) +#Specify the default values. All values are in ppm: +DEF_LINEWIDTH_F1={self.f1_radius} +DEF_LINEWIDTH_F2={self.f2_radius} +DEF_RADIUS_F1={self.f1_radius} +DEF_RADIUS_F2={self.f2_radius} +SHAPE=GLORE +# OVERLAP PEAKS +{overlap_peaks}""" + ) + with open(fuda_params, "w") as f: + print(f"Writing FuDA file {fuda_file}") + f.write(fuda_file) + if self.verbose: + print(overlap_peaks) + + +class ClustersResult: + """Class to store results of clusters function""" + + def __init__(self, labeled_array, num_features, closed_data, peaks): + self._labeled_array = labeled_array + self._num_features = num_features + self._closed_data = closed_data + self._peaks = peaks + + @property + def labeled_array(self): + return self._labeled_array + + @property + def num_features(self): + return self._num_features + + @property + def closed_data(self): + return self._closed_data + + @property + def peaks(self): + return self._peaks + + +class LoadData(Peaklist): + """Load peaklist data from peakipy .csv file output from either peakipy read or edit + + read_peaklist is redefined to just read a .csv file + + check_data_frame makes sure data frame is in good shape for setting up fits + + """ + + def read_peaklist(self): + if self.peaklist_path.suffix == ".csv": + self.df = pd.read_csv(self.peaklist_path) # , comment="#") + + elif self.peaklist_path.suffix == ".tab": + self.df = pd.read_csv(self.peaklist_path, sep="\t") # comment="#") + + else: + self.df = pd.read_pickle(self.peaklist_path) + + self._thres = threshold_otsu(self.data[0]) + + return self.df + + def check_data_frame(self): + # make diameter columns + if "X_DIAMETER_PPM" in self.df.columns: + pass + else: + self.df["X_DIAMETER_PPM"] = self.df["X_RADIUS_PPM"] * 2.0 + self.df["Y_DIAMETER_PPM"] = self.df["Y_RADIUS_PPM"] * 2.0 + + #  make a column to track edited peaks + if "Edited" in self.df.columns: + pass + else: + self.df["Edited"] = np.zeros(len(self.df), dtype=bool) + + # create include column if it doesn't exist + if "include" in self.df.columns: + pass + else: + self.df["include"] = self.df.apply(lambda _: "yes", axis=1) + + # color clusters + self.df["color"] = self.df.apply( + lambda x: Category20[20][int(x.CLUSTID) % 20] if x.MEMCNT > 1 else "black", + axis=1, + ) + + # get rid of unnamed columns + unnamed_cols = [i for i in self.df.columns if "Unnamed:" in i] + self.df = self.df.drop(columns=unnamed_cols) + + def update_df(self): + """Slightly modified to retain previous configurations""" + # int point value + self.df["X_AXIS"] = self.df.X_PPM.apply(lambda x: self.uc_f2(x, "ppm")) + self.df["Y_AXIS"] = self.df.Y_PPM.apply(lambda x: self.uc_f1(x, "ppm")) + # decimal point value + self.df["X_AXISf"] = self.df.X_PPM.apply(lambda x: self.uc_f2.f(x, "ppm")) + self.df["Y_AXISf"] = self.df.Y_PPM.apply(lambda x: self.uc_f1.f(x, "ppm")) + # in case of missing values (should estimate though) + self.df["XW_HZ"] = self.df.XW_HZ.replace(np.NaN, "20.0") + self.df["YW_HZ"] = self.df.YW_HZ.replace(np.NaN, "20.0") + # convert linewidths to float + self.df["XW_HZ"] = self.df.XW_HZ.apply(lambda x: float(x)) + self.df["YW_HZ"] = self.df.YW_HZ.apply(lambda x: float(x)) + # convert Hz lw to points + self.df["XW"] = self.df.XW_HZ.apply(lambda x: x * self.pt_per_hz_f2) + self.df["YW"] = self.df.YW_HZ.apply(lambda x: x * self.pt_per_hz_f1) + # makes an assignment column + if self.fmt == "a2": + self.df["ASS"] = self.df.apply( + lambda i: "".join([i["Assign F1"], i["Assign F2"]]), axis=1 + ) + + # make default values for X and Y radii for fit masks + # self.df["X_RADIUS_PPM"] = np.zeros(len(self.df)) + self.f2_radius + # self.df["Y_RADIUS_PPM"] = np.zeros(len(self.df)) + self.f1_radius + self.df["X_RADIUS"] = self.df.X_RADIUS_PPM.apply( + lambda x: x * self.pt_per_ppm_f2 + ) + self.df["Y_RADIUS"] = self.df.Y_RADIUS_PPM.apply( + lambda x: x * self.pt_per_ppm_f1 + ) + # add include column + if "include" in self.df.columns: + pass + else: + self.df["include"] = self.df.apply(lambda x: "yes", axis=1) + + # check assignments for duplicates + self.check_assignments() + # check that peaks are within the bounds of the data + self.check_peak_bounds() + + +def get_vclist(vclist, args): + # read vclist + if vclist is None: + vclist = False + elif vclist.exists(): + vclist_data = np.genfromtxt(vclist) + args["vclist_data"] = vclist_data + vclist = True + else: + raise Exception("vclist not found...") + + args["vclist"] = vclist + return args diff --git a/peakipy/lineshapes.py b/peakipy/lineshapes.py new file mode 100644 index 00000000..38b53e26 --- /dev/null +++ b/peakipy/lineshapes.py @@ -0,0 +1,522 @@ +from enum import Enum + +import pandas as pd +from numpy import sqrt, exp, log +from scipy.special import wofz + +from peakipy.constants import π, tiny, log2 + + +class Lineshape(str, Enum): + PV = "PV" + V = "V" + G = "G" + L = "L" + PV_PV = "PV_PV" + G_L = "G_L" + PV_G = "PV_G" + PV_L = "PV_L" + + +def gaussian(x, center=0.0, sigma=1.0): + r"""1-dimensional Gaussian function. + + gaussian(x, center, sigma) = + (1/(s2pi*sigma)) * exp(-(1.0*x-center)**2 / (2*sigma**2)) + + :math:`\\frac{1}{ \sqrt{2\pi} } exp \left( \\frac{-(x-center)^2}{2 \sigma^2} \\right)` + + :param x: x + :param center: center + :param sigma: sigma + :type x: numpy.array + :type center: float + :type sigma: float + + :return: 1-dimensional Gaussian + :rtype: numpy.array + + """ + return (1.0 / max(tiny, (sqrt(2 * π) * sigma))) * exp( + -((1.0 * x - center) ** 2) / max(tiny, (2 * sigma**2)) + ) + + +def lorentzian(x, center=0.0, sigma=1.0): + r"""1-dimensional Lorentzian function. + + lorentzian(x, center, sigma) = + (1/(1 + ((1.0*x-center)/sigma)**2)) / (pi*sigma) + + :math:`\\frac{1}{ 1+ \left( \\frac{x-center}{\sigma}\\right)^2} / (\pi\sigma)` + + :param x: x + :param center: center + :param sigma: sigma + :type x: numpy.array + :type center: float + :type sigma: float + + :return: 1-dimensional Lorenztian + :rtype: numpy.array + + """ + return (1.0 / (1 + ((1.0 * x - center) / max(tiny, sigma)) ** 2)) / max( + tiny, (π * sigma) + ) + + +def voigt(x, center=0.0, sigma=1.0, gamma=None): + r"""Return a 1-dimensional Voigt function. + + voigt(x, center, sigma, gamma) = + amplitude*wofz(z).real / (sigma*sqrt(2.0 * π)) + + :math:`V(x,\sigma,\gamma) = (\\frac{Re[\omega(z)]}{\sigma \sqrt{2\pi}})` + + :math:`z=\\frac{x+i\gamma}{\sigma\sqrt{2}}` + + see Voigt_ wiki + + .. _Voigt: https://en.wikipedia.org/wiki/Voigt_profile + + + :param x: x values + :type x: numpy array 1d + :param center: center of lineshape in points + :type center: float + :param sigma: sigma of gaussian + :type sigma: float + :param gamma: gamma of lorentzian + :type gamma: float + + :returns: Voigt lineshape + :rtype: numpy.array + + """ + if gamma is None: + gamma = sigma + + z = (x - center + 1j * gamma) / max(tiny, (sigma * sqrt(2.0))) + return wofz(z).real / max(tiny, (sigma * sqrt(2.0 * π))) + + +def pseudo_voigt(x, center=0.0, sigma=1.0, fraction=0.5): + r"""1-dimensional Pseudo-voigt function + + Superposition of Gaussian and Lorentzian function + + :math:`(1-\phi) G(x,center,\sigma_g) + \phi L(x, center, \sigma)` + + Where :math:`\phi` is the fraction of Lorentzian lineshape and :math:`G` and :math:`L` are Gaussian and + Lorentzian functions, respectively. + + :param x: data + :type x: numpy.array + :param center: center of peak + :type center: float + :param sigma: sigma of lineshape + :type sigma: float + :param fraction: fraction of lorentzian lineshape (between 0 and 1) + :type fraction: float + + :return: pseudo-voigt function + :rtype: numpy.array + + """ + sigma_g = sigma / sqrt(2 * log2) + pv = (1 - fraction) * gaussian(x, center, sigma_g) + fraction * lorentzian( + x, center, sigma + ) + return pv + + +def pvoigt2d( + XY, + amplitude=1.0, + center_x=0.5, + center_y=0.5, + sigma_x=1.0, + sigma_y=1.0, + fraction=0.5, +): + r"""2D pseudo-voigt model + + :math:`(1-fraction) G(x,center,\sigma_{gx}) + (fraction) L(x, center, \sigma_x) * (1-fraction) G(y,center,\sigma_{gy}) + (fraction) L(y, center, \sigma_y)` + + :param XY: meshgrid of X and Y coordinates [X,Y] each with shape Z + :type XY: numpy.array + + :param amplitude: amplitude of peak + :type amplitude: float + + :param center_x: center of peak in x + :type center_x: float + + :param center_y: center of peak in x + :type center_y: float + + :param sigma_x: sigma of lineshape in x + :type sigma_x: float + + :param sigma_y: sigma of lineshape in y + :type sigma_y: float + + :param fraction: fraction of lorentzian lineshape (between 0 and 1) + :type fraction: float + + :return: flattened array of Z values (use Z.reshape(X.shape) for recovery) + :rtype: numpy.array + + """ + x, y = XY + pv_x = pseudo_voigt(x, center_x, sigma_x, fraction) + pv_y = pseudo_voigt(y, center_y, sigma_y, fraction) + return amplitude * pv_x * pv_y + + +def pv_l( + XY, + amplitude=1.0, + center_x=0.5, + center_y=0.5, + sigma_x=1.0, + sigma_y=1.0, + fraction=0.5, +): + """2D lineshape model with pseudo-voigt in x and lorentzian in y + + Arguments + ========= + + -- XY: meshgrid of X and Y coordinates [X,Y] each with shape Z + -- amplitude: peak amplitude (gaussian and lorentzian) + -- center_x: position of peak in x + -- center_y: position of peak in y + -- sigma_x: linewidth in x + -- sigma_y: linewidth in y + -- fraction: fraction of lorentzian in fit + + Returns + ======= + + -- flattened array of Z values (use Z.reshape(X.shape) for recovery) + + """ + + x, y = XY + pv_x = pseudo_voigt(x, center_x, sigma_x, fraction) + pv_y = pseudo_voigt(y, center_y, sigma_y, 1.0) # lorentzian + return amplitude * pv_x * pv_y + + +def pv_g( + XY, + amplitude=1.0, + center_x=0.5, + center_y=0.5, + sigma_x=1.0, + sigma_y=1.0, + fraction=0.5, +): + """2D lineshape model with pseudo-voigt in x and gaussian in y + + Arguments + --------- + + -- XY: meshgrid of X and Y coordinates [X,Y] each with shape Z + -- amplitude: peak amplitude (gaussian and lorentzian) + -- center_x: position of peak in x + -- center_y: position of peak in y + -- sigma_x: linewidth in x + -- sigma_y: linewidth in y + -- fraction: fraction of lorentzian in fit + + Returns + ------- + + -- flattened array of Z values (use Z.reshape(X.shape) for recovery) + + """ + x, y = XY + pv_x = pseudo_voigt(x, center_x, sigma_x, fraction) + pv_y = pseudo_voigt(y, center_y, sigma_y, 0.0) # gaussian + return amplitude * pv_x * pv_y + + +def pv_pv( + XY, + amplitude=1.0, + center_x=0.5, + center_y=0.5, + sigma_x=1.0, + sigma_y=1.0, + fraction_x=0.5, + fraction_y=0.5, +): + """2D lineshape model with pseudo-voigt in x and pseudo-voigt in y + i.e. fraction_x and fraction_y params + + Arguments + ========= + + -- XY: meshgrid of X and Y coordinates [X,Y] each with shape Z + -- amplitude: peak amplitude (gaussian and lorentzian) + -- center_x: position of peak in x + -- center_y: position of peak in y + -- sigma_x: linewidth in x + -- sigma_y: linewidth in y + -- fraction_x: fraction of lorentzian in x + -- fraction_y: fraction of lorentzian in y + + Returns + ======= + + -- flattened array of Z values (use Z.reshape(X.shape) for recovery) + + """ + + x, y = XY + pv_x = pseudo_voigt(x, center_x, sigma_x, fraction_x) + pv_y = pseudo_voigt(y, center_y, sigma_y, fraction_y) + return amplitude * pv_x * pv_y + + +def gaussian_lorentzian( + XY, + amplitude=1.0, + center_x=0.5, + center_y=0.5, + sigma_x=1.0, + sigma_y=1.0, + fraction=0.5, +): + """2D lineshape model with gaussian in x and lorentzian in y + + Arguments + ========= + + -- XY: meshgrid of X and Y coordinates [X,Y] each with shape Z + -- amplitude: peak amplitude (gaussian and lorentzian) + -- center_x: position of peak in x + -- center_y: position of peak in y + -- sigma_x: linewidth in x + -- sigma_y: linewidth in y + -- fraction: fraction of lorentzian in fit + + Returns + ======= + + -- flattened array of Z values (use Z.reshape(X.shape) for recovery) + + """ + x, y = XY + pv_x = pseudo_voigt(x, center_x, sigma_x, 0.0) # gaussian + pv_y = pseudo_voigt(y, center_y, sigma_y, 1.0) # lorentzian + return amplitude * pv_x * pv_y + + +def voigt2d( + XY, + amplitude=1.0, + center_x=0.5, + center_y=0.5, + sigma_x=1.0, + sigma_y=1.0, + gamma_x=1.0, + gamma_y=1.0, + fraction=0.5, +): + fraction = 0.5 + gamma_x = None + gamma_y = None + x, y = XY + voigt_x = voigt(x, center_x, sigma_x, gamma_x) + voigt_y = voigt(y, center_y, sigma_y, gamma_y) + return amplitude * voigt_x * voigt_y + + +def get_lineshape_function(lineshape: Lineshape): + match lineshape: + case lineshape.PV | lineshape.G | lineshape.L: + lineshape_function = pvoigt2d + case lineshape.V: + lineshape_function = voigt2d + case lineshape.PV_PV: + lineshape_function = pv_pv + case lineshape.G_L: + lineshape_function = gaussian_lorentzian + case lineshape.PV_G: + lineshape_function = pv_g + case lineshape.PV_L: + lineshape_function = pv_l + case _: + raise Exception("No lineshape was selected!") + return lineshape_function + + +def calculate_height_for_voigt_lineshape(df): + df["height"] = df.apply( + lambda x: voigt2d( + XY=[0, 0], + center_x=0.0, + center_y=0.0, + sigma_x=x.sigma_x, + sigma_y=x.sigma_y, + gamma_x=x.gamma_x, + gamma_y=x.gamma_y, + amplitude=x.amp, + ), + axis=1, + ) + df["height_err"] = df.apply( + lambda x: x.amp_err * (x.height / x.amp) if x.amp_err != None else 0.0, + axis=1, + ) + return df + + +def calculate_fwhm_for_voigt_lineshape(df): + df["fwhm_g_x"] = df.sigma_x.apply( + lambda x: 2.0 * x * sqrt(2.0 * log(2.0)) + ) # fwhm of gaussian + df["fwhm_g_y"] = df.sigma_y.apply(lambda x: 2.0 * x * sqrt(2.0 * log(2.0))) + df["fwhm_l_x"] = df.gamma_x.apply(lambda x: 2.0 * x) # fwhm of lorentzian + df["fwhm_l_y"] = df.gamma_y.apply(lambda x: 2.0 * x) + df["fwhm_x"] = df.apply( + lambda x: 0.5346 * x.fwhm_l_x + + sqrt(0.2166 * x.fwhm_l_x**2.0 + x.fwhm_g_x**2.0), + axis=1, + ) + df["fwhm_y"] = df.apply( + lambda x: 0.5346 * x.fwhm_l_y + + sqrt(0.2166 * x.fwhm_l_y**2.0 + x.fwhm_g_y**2.0), + axis=1, + ) + return df + + +def calculate_height_for_pseudo_voigt_lineshape(df): + df["height"] = df.apply( + lambda x: pvoigt2d( + XY=[0, 0], + center_x=0.0, + center_y=0.0, + sigma_x=x.sigma_x, + sigma_y=x.sigma_y, + amplitude=x.amp, + fraction=x.fraction, + ), + axis=1, + ) + df["height_err"] = df.apply(lambda x: x.amp_err * (x.height / x.amp), axis=1) + return df + + +def calculate_fwhm_for_pseudo_voigt_lineshape(df): + df["fwhm_x"] = df.sigma_x.apply(lambda x: x * 2.0) + df["fwhm_y"] = df.sigma_y.apply(lambda x: x * 2.0) + return df + + +def calculate_height_for_gaussian_lineshape(df): + df["height"] = df.apply( + lambda x: pvoigt2d( + XY=[0, 0], + center_x=0.0, + center_y=0.0, + sigma_x=x.sigma_x, + sigma_y=x.sigma_y, + amplitude=x.amp, + fraction=0.0, # gaussian + ), + axis=1, + ) + df["height_err"] = df.apply(lambda x: x.amp_err * (x.height / x.amp), axis=1) + return df + + +def calculate_height_for_lorentzian_lineshape(df): + df["height"] = df.apply( + lambda x: pvoigt2d( + XY=[0, 0], + center_x=0.0, + center_y=0.0, + sigma_x=x.sigma_x, + sigma_y=x.sigma_y, + amplitude=x.amp, + fraction=1.0, # lorentzian + ), + axis=1, + ) + df["height_err"] = df.apply(lambda x: x.amp_err * (x.height / x.amp), axis=1) + return df + + +def calculate_height_for_pv_pv_lineshape(df): + df["height"] = df.apply( + lambda x: pv_pv( + XY=[0, 0], + center_x=0.0, + center_y=0.0, + sigma_x=x.sigma_x, + sigma_y=x.sigma_y, + amplitude=x.amp, + fraction_x=x.fraction_x, + fraction_y=x.fraction_y, + ), + axis=1, + ) + df["height_err"] = df.apply(lambda x: x.amp_err * (x.height / x.amp), axis=1) + return df + + +def calculate_peak_centers_in_ppm(df, peakipy_data): + #  convert values to ppm + df["center_x_ppm"] = df.center_x.apply(lambda x: peakipy_data.uc_f2.ppm(x)) + df["center_y_ppm"] = df.center_y.apply(lambda x: peakipy_data.uc_f1.ppm(x)) + df["init_center_x_ppm"] = df.init_center_x.apply( + lambda x: peakipy_data.uc_f2.ppm(x) + ) + df["init_center_y_ppm"] = df.init_center_y.apply( + lambda x: peakipy_data.uc_f1.ppm(x) + ) + return df + + +def calculate_peak_linewidths_in_hz(df, peakipy_data): + df["sigma_x_ppm"] = df.sigma_x.apply(lambda x: x * peakipy_data.ppm_per_pt_f2) + df["sigma_y_ppm"] = df.sigma_y.apply(lambda x: x * peakipy_data.ppm_per_pt_f1) + df["fwhm_x_ppm"] = df.fwhm_x.apply(lambda x: x * peakipy_data.ppm_per_pt_f2) + df["fwhm_y_ppm"] = df.fwhm_y.apply(lambda x: x * peakipy_data.ppm_per_pt_f1) + df["fwhm_x_hz"] = df.fwhm_x.apply(lambda x: x * peakipy_data.hz_per_pt_f2) + df["fwhm_y_hz"] = df.fwhm_y.apply(lambda x: x * peakipy_data.hz_per_pt_f1) + return df + + +def calculate_lineshape_specific_height_and_fwhm( + lineshape: Lineshape, df: pd.DataFrame +): + match lineshape: + case lineshape.V: + df = calculate_height_for_voigt_lineshape(df) + df = calculate_fwhm_for_voigt_lineshape(df) + + case lineshape.PV: + df = calculate_height_for_pseudo_voigt_lineshape(df) + df = calculate_fwhm_for_pseudo_voigt_lineshape(df) + + case lineshape.G: + df = calculate_height_for_gaussian_lineshape(df) + df = calculate_fwhm_for_pseudo_voigt_lineshape(df) + + case lineshape.L: + df = calculate_height_for_lorentzian_lineshape(df) + df = calculate_fwhm_for_pseudo_voigt_lineshape(df) + + case lineshape.PV_PV: + df = calculate_height_for_pv_pv_lineshape(df) + df = calculate_fwhm_for_pseudo_voigt_lineshape(df) + case _: + df = calculate_fwhm_for_pseudo_voigt_lineshape(df) + return df diff --git a/peakipy/plotting.py b/peakipy/plotting.py new file mode 100644 index 00000000..479f4ace --- /dev/null +++ b/peakipy/plotting.py @@ -0,0 +1,400 @@ +from dataclasses import dataclass, field +from typing import List + +import pandas as pd +import numpy as np +import plotly.graph_objects as go +import matplotlib.pyplot as plt +from matplotlib import cm +from matplotlib.widgets import Button +from matplotlib.backends.backend_pdf import PdfPages +from rich import print + +from peakipy.io import Pseudo3D +from peakipy.utils import df_to_rich_table, bad_color_selection, bad_column_selection + + +@dataclass +class PlottingDataForPlane: + pseudo3D: Pseudo3D + plane_id: int + plane_lineshape_parameters: pd.DataFrame + X: np.array + Y: np.array + mask: np.array + individual_masks: List[np.array] + sim_data: np.array + sim_data_singles: List[np.array] + min_x: int + max_x: int + min_y: int + max_y: int + fit_color: str + data_color: str + rcount: int + ccount: int + + x_plot: np.array = field(init=False) + y_plot: np.array = field(init=False) + masked_data: np.array = field(init=False) + masked_sim_data: np.array = field(init=False) + residual: np.array = field(init=False) + single_colors: List = field(init=False) + + def __post_init__(self): + self.plane_data = self.pseudo3D.data[self.plane_id] + self.masked_data = self.plane_data.copy() + self.masked_sim_data = self.sim_data.copy() + self.masked_data[~self.mask] = np.nan + self.masked_sim_data[~self.mask] = np.nan + + self.x_plot = self.pseudo3D.uc_f2.ppm( + self.X[self.min_y : self.max_y, self.min_x : self.max_x] + ) + self.y_plot = self.pseudo3D.uc_f1.ppm( + self.Y[self.min_y : self.max_y, self.min_x : self.max_x] + ) + self.masked_data = self.masked_data[ + self.min_y : self.max_y, self.min_x : self.max_x + ] + self.sim_plot = self.masked_sim_data[ + self.min_y : self.max_y, self.min_x : self.max_x + ] + self.residual = self.masked_data - self.sim_plot + + for single_mask, single in zip(self.individual_masks, self.sim_data_singles): + single[~single_mask] = np.nan + self.sim_data_singles = [ + sim_data_single[self.min_y : self.max_y, self.min_x : self.max_x] + for sim_data_single in self.sim_data_singles + ] + self.single_colors = [ + cm.viridis(i) for i in np.linspace(0, 1, len(self.sim_data_singles)) + ] + + +def plot_data_is_valid(plot_data: PlottingDataForPlane) -> bool: + if len(plot_data.x_plot) < 1 or len(plot_data.y_plot) < 1: + print( + f"[red]Nothing to plot for cluster {int(plot_data.plane_lineshape_parameters.clustid)}[/red]" + ) + print(f"[red]x={plot_data.x_plot},y={plot_data.y_plot}[/red]") + print( + df_to_rich_table( + plot_data.plane_lineshape_parameters, + title="", + columns=bad_column_selection, + styles=bad_color_selection, + ) + ) + plt.close() + validated = False + # print(Fore.RED + "Maybe your F1/F2 radii for fitting were too small...") + elif plot_data.masked_data.shape[0] == 0 or plot_data.masked_data.shape[1] == 0: + print(f"[red]Nothing to plot for cluster {int(plot_data.plane.clustid)}[/red]") + print( + df_to_rich_table( + plot_data.plane_lineshape_parameters, + title="Bad plane", + columns=bad_column_selection, + styles=bad_color_selection, + ) + ) + spec_lim_f1 = " - ".join( + ["%8.3f" % i for i in plot_data.pseudo3D.f1_ppm_limits] + ) + spec_lim_f2 = " - ".join( + ["%8.3f" % i for i in plot_data.pseudo3D.f2_ppm_limits] + ) + print(f"Spectrum limits are {plot_data.pseudo3D.f2_label:4s}:{spec_lim_f2} ppm") + print(f" {plot_data.pseudo3D.f1_label:4s}:{spec_lim_f1} ppm") + plt.close() + validated = False + else: + validated = True + return validated + + +def create_matplotlib_figure( + plot_data: PlottingDataForPlane, + pdf: PdfPages, + individual=False, + label=False, + ccpn_flag=False, + show=True, +): + fig = plt.figure(figsize=(10, 6)) + ax = fig.add_subplot(projection="3d") + if plot_data_is_valid(plot_data): + cset = ax.contourf( + plot_data.x_plot, + plot_data.y_plot, + plot_data.residual, + zdir="z", + offset=np.nanmin(plot_data.masked_data) * 1.1, + alpha=0.5, + cmap=cm.coolwarm, + ) + cbl = fig.colorbar(cset, ax=ax, shrink=0.5, format="%.2e") + cbl.ax.set_title("Residual", pad=20) + + if individual: + #  for plotting single fit surfaces + single_colors = [ + cm.viridis(i) + for i in np.linspace(0, 1, len(plot_data.sim_data_singles)) + ] + [ + ax.plot_surface( + plot_data.x_plot, + plot_data.y_plot, + z_single, + color=c, + alpha=0.5, + ) + for c, z_single in zip(single_colors, plot_data.sim_data_singles) + ] + ax.plot_wireframe( + plot_data.x_plot, + plot_data.y_plot, + plot_data.sim_plot, + # colors=[cm.coolwarm(i) for i in np.ravel(residual)], + colors=plot_data.fit_color, + linestyle="--", + label="fit", + rcount=plot_data.rcount, + ccount=plot_data.ccount, + ) + ax.plot_wireframe( + plot_data.x_plot, + plot_data.y_plot, + plot_data.masked_data, + colors=plot_data.data_color, + linestyle="-", + label="data", + rcount=plot_data.rcount, + ccount=plot_data.ccount, + ) + ax.set_ylabel(plot_data.pseudo3D.f1_label) + ax.set_xlabel(plot_data.pseudo3D.f2_label) + + # axes will appear inverted + ax.view_init(30, 120) + + title = f"Plane={plot_data.plane_id},Cluster={plot_data.plane_lineshape_parameters.clustid.iloc[0]}" + plt.title(title) + print(f"[green]Plotting: {title}[/green]") + out_str = "Volumes (Heights)\n===========\n" + for _, row in plot_data.plane_lineshape_parameters.iterrows(): + out_str += f"{row.assignment} = {row.amp:.3e} ({row.height:.3e})\n" + if label: + ax.text( + row.center_x_ppm, + row.center_y_ppm, + row.height * 1.2, + row.assignment, + (1, 1, 1), + ) + + ax.text2D( + -0.5, + 1.0, + out_str, + transform=ax.transAxes, + fontsize=10, + fontfamily="sans-serif", + va="top", + bbox=dict(boxstyle="round", ec="k", fc="k", alpha=0.5), + ) + + ax.legend() + + if show: + + def exit_program(event): + exit() + + def next_plot(event): + plt.close() + + axexit = plt.axes([0.81, 0.05, 0.1, 0.075]) + bnexit = Button(axexit, "Exit") + bnexit.on_clicked(exit_program) + axnext = plt.axes([0.71, 0.05, 0.1, 0.075]) + bnnext = Button(axnext, "Next") + bnnext.on_clicked(next_plot) + if ccpn_flag: + plt.show(windowTitle="", size=(1000, 500)) + else: + plt.show() + else: + pdf.savefig() + + plt.close() + + +def create_plotly_wireframe_lines(plot_data: PlottingDataForPlane): + lines = [] + show_legend = lambda x: x < 1 + showlegend = False + # make simulated data wireframe + line_marker = dict(color=plot_data.fit_color, width=4) + counter = 0 + for i, j, k in zip(plot_data.x_plot, plot_data.y_plot, plot_data.sim_plot): + showlegend = show_legend(counter) + lines.append( + go.Scatter3d( + x=i, + y=j, + z=k, + mode="lines", + line=line_marker, + name="fit", + showlegend=showlegend, + ) + ) + counter += 1 + for i, j, k in zip(plot_data.x_plot.T, plot_data.y_plot.T, plot_data.sim_plot.T): + lines.append( + go.Scatter3d( + x=i, y=j, z=k, mode="lines", line=line_marker, showlegend=showlegend + ) + ) + # make experimental data wireframe + line_marker = dict(color=plot_data.data_color, width=4) + counter = 0 + for i, j, k in zip(plot_data.x_plot, plot_data.y_plot, plot_data.masked_data): + showlegend = show_legend(counter) + lines.append( + go.Scatter3d( + x=i, + y=j, + z=k, + mode="lines", + name="data", + line=line_marker, + showlegend=showlegend, + ) + ) + counter += 1 + for i, j, k in zip(plot_data.x_plot.T, plot_data.y_plot.T, plot_data.masked_data.T): + lines.append( + go.Scatter3d( + x=i, y=j, z=k, mode="lines", line=line_marker, showlegend=showlegend + ) + ) + + return lines + + +def construct_surface_legend_string(row): + surface_legend = "" + surface_legend += row.assignment + return surface_legend + + +def create_plotly_surfaces(plot_data: PlottingDataForPlane): + data = [] + color_scale_values = np.linspace(0, 1, len(plot_data.single_colors)) + color_scale = [ + [val, f"rgb({', '.join('%d'%(i*255) for i in c[0:3])})"] + for val, c in zip(color_scale_values, plot_data.single_colors) + ] + for val, individual_peak, row in zip( + color_scale_values, + plot_data.sim_data_singles, + plot_data.plane_lineshape_parameters.itertuples(), + ): + name = construct_surface_legend_string(row) + colors = np.zeros(shape=individual_peak.shape) + val + data.append( + go.Surface( + z=individual_peak, + x=plot_data.x_plot, + y=plot_data.y_plot, + opacity=0.5, + surfacecolor=colors, + colorscale=color_scale, + showscale=False, + cmin=0, + cmax=1, + name=name, + ) + ) + return data + + +def create_residual_contours(plot_data: PlottingDataForPlane): + contours = go.Contour( + x=plot_data.x_plot[0], y=plot_data.y_plot.T[0], z=plot_data.residual + ) + return contours + + +def create_residual_figure(plot_data: PlottingDataForPlane): + data = create_residual_contours(plot_data) + fig = go.Figure(data=data) + fig.update_layout( + title="Fit residuals", + xaxis_title=f"{plot_data.pseudo3D.f2_label} ppm", + yaxis_title=f"{plot_data.pseudo3D.f1_label} ppm", + xaxis=dict(range=[plot_data.x_plot.max(), plot_data.x_plot.min()]), + yaxis=dict(range=[plot_data.y_plot.max(), plot_data.y_plot.min()]), + ) + return fig + + +def create_plotly_figure(plot_data: PlottingDataForPlane): + lines = create_plotly_wireframe_lines(plot_data) + surfaces = create_plotly_surfaces(plot_data) + fig = go.Figure(data=lines + surfaces) + fig = update_axis_ranges(fig, plot_data) + return fig + + +def update_axis_ranges(fig, plot_data: PlottingDataForPlane): + fig.update_layout( + scene=dict( + xaxis=dict(range=[plot_data.x_plot.max(), plot_data.x_plot.min()]), + yaxis=dict(range=[plot_data.y_plot.max(), plot_data.y_plot.min()]), + xaxis_title=f"{plot_data.pseudo3D.f2_label} ppm", + yaxis_title=f"{plot_data.pseudo3D.f1_label} ppm", + annotations=make_annotations(plot_data), + ) + ) + return fig + + +def make_annotations(plot_data: PlottingDataForPlane): + annotations = [] + for row in plot_data.plane_lineshape_parameters.itertuples(): + annotations.append( + dict( + showarrow=True, + x=row.center_x_ppm, + y=row.center_y_ppm, + z=row.height * 1.0, + text=row.assignment, + opacity=0.8, + textangle=0, + arrowsize=1, + ) + ) + return annotations + + +def validate_sample_count(sample_count): + if type(sample_count) == int: + sample_count = sample_count + else: + raise TypeError("Sample count (ccount, rcount) should be an integer") + return sample_count + + +def unpack_plotting_colors(colors): + match colors: + case (data_color, fit_color): + data_color, fit_color = colors + case _: + data_color, fit_color = "green", "blue" + return data_color, fit_color diff --git a/peakipy/utils.py b/peakipy/utils.py new file mode 100644 index 00000000..310ee656 --- /dev/null +++ b/peakipy/utils.py @@ -0,0 +1,239 @@ +import sys +import json +from datetime import datetime +from pathlib import Path +from typing import List + +from rich import print +from rich.table import Table + +# for printing dataframes +peaklist_columns_for_printing = ["INDEX", "ASS", "X_PPM", "Y_PPM", "CLUSTID", "MEMCNT"] +bad_column_selection = [ + "clustid", + "amp", + "center_x_ppm", + "center_y_ppm", + "fwhm_x_hz", + "fwhm_y_hz", + "lineshape", +] +bad_color_selection = [ + "green", + "blue", + "yellow", + "red", + "yellow", + "red", + "magenta", +] + + +def run_log(log_name="run_log.txt"): + """Write log file containing time script was run and with which arguments""" + with open(log_name, "a") as log: + sys_argv = sys.argv + sys_argv[0] = Path(sys_argv[0]).name + run_args = " ".join(sys_argv) + time_stamp = datetime.now() + time_stamp = time_stamp.strftime("%A %d %B %Y at %H:%M") + log.write(f"# Script run on {time_stamp}:\n{run_args}\n") + + +def df_to_rich_table(df, title: str, columns: List[str], styles: str): + """Print dataframe using rich library + + Parameters + ---------- + df : pandas.DataFrame + title : str + title of table + columns : List[str] + list of column names (must be in df) + styles : List[str] + list of styles in same order as columns + """ + table = Table(title=title) + for col, style in zip(columns, styles): + table.add_column(col, style=style) + for _, row in df.iterrows(): + row = row[columns].values + str_row = [] + for i in row: + match i: + case str(): + str_row.append(f"{i}") + case float() if i > 1e5: + str_row.append(f"{i:.1e}") + case float(): + str_row.append(f"{i:.3f}") + case bool(): + str_row.append(f"{i}") + case int(): + str_row.append(f"{i}") + table.add_row(*str_row) + return table + + +def load_config(config_path): + if config_path.exists(): + with open(config_path) as opened_config: + config_dic = json.load(opened_config) + return config_dic + else: + return {} + + +def write_config(config_path, config_dic): + with open(config_path, "w") as config: + config.write(json.dumps(config_dic, sort_keys=True, indent=4)) + + +def update_config_file(config_path, config_kvs): + config_dic = load_config(config_path) + config_dic.update(config_kvs) + write_config(config_path, config_dic) + return config_dic + + +def update_args_with_values_from_config_file(args, config_path="peakipy.config"): + """read a peakipy config file, extract params and update args dict + + :param args: dict containing params extracted from docopt command line + :type args: dict + :param config_path: path to peakipy config file [default: peakipy.config] + :type config_path: str + + :returns args: updated args dict + :rtype args: dict + :returns config: dict that resulted from reading config file + :rtype config: dict + + """ + # update args with values from peakipy.config file + config_path = Path(config_path) + if config_path.exists(): + try: + config = load_config(config_path) + print( + f"[green]Using config file with dims [yellow]{config.get('dims')}[/yellow][/green]" + ) + args["dims"] = config.get("dims", (0, 1, 2)) + noise = config.get("noise") + if noise: + noise = float(noise) + + colors = config.get("colors", ["#5e3c99", "#e66101"]) + except json.decoder.JSONDecodeError: + print( + "[red]Your peakipy.config file is corrupted - maybe your JSON is not correct...[/red]" + ) + print("[red]Not using[/red]") + noise = False + colors = args.get("colors", ("#5e3c99", "#e66101")) + config = {} + else: + print( + "[red]No peakipy.config found - maybe you need to generate one with peakipy read or see docs[/red]" + ) + noise = False + colors = args.get("colors", ("#5e3c99", "#e66101")) + config = {} + + args["noise"] = noise + args["colors"] = colors + + return args, config + + +def update_linewidths_from_hz_to_points(peakipy_data): + """in case they were adjusted when running edit.py""" + peakipy_data.df["XW"] = peakipy_data.df.XW_HZ * peakipy_data.pt_per_hz_f2 + peakipy_data.df["YW"] = peakipy_data.df.YW_HZ * peakipy_data.pt_per_hz_f1 + return peakipy_data + + +def update_peak_positions_from_ppm_to_points(peakipy_data): + # convert peak positions from ppm to points in case they were adjusted running edit.py + peakipy_data.df["X_AXIS"] = peakipy_data.df.X_PPM.apply( + lambda x: peakipy_data.uc_f2(x, "PPM") + ) + peakipy_data.df["Y_AXIS"] = peakipy_data.df.Y_PPM.apply( + lambda x: peakipy_data.uc_f1(x, "PPM") + ) + peakipy_data.df["X_AXISf"] = peakipy_data.df.X_PPM.apply( + lambda x: peakipy_data.uc_f2.f(x, "PPM") + ) + peakipy_data.df["Y_AXISf"] = peakipy_data.df.Y_PPM.apply( + lambda x: peakipy_data.uc_f1.f(x, "PPM") + ) + return peakipy_data + + +def save_data(df, output_name): + suffix = output_name.suffix + + if suffix == ".csv": + df.to_csv(output_name, float_format="%.4f", index=False) + + elif suffix == ".tab": + df.to_csv(output_name, sep="\t", float_format="%.4f", index=False) + + else: + df.to_pickle(output_name) + + +def check_data_shape_is_consistent_with_dims(peakipy_data): + # check data shape is consistent with dims + if len(peakipy_data.dims) != len(peakipy_data.data.shape): + print( + f"Dims are {peakipy_data.dims} while data shape is {peakipy_data.data.shape}?" + ) + exit() + + +def check_for_include_column_and_add_if_missing(peakipy_data): + # only include peaks with 'include' + if "include" in peakipy_data.df.columns: + pass + else: + # for compatibility + peakipy_data.df["include"] = peakipy_data.df.apply(lambda _: "yes", axis=1) + return peakipy_data + + +def remove_excluded_peaks(peakipy_data): + if len(peakipy_data.df[peakipy_data.df.include != "yes"]) > 0: + excluded = peakipy_data.df[peakipy_data.df.include != "yes"][ + peaklist_columns_for_printing + ] + table = df_to_rich_table( + excluded, + title="[yellow] Excluded peaks [/yellow]", + columns=excluded.columns, + styles=["yellow" for i in excluded.columns], + ) + print(table) + peakipy_data.df = peakipy_data.df[peakipy_data.df.include == "yes"] + return peakipy_data + + +def warn_if_trying_to_fit_large_clusters(max_cluster_size, peakipy_data): + if max_cluster_size is None: + max_cluster_size = peakipy_data.df.MEMCNT.max() + if peakipy_data.df.MEMCNT.max() > 10: + print( + f"""[red] + ################################################################## + You have some clusters of as many as {max_cluster_size} peaks. + You may want to consider reducing the size of your clusters as the + fits will struggle. + + Otherwise you can use the --max-cluster-size flag to exclude large + clusters + ################################################################## + [/red]""" + ) + else: + max_cluster_size = max_cluster_size + return max_cluster_size diff --git a/test/test_cli.py b/test/test_cli.py index aec9c35c..d30404c4 100644 --- a/test/test_cli.py +++ b/test/test_cli.py @@ -6,12 +6,10 @@ import peakipy.cli.main from peakipy.cli.main import PeaklistFormat, Lineshape -os.chdir("test") - @pytest.fixture def protein_L(): - path = Path("test_protein_L") + path = Path("test/test_protein_L") return path diff --git a/test/test_data.py b/test/test_data.py index 2a37150e..0b105bf8 100644 --- a/test/test_data.py +++ b/test/test_data.py @@ -6,15 +6,8 @@ from mpl_toolkits.mplot3d import Axes3D from lmfit import Model, report_fit -from peakipy.core import ( - pvoigt2d, - fix_params, - get_params, - make_mask, - # fit_first_plane, - make_models, - Lineshape, -) +from peakipy.lineshapes import pvoigt2d, Lineshape +from peakipy.fitting import make_mask, make_models def fit_first_plane( @@ -153,7 +146,7 @@ def fit_first_plane( for p in peaks: data += pvoigt2d( XY, - *p + *p, # amplitude=1e8, # center_x=200, # center_y=100, diff --git a/test/test_edit.py b/test/test_edit.py new file mode 100644 index 00000000..ab01e4ab --- /dev/null +++ b/test/test_edit.py @@ -0,0 +1 @@ +import panel as pn diff --git a/test/test_fit.py b/test/test_fit.py index 83512aed..c41d0ebf 100644 --- a/test/test_fit.py +++ b/test/test_fit.py @@ -25,7 +25,7 @@ FitPeaksArgs, FitPeaksInput, ) -from peakipy.core import Lineshape, pvoigt2d +from peakipy.lineshapes import Lineshape, pvoigt2d def test_get_fit_peaks_result_validation_model_PVPV(): diff --git a/test/test_fitting.py b/test/test_fitting.py new file mode 100644 index 00000000..01c9ed38 --- /dev/null +++ b/test/test_fitting.py @@ -0,0 +1,692 @@ +import unittest +from pathlib import Path +from collections import namedtuple + +import numpy as np +import pandas as pd +import pytest +import nmrglue as ng +from numpy.testing import assert_array_equal +from lmfit import Model, Parameters + +from peakipy.io import Pseudo3D +from peakipy.fitting import ( + FitDataModel, + validate_fit_data, + validate_fit_dataframe, + select_reference_planes_using_indices, + slice_peaks_from_data_using_mask, + select_planes_above_threshold_from_masked_data, + get_limits_for_axis_in_points, + deal_with_peaks_on_edge_of_spectrum, + estimate_amplitude, + make_mask, + make_mask_from_peak_cluster, + make_meshgrid, + get_params, + fix_params, + make_param_dict, + to_prefix, + make_models, + PeakLimits, + update_params, + make_masks_from_plane_data, +) +from peakipy.lineshapes import Lineshape, pvoigt2d, pv_pv + + +@pytest.fixture +def fitdatamodel_dict(): + return FitDataModel( + plane=1, + clustid=1, + assignment="assignment", + memcnt=1, + amp=10.0, + height=10.0, + center_x_ppm=0.0, + center_y_ppm=0.0, + fwhm_x_hz=10.0, + fwhm_y_hz=10.0, + lineshape="PV", + x_radius=0.04, + y_radius=0.4, + center_x=0.0, + center_y=0.0, + sigma_x=1.0, + sigma_y=1.0, + ).model_dump() + + +def test_validate_fit_data_PVGL(fitdatamodel_dict): + fitdatamodel_dict.update(dict(fraction=0.5)) + validate_fit_data(fitdatamodel_dict) + + fitdatamodel_dict.update(dict(lineshape="G")) + validate_fit_data(fitdatamodel_dict) + + fitdatamodel_dict.update(dict(lineshape="L")) + validate_fit_data(fitdatamodel_dict) + + fitdatamodel_dict.update( + dict(lineshape="V", fraction=0.5, gamma_x=1.0, gamma_y=1.0) + ) + validate_fit_data(fitdatamodel_dict) + + fitdatamodel_dict.update(dict(lineshape="PVPV", fraction_x=0.5, fraction_y=1.0)) + validate_fit_data(fitdatamodel_dict) + + +def test_validate_fit_dataframe(fitdatamodel_dict): + fitdatamodel_dict.update(dict(fraction=0.5)) + df = pd.DataFrame([fitdatamodel_dict] * 5) + validate_fit_dataframe(df) + + +def test_select_reference_planes_using_indices(): + data = np.zeros((6, 100, 200)) + indices = [] + np.testing.assert_array_equal( + select_reference_planes_using_indices(data, indices), data + ) + indices = [1] + assert select_reference_planes_using_indices(data, indices).shape == (1, 100, 200) + indices = [1, -1] + assert select_reference_planes_using_indices(data, indices).shape == (2, 100, 200) + + +def test_select_reference_planes_using_indices_min_index_error(): + data = np.zeros((6, 100, 200)) + indices = [-7] + with pytest.raises(IndexError): + select_reference_planes_using_indices(data, indices) + + +def test_select_reference_planes_using_indices_max_index_error(): + data = np.zeros((6, 100, 200)) + indices = [6] + with pytest.raises(IndexError): + select_reference_planes_using_indices(data, indices) + + +def test_slice_peaks_from_data_using_mask(): + data = np.array( + [ + np.array( + [ + [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 1, 1, 0, 0, 0, 0], + [0, 0, 0, 1, 2, 2, 1, 0, 0, 0], + [0, 0, 1, 2, 3, 3, 2, 1, 0, 0], + [0, 1, 2, 3, 4, 4, 3, 2, 1, 0], + [1, 2, 3, 4, 5, 5, 4, 3, 2, 1], + [0, 1, 2, 3, 4, 4, 3, 2, 1, 0], + [0, 0, 1, 2, 3, 3, 2, 1, 0, 0], + [0, 0, 0, 1, 2, 2, 1, 0, 0, 0], + [0, 0, 0, 0, 1, 1, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], + ] + ) + for i in range(5) + ] + ) + mask = data[0] > 0 + assert data.shape == (5, 11, 10) + assert mask.shape == (11, 10) + peak_slices = slice_peaks_from_data_using_mask(data, mask) + # array is flattened by application of mask + assert peak_slices.shape == (5, 50) + + +def test_select_planes_above_threshold_from_masked_data(): + peak_slices = np.array( + [ + [1, 1, 1, 1, 1, 1], + [2, 2, 2, 2, 2, 2], + [-1, -1, -1, -1, -1, -1], + [-2, -2, -2, -2, -2, -2], + ] + ) + assert peak_slices.shape == (4, 6) + threshold = -1 + assert select_planes_above_threshold_from_masked_data( + peak_slices, threshold + ).shape == ( + 4, + 6, + ) + threshold = 2 + assert_array_equal( + select_planes_above_threshold_from_masked_data(peak_slices, threshold), + peak_slices, + ) + threshold = 1 + assert select_planes_above_threshold_from_masked_data( + peak_slices, threshold + ).shape == (2, 6) + + threshold = None + assert_array_equal( + select_planes_above_threshold_from_masked_data(peak_slices, threshold), + peak_slices, + ) + threshold = 10 + assert_array_equal( + select_planes_above_threshold_from_masked_data(peak_slices, threshold), + peak_slices, + ) + + +def test_make_param_dict(): + selected_planes = [1, 2] + data = np.ones((4, 10, 5)) + expected_shape = (2, 10, 5) + actual_shape = data[np.array(selected_planes)].shape + assert expected_shape == actual_shape + + +def test_make_param_dict_sum(): + data = np.ones((4, 10, 5)) + expected_sum = 200 + actual_sum = data.sum() + assert expected_sum == actual_sum + + +def test_make_param_dict_selected(): + selected_planes = [1, 2] + data = np.ones((4, 10, 5)) + data = data[np.array(selected_planes)] + expected_sum = 100 + actual_sum = data.sum() + assert expected_sum == actual_sum + + +def test_update_params_normal_case(): + params = Parameters() + params.add("center_x", value=0) + params.add("center_y", value=0) + params.add("sigma", value=1) + params.add("gamma", value=1) + params.add("fraction", value=0.5) + + param_dict = { + "center_x": 10, + "center_y": 20, + "sigma": 2, + "gamma": 3, + "fraction": 0.8, + } + + xy_bounds = (5, 5) + + update_params(params, param_dict, Lineshape.PV, xy_bounds) + + assert params["center_x"].value == 10 + assert params["center_y"].value == 20 + assert params["sigma"].value == 2 + assert params["gamma"].value == 3 + assert params["fraction"].value == 0.8 + assert params["center_x"].min == 5 + assert params["center_x"].max == 15 + assert params["center_y"].min == 15 + assert params["center_y"].max == 25 + assert params["sigma"].min == 0.0 + assert params["sigma"].max == 1e4 + assert params["gamma"].min == 0.0 + assert params["gamma"].max == 1e4 + assert params["fraction"].min == 0.0 + assert params["fraction"].max == 1.0 + assert params["fraction"].vary is True + + +def test_update_params_lineshape_G(): + params = Parameters() + params.add("fraction", value=0.5) + + param_dict = {"fraction": 0.7} + + update_params(params, param_dict, Lineshape.G) + + assert params["fraction"].value == 0.7 + assert params["fraction"].min == 0.0 + assert params["fraction"].max == 1.0 + assert params["fraction"].vary is False + + +def test_update_params_lineshape_L(): + params = Parameters() + params.add("fraction", value=0.5) + + param_dict = {"fraction": 0.7} + + update_params(params, param_dict, Lineshape.L) + + assert params["fraction"].value == 0.7 + assert params["fraction"].min == 0.0 + assert params["fraction"].max == 1.0 + assert params["fraction"].vary is False + + +def test_update_params_lineshape_PV_PV(): + params = Parameters() + params.add("fraction", value=0.5) + + param_dict = {"fraction": 0.7} + + update_params(params, param_dict, Lineshape.PV_PV) + + assert params["fraction"].value == 0.7 + assert params["fraction"].min == 0.0 + assert params["fraction"].max == 1.0 + assert params["fraction"].vary is True + + +def test_update_params_no_bounds(): + params = Parameters() + params.add("center_x", value=0) + params.add("center_y", value=0) + + param_dict = { + "center_x": 10, + "center_y": 20, + } + + update_params(params, param_dict, Lineshape.PV, None) + + assert params["center_x"].value == 10 + assert params["center_y"].value == 20 + assert params["center_x"].min == -np.inf + assert params["center_x"].max == np.inf + assert params["center_y"].min == -np.inf + assert params["center_y"].max == np.inf + + +def test_peak_limits_normal_case(): + peak = pd.DataFrame({"X_AXIS": [5], "Y_AXIS": [5], "XW": [2], "YW": [2]}).iloc[0] + data = np.zeros((10, 10)) + pl = PeakLimits(peak, data) + assert pl.min_x == 3 + assert pl.max_x == 8 + assert pl.min_y == 3 + assert pl.max_y == 8 + + +def test_peak_limits_at_edge(): + peak = pd.DataFrame({"X_AXIS": [1], "Y_AXIS": [1], "XW": [2], "YW": [2]}).iloc[0] + data = np.zeros((10, 10)) + pl = PeakLimits(peak, data) + assert pl.min_x == 0 + assert pl.max_x == 4 + assert pl.min_y == 0 + assert pl.max_y == 4 + + +def test_peak_limits_exceeding_bounds(): + peak = pd.DataFrame({"X_AXIS": [9], "Y_AXIS": [9], "XW": [2], "YW": [2]}).iloc[0] + data = np.zeros((10, 10)) + pl = PeakLimits(peak, data) + assert pl.min_x == 7 + assert pl.max_x == 10 + assert pl.min_y == 7 + assert pl.max_y == 10 + + +def test_peak_limits_small_data(): + peak = pd.DataFrame({"X_AXIS": [2], "Y_AXIS": [2], "XW": [5], "YW": [5]}).iloc[0] + data = np.zeros((5, 5)) + pl = PeakLimits(peak, data) + assert pl.min_x == 0 + assert pl.max_x == 5 + assert pl.min_y == 0 + assert pl.max_y == 5 + + +def test_peak_limits_assertion_error(): + peak = pd.DataFrame({"X_AXIS": [11], "Y_AXIS": [11], "XW": [2], "YW": [2]}).iloc[0] + data = np.zeros((10, 10)) + with pytest.raises(AssertionError): + pl = PeakLimits(peak, data) + + +def test_estimate_amplitude(): + peak = namedtuple("peak", ["X_AXIS", "XW", "Y_AXIS", "YW"]) + p = peak(5, 2, 3, 2) + data = np.ones((20, 10)) + expected_result = 25 + actual_result = estimate_amplitude(p, data) + assert expected_result == actual_result + + +def test_estimate_amplitude_invalid_indices(): + peak = namedtuple("peak", ["X_AXIS", "XW", "Y_AXIS", "YW"]) + p = peak(1, 2, 3, 2) + data = np.ones((20, 10)) + expected_result = 20 + actual_result = estimate_amplitude(p, data) + assert expected_result == actual_result + + +def test_make_mask_from_peak_cluster(): + data = np.ones((10, 10)) + group = pd.DataFrame( + {"X_AXISf": [3, 6], "Y_AXISf": [3, 6], "X_RADIUS": [2, 3], "Y_RADIUS": [2, 3]} + ) + mask, peak = make_mask_from_peak_cluster(group, data) + expected_mask = np.array( + [ + [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], + [0, 0, 0, 1, 0, 0, 0, 0, 0, 0], + [0, 0, 1, 1, 1, 0, 0, 0, 0, 0], + [0, 1, 1, 1, 1, 1, 1, 0, 0, 0], + [0, 0, 1, 1, 1, 1, 1, 1, 1, 0], + [0, 0, 0, 1, 1, 1, 1, 1, 1, 0], + [0, 0, 0, 1, 1, 1, 1, 1, 1, 1], + [0, 0, 0, 0, 1, 1, 1, 1, 1, 0], + [0, 0, 0, 0, 1, 1, 1, 1, 1, 0], + [0, 0, 0, 0, 0, 0, 1, 0, 0, 0], + ], + dtype=bool, + ) + assert_array_equal(expected_mask, mask) + + +# get_limits_for_axis_in_points +def test_positive_points(): + group_axis_points = np.array([1, 2, 3, 4, 5]) + mask_radius_in_points = 2 + expected = (8, -1) # ceil(5+1+1), floor(1-1) + assert ( + get_limits_for_axis_in_points(group_axis_points, mask_radius_in_points) + == expected + ) + + +def test_single_point(): + group_axis_points = np.array([5]) + mask_radius_in_points = 3 + expected = (9, 2) + assert ( + get_limits_for_axis_in_points(group_axis_points, mask_radius_in_points) + == expected + ) + + +def test_no_radius(): + group_axis_points = np.array([1, 2, 3]) + mask_radius_in_points = 0 + expected = (4, 1) + assert ( + get_limits_for_axis_in_points(group_axis_points, mask_radius_in_points) + == expected + ) + + +# deal_with_peaks_on_edge_of_spectrum +def test_min_y_less_than_zero(): + assert deal_with_peaks_on_edge_of_spectrum((100, 200), 50, 30, 10, -10) == ( + 50, + 30, + 10, + 0, + ) + + +def test_min_x_less_than_zero(): + assert deal_with_peaks_on_edge_of_spectrum((100, 200), 50, -5, 70, 20) == ( + 50, + 0, + 70, + 20, + ) + + +def test_max_y_exceeds_data_shape(): + assert deal_with_peaks_on_edge_of_spectrum((100, 200), 50, 30, 110, 20) == ( + 50, + 30, + 100, + 20, + ) + + +def test_max_x_exceeds_data_shape(): + assert deal_with_peaks_on_edge_of_spectrum((100, 200), 250, 30, 70, 20) == ( + 200, + 30, + 70, + 20, + ) + + +def test_values_within_range(): + assert deal_with_peaks_on_edge_of_spectrum((100, 200), 50, 30, 70, 20) == ( + 50, + 30, + 70, + 20, + ) + + +def test_all_edge_cases(): + assert deal_with_peaks_on_edge_of_spectrum((100, 200), 250, -5, 110, -10) == ( + 200, + 0, + 100, + 0, + ) + + +def test_make_meshgrid(): + data_shape = (4, 5) + expected_x = np.array( + [[0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4]] + ) + expected_y = np.array( + [[0, 0, 0, 0, 0], [1, 1, 1, 1, 1], [2, 2, 2, 2, 2], [3, 3, 3, 3, 3]] + ) + XY = make_meshgrid(data_shape) + np.testing.assert_array_equal(XY[0], expected_x) + np.testing.assert_array_equal(XY[1], expected_y) + + +class TestCoreFunctions(unittest.TestCase): + test_directory = Path(__file__).parent + test_directory = "./test" + + def test_make_mask(self): + data = np.ones((10, 10)) + c_x = 5 + c_y = 5 + r_x = 3 + r_y = 2 + + expected_result = np.array( + [ + [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0], + [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0], + [0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0], + [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0], + [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0], + [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + ] + ) + + result = np.array(make_mask(data, c_x, c_y, r_x, r_y), dtype=int) + test = result - expected_result + # print(test) + # print(test.sum()) + # print(result) + self.assertEqual(test.sum(), 0) + + def test_make_mask_2(self): + data = np.ones((10, 10)) + c_x = 5 + c_y = 8 + r_x = 3 + r_y = 2 + + expected_result = np.array( + [ + [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0], + [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0], + [0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0], + [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0], + ] + ) + + result = np.array(make_mask(data, c_x, c_y, r_x, r_y), dtype=int) + test = result - expected_result + # print(test) + # print(test.sum()) + # print(result) + self.assertEqual(test.sum(), 0) + + def test_fix_params(self): + mod = Model(pvoigt2d) + pars = mod.make_params() + to_fix = ["center", "sigma", "fraction"] + fix_params(pars, to_fix) + + self.assertEqual(pars["center_x"].vary, False) + self.assertEqual(pars["center_y"].vary, False) + self.assertEqual(pars["sigma_x"].vary, False) + self.assertEqual(pars["sigma_y"].vary, False) + self.assertEqual(pars["fraction"].vary, False) + + def test_get_params(self): + mod = Model(pvoigt2d, prefix="p1_") + pars = mod.make_params(p1_center_x=20.0, p1_center_y=30.0) + pars["p1_center_x"].stderr = 1.0 + pars["p1_center_y"].stderr = 2.0 + ps, ps_err, names, prefixes = get_params(pars, "center") + #  get index of values + cen_x = names.index("p1_center_x") + cen_y = names.index("p1_center_y") + + self.assertEqual(ps[cen_x], 20.0) + self.assertEqual(ps[cen_y], 30.0) + self.assertEqual(ps_err[cen_x], 1.0) + self.assertEqual(ps_err[cen_y], 2.0) + self.assertEqual(prefixes[cen_y], "p1_") + + def test_make_param_dict(self): + peaks = pd.DataFrame( + { + "ASS": ["one", "two", "three"], + "X_AXISf": [5.0, 10.0, 15.0], + "X_AXIS": [5, 10, 15], + "Y_AXISf": [15.0, 10.0, 5.0], + "Y_AXIS": [15, 10, 5], + "XW": [2.5, 2.5, 2.5], + "YW": [2.5, 2.5, 2.5], + } + ) + data = np.ones((20, 20)) + + for ls, frac in zip([Lineshape.PV, Lineshape.G, Lineshape.L], [0.5, 0.0, 1.0]): + params = make_param_dict(peaks, data, ls) + self.assertEqual(params["_one_fraction"], frac) + self.assertEqual(params["_two_fraction"], frac) + self.assertEqual(params["_three_fraction"], frac) + + self.assertEqual(params["_one_center_x"], 5.0) + self.assertEqual(params["_two_center_x"], 10.0) + self.assertEqual(params["_two_sigma_x"], 1.25) + self.assertEqual(params["_two_sigma_y"], 1.25) + + voigt_params = make_param_dict(peaks, data, Lineshape.V) + self.assertEqual( + voigt_params["_one_sigma_x"], 2.5 / (2.0 * np.sqrt(2.0 * np.log(2))) + ) + self.assertEqual(voigt_params["_one_gamma_x"], 2.5 / 2.0) + + def test_to_prefix(self): + names = [ + (1, "_1_"), + (1.0, "_1_0_"), + (" one", "_one_"), + (" one/two", "_oneortwo_"), + (" one?two", "_onemaybetwo_"), + (r" [{one?two\}][", "___onemaybetwo____"), + ] + for test, expect in names: + prefix = to_prefix(test) + # print(prefix) + self.assertEqual(prefix, expect) + + def test_make_models(self): + peaks = pd.DataFrame( + { + "ASS": ["one", "two", "three"], + "X_AXISf": [5.0, 10.0, 15.0], + "X_AXIS": [5, 10, 15], + "Y_AXISf": [15.0, 10.0, 5.0], + "Y_AXIS": [15, 10, 5], + "XW": [2.5, 2.5, 2.5], + "YW": [2.5, 2.5, 2.5], + "CLUSTID": [1, 1, 1], + } + ) + + group = peaks.groupby("CLUSTID") + + data = np.ones((20, 20)) + + lineshapes = [Lineshape.PV, Lineshape.L, Lineshape.G, Lineshape.PV_PV] + + for lineshape in lineshapes: + match lineshape: + case lineshape.PV: + mod, p_guess = make_models(pvoigt2d, peaks, data, lineshape) + self.assertEqual(p_guess["_one_fraction"].vary, True) + self.assertEqual(p_guess["_one_fraction"].value, 0.5) + + case lineshape.G: + mod, p_guess = make_models(pvoigt2d, peaks, data, lineshape) + self.assertEqual(p_guess["_one_fraction"].vary, False) + self.assertEqual(p_guess["_one_fraction"].value, 0.0) + + case lineshape.L: + mod, p_guess = make_models(pvoigt2d, peaks, data, lineshape) + self.assertEqual(p_guess["_one_fraction"].vary, False) + self.assertEqual(p_guess["_one_fraction"].value, 1.0) + + case lineshape.PV_PV: + mod, p_guess = make_models(pv_pv, peaks, data, lineshape) + self.assertEqual(p_guess["_one_fraction_x"].vary, True) + self.assertEqual(p_guess["_one_fraction_x"].value, 0.5) + self.assertEqual(p_guess["_one_fraction_y"].vary, True) + self.assertEqual(p_guess["_one_fraction_y"].value, 0.5) + + def test_Pseudo3D(self): + datasets = [ + (f"{self.test_directory}/test_protein_L/test1.ft2", [0, 1, 2]), + (f"{self.test_directory}/test_protein_L/test_tp.ft2", [2, 1, 0]), + (f"{self.test_directory}/test_protein_L/test_tp2.ft2", [1, 2, 0]), + ] + + # expected shape + data_shape = (4, 256, 546) + test_nu = 1 + for dataset, dims in datasets: + with self.subTest(i=test_nu): + dic, data = ng.pipe.read(dataset) + pseudo3D = Pseudo3D(dic, data, dims) + self.assertEqual(dims, pseudo3D.dims) + self.assertEqual(pseudo3D.data.shape, data_shape) + self.assertEqual(pseudo3D.f1_label, "15N") + self.assertEqual(pseudo3D.f2_label, "HN") + self.assertEqual(pseudo3D.dims, dims) + self.assertEqual(pseudo3D.f1_size, 256) + self.assertEqual(pseudo3D.f2_size, 546) + test_nu += 1 diff --git a/test/test_io.py b/test/test_io.py new file mode 100644 index 00000000..a945852d --- /dev/null +++ b/test/test_io.py @@ -0,0 +1,428 @@ +import unittest +from unittest.mock import patch +from pathlib import Path +import json + +import pytest +import numpy as np +import nmrglue as ng +import pandas as pd + +from peakipy.io import ( + Pseudo3D, + Peaklist, + LoadData, + PeaklistFormat, + OutFmt, + StrucEl, + UnknownFormat, + ClustersResult, + get_vclist, +) +from peakipy.fitting import PeakLimits +from peakipy.utils import load_config, write_config, update_config_file + + +@pytest.fixture +def test_directory(): + return Path(__file__).parent + + +# test for read, edit, fit, check and spec scripts +# need to actually write proper tests +class TestBokehScript(unittest.TestCase): + @patch("peakipy.cli.edit.BokehScript") + def test_BokehScript(self, MockBokehScript): + args = {"": "hello", "": "data"} + bokeh_plots = MockBokehScript(args) + self.assertIsNotNone(bokeh_plots) + + +class TestCheckScript(unittest.TestCase): + @patch("peakipy.cli.main.check") + def test_main(self, MockCheck): + args = {"": "hello", "": "data"} + check = MockCheck(args) + self.assertIsNotNone(check) + + +class TestFitScript(unittest.TestCase): + @patch("peakipy.cli.main.fit") + def test_main(self, MockFit): + args = {"": "hello", "": "data"} + fit = MockFit(args) + self.assertIsNotNone(fit) + + +class TestReadScript(unittest.TestCase): + test_directory = "./test/" + + @patch("peakipy.cli.main.read") + def test_main(self, MockRead): + args = {"": "hello", "": "data"} + read = MockRead(args) + self.assertIsNotNone(read) + + def test_read_pipe_peaklist(self): + args = { + "path": f"{self.test_directory}/test_pipe.tab", + "data_path": f"{self.test_directory}/test_pipe.ft2", + "dims": [0, 1, 2], + "fmt": PeaklistFormat.pipe, + } + peaklist = Peaklist(**args) + self.assertIsNotNone(peaklist) + self.assertIs(len(peaklist.df), 3) + # self.assertIs(peaklist.df.X_AXISf.iloc[0], 323.019) + self.assertIs(peaklist.fmt.value, "pipe") + # self.assertEqual(peaklist.df.ASS.iloc[0], "None") + # self.assertEqual(peaklist.df.ASS.iloc[1], "None_dummy_1") + + +class TestSpecScript(unittest.TestCase): + @patch("peakipy.cli.main.spec") + def test_main(self, MockSpec): + args = {"": "hello", "": "data"} + spec = MockSpec(args) + self.assertIsNotNone(spec) + + +def test_load_config_existing(): + config_path = Path("test_config.json") + # Create a dummy existing config file + with open(config_path, "w") as f: + json.dump({"key1": "value1"}, f) + + loaded_config = load_config(config_path) + + assert loaded_config == {"key1": "value1"} + + # Clean up + config_path.unlink() + + +def test_load_config_nonexistent(): + config_path = Path("test_config.json") + + loaded_config = load_config(config_path) + + assert loaded_config == {} + + +def test_write_config(): + config_path = Path("test_config.json") + config_kvs = {"key1": "value1", "key2": "value2"} + + write_config(config_path, config_kvs) + + # Check if the config file is created correctly + assert config_path.exists() + + # Check if the config file content is correct + with open(config_path) as f: + created_config = json.load(f) + assert created_config == {"key1": "value1", "key2": "value2"} + + # Clean up + config_path.unlink() + + +def test_update_config_file_existing(): + config_path = Path("test_config.json") + # Create a dummy existing config file + with open(config_path, "w") as f: + json.dump({"key1": "value1"}, f) + + config_kvs = {"key2": "value2", "key3": "value3"} + updated_config = update_config_file(config_path, config_kvs) + + assert updated_config == {"key1": "value1", "key2": "value2", "key3": "value3"} + + # Clean up + config_path.unlink() + + +def test_update_config_file_nonexistent(): + config_path = Path("test_config.json") + config_kvs = {"key1": "value1", "key2": "value2"} + updated_config = update_config_file(config_path, config_kvs) + + assert updated_config == {"key1": "value1", "key2": "value2"} + + # Clean up + config_path.unlink() + + +@pytest.fixture +def sample_data(): + return np.zeros((10, 10)) + + +@pytest.fixture +def sample_peak(): + peak_data = {"X_AXIS": [5], "Y_AXIS": [5], "XW": [2], "YW": [2]} + return pd.DataFrame(peak_data).iloc[0] + + +def test_peak_limits_max_min(sample_peak, sample_data): + limits = PeakLimits(sample_peak, sample_data) + + assert limits.max_x == 8 + assert limits.max_y == 8 + assert limits.min_x == 3 + assert limits.min_y == 3 + + +def test_peak_limits_boundary(sample_data): + peak_data = {"X_AXIS": [8], "Y_AXIS": [8], "XW": [2], "YW": [2]} + peak = pd.DataFrame(peak_data).iloc[0] + limits = PeakLimits(peak, sample_data) + + assert limits.max_x == 10 + assert limits.max_y == 10 + assert limits.min_x == 6 + assert limits.min_y == 6 + + +def test_peak_limits_at_boundary(sample_data): + peak_data = {"X_AXIS": [0], "Y_AXIS": [0], "XW": [2], "YW": [2]} + peak = pd.DataFrame(peak_data).iloc[0] + limits = PeakLimits(peak, sample_data) + + assert limits.max_x == 3 + assert limits.max_y == 3 + assert limits.min_x == 0 + assert limits.min_y == 0 + + +def test_peak_limits_outside_boundary(sample_data): + peak_data = {"X_AXIS": [15], "Y_AXIS": [15], "XW": [2], "YW": [2]} + peak = pd.DataFrame(peak_data).iloc[0] + with pytest.raises(AssertionError): + limits = PeakLimits(peak, sample_data) + + +def test_peak_limits_1d_data(): + data = np.zeros(10) + peak_data = {"X_AXIS": [5], "Y_AXIS": [0], "XW": [2], "YW": [0]} + peak = pd.DataFrame(peak_data).iloc[0] + with pytest.raises(IndexError): + limits = PeakLimits(peak, data) + + +def test_StrucEl(): + assert StrucEl.square.value == "square" + assert StrucEl.disk.value == "disk" + assert StrucEl.rectangle.value == "rectangle" + assert StrucEl.mask_method.value == "mask_method" + + +def test_PeaklistFormat(): + assert PeaklistFormat.a2.value == "a2" + assert PeaklistFormat.a3.value == "a3" + assert PeaklistFormat.sparky.value == "sparky" + assert PeaklistFormat.pipe.value == "pipe" + assert PeaklistFormat.peakipy.value == "peakipy" + + +def test_OutFmt(): + assert OutFmt.csv.value == "csv" + assert OutFmt.pkl.value == "pkl" + + +@pytest.fixture +def test_data_path(): + return Path("./test/test_protein_L") + + +@pytest.fixture +def pseudo3d_args(test_data_path): + dic, data = ng.pipe.read(test_data_path / "test1.ft2") + dims = [0, 1, 2] + return dic, data, dims + + +@pytest.fixture +def peaklist(test_data_path): + dims = [0, 1, 2] + path = test_data_path / "test.tab" + data_path = test_data_path / "test1.ft2" + fmt = PeaklistFormat.pipe + radii = [0.04, 0.4] + return Peaklist(path, data_path, fmt, dims, radii) + + +def test_Pseudo3D_properties(pseudo3d_args): + dic, data, dims = pseudo3d_args + pseudo3d = Pseudo3D(dic, data, dims) + assert pseudo3d.dic == dic + assert np.array_equal(pseudo3d._data, data.reshape((4, 256, 546))) + assert pseudo3d.dims == dims + + +def test_Peaklist_initialization(test_data_path, peaklist): + + assert peaklist.peaklist_path == test_data_path / "test.tab" + assert peaklist.data_path == test_data_path / "test1.ft2" + assert peaklist.fmt == PeaklistFormat.pipe + assert peaklist.radii == [0.04, 0.4] + + +def test_Peaklist_a2(test_data_path): + dims = [0, 1, 2] + path = test_data_path / "peaks.a2" + data_path = test_data_path / "test1.ft2" + fmt = PeaklistFormat.a2 + radii = [0.04, 0.4] + peaklist = Peaklist(path, data_path, fmt, dims, radii) + peaklist.update_df() + + +def test_Peaklist_a3(test_data_path): + dims = [0, 1, 2] + path = test_data_path / "ccpnTable.tsv" + data_path = test_data_path / "test1.ft2" + fmt = PeaklistFormat.a3 + radii = [0.04, 0.4] + peaklist = Peaklist(path, data_path, fmt, dims, radii) + peaklist.update_df() + + +def test_Peaklist_sparky(test_data_path): + dims = [0, 1, 2] + path = test_data_path / "peaks.sparky" + data_path = test_data_path / "test1.ft2" + fmt = PeaklistFormat.sparky + radii = [0.04, 0.4] + Peaklist(path, data_path, fmt, dims, radii) + + +@pytest.fixture +def loaddata(test_data_path): + dims = [0, 1, 2] + path = test_data_path / "test.csv" + data_path = test_data_path / "test1.ft2" + fmt = PeaklistFormat.peakipy + radii = [0.04, 0.4] + return LoadData(path, data_path, fmt, dims, radii) + + +def test_LoadData_initialization(test_data_path, loaddata): + assert loaddata.peaklist_path == test_data_path / "test.csv" + assert loaddata.data_path == test_data_path / "test1.ft2" + assert loaddata.fmt == PeaklistFormat.peakipy + assert loaddata.radii == [0.04, 0.4] + loaddata.check_data_frame() + loaddata.check_assignments() + loaddata.check_peak_bounds() + loaddata.update_df() + + +def test_LoadData_with_Edited_column(loaddata): + loaddata.df["Edited"] = "yes" + loaddata.check_data_frame() + + +def test_LoadData_without_include_column(loaddata): + loaddata.df.drop(columns=["include"], inplace=True) + loaddata.check_data_frame() + assert "include" in loaddata.df.columns + assert np.all(loaddata.df.include == "yes") + + +def test_LoadData_with_X_DIAMETER_PPM_column(loaddata): + loaddata.df["X_DIAMETER_PPM"] = 0.04 + loaddata.check_data_frame() + assert "X_DIAMETER_PPM" in loaddata.df.columns + + +def test_UnknownFormat(): + with pytest.raises(UnknownFormat): + raise UnknownFormat("This is an unknown format") + + +def test_update_df(peaklist): + peaklist.update_df() + + df = peaklist.df + + # Check that X_AXIS and Y_AXIS columns are created and values are set correctly + assert "X_AXIS" in df.columns + assert "Y_AXIS" in df.columns + + # Check that X_AXISf and Y_AXISf columns are created and values are set correctly + assert "X_AXISf" in df.columns + assert "Y_AXISf" in df.columns + + # Check that XW_HZ and YW_HZ columns are converted to float correctly + assert df["XW_HZ"].dtype == float + assert df["YW_HZ"].dtype == float + + # Check that XW and YW columns are created + assert "XW" in df.columns + assert "YW" in df.columns + + # Check the assignment column + assert "ASS" in df.columns + + # Check radii columns + assert "X_RADIUS_PPM" in df.columns + assert "Y_RADIUS_PPM" in df.columns + assert "X_RADIUS" in df.columns + assert "Y_RADIUS" in df.columns + + # Check 'include' column is created and set to 'yes' + assert "include" in df.columns + assert all(df["include"] == "yes") + + # Check that the peaks are within bounds + assert all( + (df["X_PPM"] < peaklist.f2_ppm_max) & (df["X_PPM"] > peaklist.f2_ppm_min) + ) + assert all( + (df["Y_PPM"] < peaklist.f1_ppm_max) & (df["Y_PPM"] > peaklist.f1_ppm_min) + ) + + +def test_update_df_with_excluded_peaks(peaklist): + peaklist._df.loc[1, "X_PPM"] = 100.0 # This peak should be out of bounds + peaklist.update_df() + + df = peaklist.df + + # Check that out of bounds peak is excluded + assert len(df) == 62 + assert not ((df["X_PPM"] == 100.0).any()) + + +def test_clusters_result_initialization(): + labeled_array = np.array([[1, 2], [3, 4]]) + num_features = 5 + closed_data = np.array([[5, 6], [7, 8]]) + peaks = [(1, 2), (3, 4)] + + clusters_result = ClustersResult(labeled_array, num_features, closed_data, peaks) + + assert np.array_equal(clusters_result.labeled_array, labeled_array) + assert clusters_result.num_features == num_features + assert np.array_equal(clusters_result.closed_data, closed_data) + assert clusters_result.peaks == peaks + + +def test_get_vclist_None(): + assert get_vclist(None, {})["vclist"] == False + + +def test_get_vclist_exists(test_data_path): + vclist = test_data_path / "vclist" + assert get_vclist(vclist, {})["vclist"] == True + + +def test_get_vclist_not_exists(test_data_path): + vclist = test_data_path / "vclistbla" + with pytest.raises(Exception): + get_vclist(vclist, {})["vclist"] == True + + +if __name__ == "__main__": + unittest.main(verbosity=2) diff --git a/test/test_lineshapes.py b/test/test_lineshapes.py new file mode 100644 index 00000000..43e867c9 --- /dev/null +++ b/test/test_lineshapes.py @@ -0,0 +1,353 @@ +from pathlib import Path +from unittest.mock import Mock + +import pytest +import pandas as pd +import numpy as np +from numpy.testing import assert_almost_equal + +from peakipy.io import Peaklist, PeaklistFormat +from peakipy.constants import tiny +from peakipy.lineshapes import ( + gaussian, + gaussian_lorentzian, + pv_g, + pv_l, + voigt2d, + pvoigt2d, + pv_pv, + get_lineshape_function, + Lineshape, + calculate_height_for_voigt_lineshape, + calculate_fwhm_for_voigt_lineshape, + calculate_fwhm_for_pseudo_voigt_lineshape, + calculate_height_for_pseudo_voigt_lineshape, + calculate_height_for_gaussian_lineshape, + calculate_height_for_lorentzian_lineshape, + calculate_height_for_pv_pv_lineshape, + calculate_lineshape_specific_height_and_fwhm, + calculate_peak_linewidths_in_hz, + calculate_peak_centers_in_ppm, +) + + +def test_gaussian_typical_values(): + x = np.array([0, 1, 2]) + center = 0.0 + sigma = 1.0 + expected = (1.0 / (np.sqrt(2 * np.pi) * sigma)) * np.exp( + -((x - center) ** 2) / (2 * sigma**2) + ) + result = gaussian(x, center, sigma) + assert_almost_equal(result, expected, decimal=7) + + +def test_gaussian_center_nonzero(): + x = np.array([0, 1, 2]) + center = 1.0 + sigma = 1.0 + expected = (1.0 / (np.sqrt(2 * np.pi) * sigma)) * np.exp( + -((x - center) ** 2) / (2 * sigma**2) + ) + result = gaussian(x, center, sigma) + assert_almost_equal(result, expected, decimal=7) + + +def test_gaussian_sigma_nonzero(): + x = np.array([0, 1, 2]) + center = 0.0 + sigma = 2.0 + expected = (1.0 / (np.sqrt(2 * np.pi) * sigma)) * np.exp( + -((x - center) ** 2) / (2 * sigma**2) + ) + result = gaussian(x, center, sigma) + assert_almost_equal(result, expected, decimal=7) + + +def test_gaussian_zero_center(): + x = np.array([0, 1, 2]) + center = 0.0 + sigma = 1.0 + expected = (1.0 / (np.sqrt(2 * np.pi) * sigma)) * np.exp( + -((x - center) ** 2) / (2 * sigma**2) + ) + result = gaussian(x, center, sigma) + assert_almost_equal(result, expected, decimal=7) + + +def test_calculate_height_for_voigt_lineshape(): + data = { + "sigma_x": [1.0, 2.0], + "sigma_y": [1.0, 2.0], + "gamma_x": [1.0, 2.0], + "gamma_y": [1.0, 2.0], + "amp": [10.0, 20.0], + "amp_err": [1.0, 2.0], + } + df = pd.DataFrame(data) + result_df = calculate_height_for_voigt_lineshape(df) + + assert np.allclose(result_df["height"], [0.435596, 0.217798]) + assert np.allclose(result_df["height_err"], [0.04356, 0.02178]) + + +def test_calculate_fwhm_for_voigt_lineshape(): + data = { + "sigma_x": [1.0, 2.0], + "sigma_y": [1.0, 2.0], + "gamma_x": [1.0, 2.0], + "gamma_y": [1.0, 2.0], + "amp": [10.0, 20.0], + "amp_err": [1.0, 2.0], + } + df = pd.DataFrame(data) + result_df = calculate_fwhm_for_voigt_lineshape(df) + + assert np.allclose(result_df["fwhm_l_x"], [2.0, 4.0]) + assert np.allclose(result_df["fwhm_l_y"], [2.0, 4.0]) + assert np.allclose(result_df["fwhm_g_x"], [2.35482, 4.70964]) + assert np.allclose(result_df["fwhm_g_y"], [2.35482, 4.70964]) + assert np.allclose(result_df["fwhm_x"], [3.601309, 7.202619]) + assert np.allclose(result_df["fwhm_y"], [3.601309, 7.202619]) + + +def test_calculate_height_for_pseudo_voigt_lineshape(): + data = { + "sigma_x": [1.0, 2.0], + "sigma_y": [1.0, 2.0], + "gamma_x": [1.0, 2.0], + "gamma_y": [1.0, 2.0], + "amp": [10.0, 20.0], + "amp_err": [1.0, 2.0], + "fraction": [0.5, 0.5], + } + df = pd.DataFrame(data) + result_df = calculate_height_for_pseudo_voigt_lineshape(df) + + assert np.allclose(result_df["height"], [1.552472, 0.776236]) + assert np.allclose(result_df["height_err"], [0.155247, 0.077624]) + + +def test_calculate_fwhm_for_pseudo_voigt_lineshape(): + data = { + "sigma_x": [1.0, 2.0], + "sigma_y": [1.0, 2.0], + "gamma_x": [1.0, 2.0], + "gamma_y": [1.0, 2.0], + "amp": [10.0, 20.0], + "amp_err": [1.0, 2.0], + "fraction": [0.5, 0.5], + } + df = pd.DataFrame(data) + result_df = calculate_fwhm_for_pseudo_voigt_lineshape(df) + + assert np.allclose(result_df["fwhm_x"], [2.0, 4.0]) + assert np.allclose(result_df["fwhm_y"], [2.0, 4.0]) + + +def test_calculate_height_for_gaussian_lineshape(): + data = { + "sigma_x": [1.0, 2.0], + "sigma_y": [1.0, 2.0], + "gamma_x": [1.0, 2.0], + "gamma_y": [1.0, 2.0], + "amp": [10.0, 20.0], + "amp_err": [1.0, 2.0], + "fraction": [0.5, 0.5], + } + df = pd.DataFrame(data) + result_df = calculate_height_for_gaussian_lineshape(df) + + assert np.allclose(result_df["height"], [2.206356, 1.103178]) + assert np.allclose(result_df["height_err"], [0.220636, 0.110318]) + + +def test_calculate_height_for_lorentzian_lineshape(): + data = { + "sigma_x": [1.0, 2.0], + "sigma_y": [1.0, 2.0], + "gamma_x": [1.0, 2.0], + "gamma_y": [1.0, 2.0], + "amp": [10.0, 20.0], + "amp_err": [1.0, 2.0], + "fraction": [0.5, 0.5], + } + df = pd.DataFrame(data) + result_df = calculate_height_for_lorentzian_lineshape(df) + + assert np.allclose(result_df["height"], [1.013212, 0.506606]) + assert np.allclose(result_df["height_err"], [0.101321, 0.050661]) + + +def test_calculate_height_for_pv_pv_lineshape(): + data = { + "sigma_x": [1.0, 2.0], + "sigma_y": [1.0, 2.0], + "gamma_x": [1.0, 2.0], + "gamma_y": [1.0, 2.0], + "amp": [10.0, 20.0], + "amp_err": [1.0, 2.0], + "fraction_x": [0.5, 0.5], + "fraction_y": [0.5, 0.5], + } + df = pd.DataFrame(data) + result_df = calculate_height_for_pv_pv_lineshape(df) + + assert np.allclose(result_df["height"], [1.552472, 0.776236]) + assert np.allclose(result_df["height_err"], [0.155247, 0.077624]) + + +def test_calculate_height_for_pv_pv_lineshape_fraction_y(): + data = { + "sigma_x": [1.0, 2.0], + "sigma_y": [1.0, 2.0], + "gamma_x": [1.0, 2.0], + "gamma_y": [1.0, 2.0], + "amp": [10.0, 20.0], + "amp_err": [1.0, 2.0], + "fraction_x": [0.5, 0.5], + "fraction_y": [1.0, 1.0], + } + df = pd.DataFrame(data) + result_df = calculate_height_for_pv_pv_lineshape(df) + + assert np.allclose(result_df["height"], [1.254186, 0.627093]) + assert np.allclose(result_df["height_err"], [0.125419, 0.062709]) + + +def test_calculate_lineshape_specific_height_and_fwhm(): + data = { + "sigma_x": [1.0, 2.0], + "sigma_y": [1.0, 2.0], + "gamma_x": [1.0, 2.0], + "gamma_y": [1.0, 2.0], + "amp": [10.0, 20.0], + "amp_err": [1.0, 2.0], + "fraction": [0.5, 0.5], + "fraction_x": [0.5, 0.5], + "fraction_y": [0.5, 0.5], + } + df = pd.DataFrame(data) + calculate_lineshape_specific_height_and_fwhm(Lineshape.G, df) + calculate_lineshape_specific_height_and_fwhm(Lineshape.L, df) + calculate_lineshape_specific_height_and_fwhm(Lineshape.V, df) + calculate_lineshape_specific_height_and_fwhm(Lineshape.PV, df) + calculate_lineshape_specific_height_and_fwhm(Lineshape.PV_PV, df) + calculate_lineshape_specific_height_and_fwhm(Lineshape.PV_G, df) + calculate_lineshape_specific_height_and_fwhm(Lineshape.PV_L, df) + + +def test_get_lineshape_function(): + assert get_lineshape_function(Lineshape.PV) == pvoigt2d + assert get_lineshape_function(Lineshape.L) == pvoigt2d + assert get_lineshape_function(Lineshape.G) == pvoigt2d + assert get_lineshape_function(Lineshape.G_L) == gaussian_lorentzian + assert get_lineshape_function(Lineshape.PV_G) == pv_g + assert get_lineshape_function(Lineshape.PV_L) == pv_l + assert get_lineshape_function(Lineshape.PV_PV) == pv_pv + assert get_lineshape_function(Lineshape.V) == voigt2d + + +def test_get_lineshape_function_exception(): + with pytest.raises(Exception): + get_lineshape_function("bla") + + +@pytest.fixture +def peakipy_data(): + test_data_path = Path("./test/test_protein_L/") + return Peaklist( + test_data_path / "test.tab", test_data_path / "test1.ft2", PeaklistFormat.pipe + ) + + +def test_calculate_peak_linewidths_in_hz(): + # Sample data for testing + data = { + "sigma_x": [1.0, 2.0, 3.0], + "sigma_y": [1.5, 2.5, 3.5], + "fwhm_x": [0.5, 1.5, 2.5], + "fwhm_y": [0.7, 1.7, 2.7], + } + df = pd.DataFrame(data) + + # Mock peakipy_data object + peakipy_data = Mock() + peakipy_data.ppm_per_pt_f2 = 0.01 + peakipy_data.ppm_per_pt_f1 = 0.02 + peakipy_data.hz_per_pt_f2 = 10.0 + peakipy_data.hz_per_pt_f1 = 20.0 + + # Expected results + expected_sigma_x_ppm = [0.01, 0.02, 0.03] + expected_sigma_y_ppm = [0.03, 0.05, 0.07] + expected_fwhm_x_ppm = [0.005, 0.015, 0.025] + expected_fwhm_y_ppm = [0.014, 0.034, 0.054] + expected_fwhm_x_hz = [5.0, 15.0, 25.0] + expected_fwhm_y_hz = [14.0, 34.0, 54.0] + + # Run the function + result_df = calculate_peak_linewidths_in_hz(df, peakipy_data) + + # Assertions + pd.testing.assert_series_equal( + result_df["sigma_x_ppm"], pd.Series(expected_sigma_x_ppm), check_names=False + ) + pd.testing.assert_series_equal( + result_df["sigma_y_ppm"], pd.Series(expected_sigma_y_ppm), check_names=False + ) + pd.testing.assert_series_equal( + result_df["fwhm_x_ppm"], pd.Series(expected_fwhm_x_ppm), check_names=False + ) + pd.testing.assert_series_equal( + result_df["fwhm_y_ppm"], pd.Series(expected_fwhm_y_ppm), check_names=False + ) + pd.testing.assert_series_equal( + result_df["fwhm_x_hz"], pd.Series(expected_fwhm_x_hz), check_names=False + ) + pd.testing.assert_series_equal( + result_df["fwhm_y_hz"], pd.Series(expected_fwhm_y_hz), check_names=False + ) + + +def test_calculate_peak_centers_in_ppm(): + # Sample data for testing + data = { + "center_x": [10, 20, 30], + "center_y": [15, 25, 35], + "init_center_x": [12, 22, 32], + "init_center_y": [18, 28, 38], + } + df = pd.DataFrame(data) + + # Mock peakipy_data object + peakipy_data = Mock() + peakipy_data.uc_f2.ppm = Mock(side_effect=lambda x: x * 0.1) + peakipy_data.uc_f1.ppm = Mock(side_effect=lambda x: x * 0.2) + + # Expected results + expected_center_x_ppm = [1.0, 2.0, 3.0] + expected_center_y_ppm = [3.0, 5.0, 7.0] + expected_init_center_x_ppm = [1.2, 2.2, 3.2] + expected_init_center_y_ppm = [3.6, 5.6, 7.6] + + # Run the function + result_df = calculate_peak_centers_in_ppm(df, peakipy_data) + + # Assertions + pd.testing.assert_series_equal( + result_df["center_x_ppm"], pd.Series(expected_center_x_ppm), check_names=False + ) + pd.testing.assert_series_equal( + result_df["center_y_ppm"], pd.Series(expected_center_y_ppm), check_names=False + ) + pd.testing.assert_series_equal( + result_df["init_center_x_ppm"], + pd.Series(expected_init_center_x_ppm), + check_names=False, + ) + pd.testing.assert_series_equal( + result_df["init_center_y_ppm"], + pd.Series(expected_init_center_y_ppm), + check_names=False, + ) diff --git a/test/test_utils.py b/test/test_utils.py new file mode 100644 index 00000000..baf9f8da --- /dev/null +++ b/test/test_utils.py @@ -0,0 +1,252 @@ +from unittest.mock import patch, mock_open, MagicMock +from datetime import datetime +import json +import os +import tempfile +from pathlib import Path + +import pytest +import pandas as pd + +# Assuming the run_log function is defined in a module named 'log_module' +from peakipy.utils import ( + run_log, + update_args_with_values_from_config_file, + update_peak_positions_from_ppm_to_points, + update_linewidths_from_hz_to_points, + save_data, +) + + +@patch("peakipy.utils.open", new_callable=mock_open) +@patch("peakipy.utils.datetime") +@patch("peakipy.utils.sys") +def test_run_log(mock_sys, mock_datetime, mock_open_file): + # Mocking sys.argv + mock_sys.argv = ["test_script.py", "arg1", "arg2"] + + # Mocking datetime to return a fixed timestamp + fixed_timestamp = datetime(2024, 5, 20, 15, 45) + mock_datetime.now.return_value = fixed_timestamp + + # Expected timestamp string + expected_time_stamp = fixed_timestamp.strftime("%A %d %B %Y at %H:%M") + + # Run the function + run_log("mock_run_log.txt") + + # Prepare the expected log content + expected_log_content = ( + f"# Script run on {expected_time_stamp}:\ntest_script.py arg1 arg2\n" + ) + + # Assert that the file was opened correctly + mock_open_file.assert_called_once_with("mock_run_log.txt", "a") + + # Assert that the correct content was written to the file + mock_open_file().write.assert_called_once_with(expected_log_content) + + # Assert that the script name is correctly set to the basename + assert mock_sys.argv[0] == "test_script.py" + + +# Mock configuration loader function (you need to replace 'config_module.load_config' with the actual path if different) +@patch("peakipy.utils.load_config") +@patch("peakipy.utils.Path.exists") +def test_update_args_with_config(mock_path_exists, mock_load_config): + # Test setup + mock_path_exists.return_value = True # Pretend the config file exists + mock_load_config.return_value = { + "dims": [1, 2, 3], + "noise": "0.05", + "colors": ["#ff0000", "#00ff00"], + } + + args = {"dims": (0, 1, 2), "noise": False, "colors": ["#5e3c99", "#e66101"]} + + # Run the function + updated_args, config = update_args_with_values_from_config_file(args) + + # Check the updates to args + assert updated_args["dims"] == [1, 2, 3] + assert updated_args["noise"] == 0.05 + assert updated_args["colors"] == ["#ff0000", "#00ff00"] + + # Check the returned config + assert config == { + "dims": [1, 2, 3], + "noise": "0.05", + "colors": ["#ff0000", "#00ff00"], + } + + +@patch("peakipy.utils.Path.exists") +def test_update_args_with_no_config_file(mock_path_exists): + # Test setup + mock_path_exists.return_value = False # Pretend the config file does not exist + + args = {"dims": (0, 1, 2), "noise": False, "colors": ["#5e3c99", "#e66101"]} + + # Run the function + updated_args, config = update_args_with_values_from_config_file(args) + + # Check the updates to args + assert updated_args["dims"] == (0, 1, 2) + assert updated_args["noise"] == False + assert updated_args["colors"] == ["#5e3c99", "#e66101"] + + # Check the returned config (should be empty) + assert config == {} + + +@patch("peakipy.utils.load_config") +@patch("peakipy.utils.Path.exists") +def test_update_args_with_corrupt_config_file(mock_path_exists, mock_load_config): + # Test setup + mock_path_exists.return_value = True # Pretend the config file exists + mock_load_config.side_effect = json.decoder.JSONDecodeError( + "Expecting value", "", 0 + ) # Simulate corrupt JSON + + args = {"dims": (0, 1, 2), "noise": False, "colors": ["#5e3c99", "#e66101"]} + + # Run the function + updated_args, config = update_args_with_values_from_config_file(args) + + # Check the updates to args + assert updated_args["dims"] == (0, 1, 2) + assert updated_args["noise"] == False + assert updated_args["colors"] == ["#5e3c99", "#e66101"] + + # Check the returned config (should be empty due to error) + assert config == {} + + # Mock class to simulate the peakipy_data object + + +class MockPeakipyData: + def __init__(self, df, pt_per_hz_f2, pt_per_hz_f1, uc_f2, uc_f1): + self.df = df + self.pt_per_hz_f2 = pt_per_hz_f2 + self.pt_per_hz_f1 = pt_per_hz_f1 + self.uc_f2 = uc_f2 + self.uc_f1 = uc_f1 + + +# Test data +@pytest.fixture +def mock_peakipy_data(): + df = pd.DataFrame( + { + "XW_HZ": [10, 20, 30], + "YW_HZ": [5, 15, 25], + "X_PPM": [1.0, 2.0, 3.0], + "Y_PPM": [0.5, 1.5, 2.5], + } + ) + + pt_per_hz_f2 = 2.0 + pt_per_hz_f1 = 3.0 + + uc_f2 = MagicMock() + uc_f1 = MagicMock() + uc_f2.side_effect = lambda x, unit: x * 100.0 if unit == "PPM" else x + uc_f1.side_effect = lambda x, unit: x * 200.0 if unit == "PPM" else x + uc_f2.f = MagicMock(side_effect=lambda x, unit: x * 1000.0 if unit == "PPM" else x) + uc_f1.f = MagicMock(side_effect=lambda x, unit: x * 2000.0 if unit == "PPM" else x) + + return MockPeakipyData(df, pt_per_hz_f2, pt_per_hz_f1, uc_f2, uc_f1) + + +def test_update_linewidths_from_hz_to_points(mock_peakipy_data): + peakipy_data = update_linewidths_from_hz_to_points(mock_peakipy_data) + + expected_XW = [20.0, 40.0, 60.0] + expected_YW = [15.0, 45.0, 75.0] + + pd.testing.assert_series_equal( + peakipy_data.df["XW"], pd.Series(expected_XW, name="XW") + ) + pd.testing.assert_series_equal( + peakipy_data.df["YW"], pd.Series(expected_YW, name="YW") + ) + + +def test_update_peak_positions_from_ppm_to_points(mock_peakipy_data): + peakipy_data = update_peak_positions_from_ppm_to_points(mock_peakipy_data) + + expected_X_AXIS = [100.0, 200.0, 300.0] + expected_Y_AXIS = [100.0, 300.0, 500.0] + expected_X_AXISf = [1000.0, 2000.0, 3000.0] + expected_Y_AXISf = [1000.0, 3000.0, 5000.0] + + pd.testing.assert_series_equal( + peakipy_data.df["X_AXIS"], pd.Series(expected_X_AXIS, name="X_AXIS") + ) + pd.testing.assert_series_equal( + peakipy_data.df["Y_AXIS"], pd.Series(expected_Y_AXIS, name="Y_AXIS") + ) + pd.testing.assert_series_equal( + peakipy_data.df["X_AXISf"], pd.Series(expected_X_AXISf, name="X_AXISf") + ) + pd.testing.assert_series_equal( + peakipy_data.df["Y_AXISf"], pd.Series(expected_Y_AXISf, name="Y_AXISf") + ) + + +@pytest.fixture +def sample_dataframe(): + data = {"A": [1, 2, 3], "B": [4.5678, 5.6789, 6.7890]} + return pd.DataFrame(data) + + +def test_save_data_csv(sample_dataframe): + with tempfile.NamedTemporaryFile(suffix=".csv", delete=False) as tmpfile: + output_name = Path(tmpfile.name) + + try: + save_data(sample_dataframe, output_name) + + assert output_name.exists() + + # Load the CSV and compare with the original dataframe + loaded_df = pd.read_csv(output_name) + pd.testing.assert_frame_equal( + loaded_df, sample_dataframe, check_exact=False, rtol=1e-4 + ) + finally: + os.remove(output_name) + + +def test_save_data_tab(sample_dataframe): + with tempfile.NamedTemporaryFile(suffix=".tab", delete=False) as tmpfile: + output_name = Path(tmpfile.name) + + try: + save_data(sample_dataframe, output_name) + + assert output_name.exists() + + # Load the tab-separated file and compare with the original dataframe + loaded_df = pd.read_csv(output_name, sep="\t") + pd.testing.assert_frame_equal( + loaded_df, sample_dataframe, check_exact=False, rtol=1e-4 + ) + finally: + os.remove(output_name) + + +def test_save_data_pickle(sample_dataframe): + with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as tmpfile: + output_name = Path(tmpfile.name) + + try: + save_data(sample_dataframe, output_name) + + assert output_name.exists() + + # Load the pickle file and compare with the original dataframe + loaded_df = pd.read_pickle(output_name) + pd.testing.assert_frame_equal(loaded_df, sample_dataframe) + finally: + os.remove(output_name) From 1ceb4fb87fa21f16a8d6de538e3c4eafdf256925 Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Sun, 26 May 2024 21:24:05 -0400 Subject: [PATCH 33/37] added more tests --- Makefile | 8 +- peakipy/cli/check_panel.py | 3 + peakipy/cli/edit_panel.py | 11 +- peakipy/cli/main.py | 401 +++++++------------------------- test/test_cli.py | 98 +++++++- test/test_fitting.py | 452 +++++++++++++++++++++++++++++++++++++ test/test_io.py | 8 - test/test_plotting.py | 104 +++++++++ 8 files changed, 745 insertions(+), 340 deletions(-) create mode 100644 test/test_plotting.py diff --git a/Makefile b/Makefile index 431618da..386f86c3 100644 --- a/Makefile +++ b/Makefile @@ -2,7 +2,13 @@ coverage: #coverage run -m pytest test/test_core.py test/test_main.py test/test_fit.py test/test_cli.py - coverage run -m pytest test/test_fitting.py test/test_lineshapes.py test/test_io.py test/test_utils.py test/test_main.py test/test_cli.py + coverage run -m pytest test/test_fitting.py \ + test/test_lineshapes.py \ + test/test_io.py \ + test/test_utils.py \ + test/test_main.py \ + test/test_cli.py \ + test/test_plotting.py coverage-html: coverage html diff --git a/peakipy/cli/check_panel.py b/peakipy/cli/check_panel.py index 2b11d131..5dc06cd9 100644 --- a/peakipy/cli/check_panel.py +++ b/peakipy/cli/check_panel.py @@ -88,6 +88,7 @@ def create_check_panel( data_path: Path, config_path: Path = Path("./peakipy.config"), edit_panel: bool = False, + test: bool = False, ): data = data_singleton() data.fits_path = fits_path @@ -126,6 +127,8 @@ def create_check_panel( ) if edit_panel: return check_pane + elif test: + return else: check_pane.show() diff --git a/peakipy/cli/edit_panel.py b/peakipy/cli/edit_panel.py index f74f5b3e..f44b5faa 100644 --- a/peakipy/cli/edit_panel.py +++ b/peakipy/cli/edit_panel.py @@ -42,7 +42,7 @@ def update_peakipy_data_on_edit_of_table(event): data.bs.update_memcnt() -def panel_app(): +def panel_app(test=False): data = data_singleton() bs = data.bs bokeh_pane = pn.pane.Bokeh(bs.p) @@ -129,16 +129,19 @@ def update_source_selected_indices(event): ) check_app = pn.Card(title="Peakipy check") template.main.append(pn.Column(check_app, spectrum)) - template.show() + if test: + return + else: + template.show() @app.command() -def main(peaklist_path: Path, data_path: Path): +def main(peaklist_path: Path, data_path: Path, test: bool = False): data = data_singleton() data.peaklist_path = peaklist_path data.data_path = data_path data.load_data() - panel_app() + panel_app(test=test) if __name__ == "__main__": diff --git a/peakipy/cli/main.py b/peakipy/cli/main.py index 79613f42..6b3b0aed 100644 --- a/peakipy/cli/main.py +++ b/peakipy/cli/main.py @@ -3,7 +3,6 @@ import json import shutil from pathlib import Path -from dataclasses import dataclass, field from enum import Enum from typing import Optional, Tuple, List, Annotated from multiprocessing import Pool, cpu_count @@ -16,15 +15,11 @@ from tqdm import tqdm from rich import print from skimage.filters import threshold_otsu -from pydantic import BaseModel -import matplotlib -import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import axes3d from matplotlib import cm from matplotlib.backends.backend_pdf import PdfPages -import yaml import plotly.io as pio pio.templates.default = "plotly_dark" @@ -71,13 +66,11 @@ simulate_lineshapes_from_fitted_peak_parameters, simulate_pv_pv_lineshapes_from_fitted_peak_parameters, validate_fit_dataframe, -) - -from .fit import ( fit_peak_clusters, FitPeaksInput, FitPeaksArgs, ) + from peakipy.plotting import ( PlottingDataForPlane, validate_sample_count, @@ -86,7 +79,6 @@ create_residual_figure, create_matplotlib_figure, ) -from .spec import yaml_file app = typer.Typer() tmp_path = Path("tmp") @@ -119,7 +111,6 @@ def read( y_ppm_column_name: str = "Position F2", dims: Annotated[List[int], typer.Option(help=dims_help)] = [0, 1, 2], outfmt: OutFmt = OutFmt.csv, - show: bool = False, fuda: bool = False, ): """Read NMRPipe/Analysis peaklist into pandas dataframe @@ -163,9 +154,6 @@ def read( peak positions [default: "Position F2"] outfmt : OutFmt Format of output peaklist [default: csv] - show : bool - Show the clusters on the spectrum color coded using matplotlib - fuda : bool Create a parameter file for running fuda (params.fuda) @@ -302,39 +290,6 @@ def read( run_log() - yaml = f""" - ########################################################################################################## - # This first block is global parameters which can be overridden by adding the desired argument # - # to your list of spectra. One exception is "colors" which if set in global params overrides the # - # color option set for individual spectra as the colors will now cycle through the chosen matplotlib # - # colormap # - ########################################################################################################## - - cs: {thres} # contour start - contour_num: 10 # number of contours - contour_factor: 1.2 # contour factor - colors: tab20 # must be matplotlib.cm colormap - show_cs: True - - outname: ["clusters.pdf","clusters.png"] # either single value or list of output names - ncol: 1 # tells matplotlib how many columns to give the figure legend - if not set defaults to 2 - clusters: {outname} - dims: {dims} - - # Here is where your list of spectra to plot goes - spectra: - - - fname: {data_path} - label: "" - contour_num: 20 - linewidths: 0.5 - """ - - if show: - with open("show_clusters.yml", "w") as out: - out.write(yaml) - os.system("peakipy spec show_clusters.yml") - print( f"""[green] @@ -536,10 +491,13 @@ def fit( run_log() +fits_help = "CSV file containing peakipy fits" + + @app.command(help="Interactive plots for checking fits") def check( - fits: Path, - data_path: Path, + fits: Annotated[Path, typer.Argument(help=fits_help)], + data_path: Annotated[Path, typer.Argument(help=data_path_help)], clusters: Optional[List[int]] = None, plane: Optional[List[int]] = None, outname: Path = Path("plots.pdf"), @@ -551,8 +509,8 @@ def check( rcount: int = 50, ccount: int = 50, colors: Tuple[str, str] = ("#5e3c99", "#e66101"), - verb: bool = False, plotly: bool = False, + test: bool = False, ): """Interactive plots for checking fits @@ -635,288 +593,87 @@ def check( XY = np.meshgrid(x, y) X, Y = XY - with PdfPages(outname) as pdf: - for _, peak_cluster in peak_clusters: - table = df_to_rich_table( - peak_cluster, - title="", - columns=columns_to_print, - styles=["blue" for _ in columns_to_print], - ) - print(table) + all_plot_data = [] + for _, peak_cluster in peak_clusters: + table = df_to_rich_table( + peak_cluster, + title="", + columns=columns_to_print, + styles=["blue" for _ in columns_to_print], + ) + print(table) - x_radius = peak_cluster.x_radius.max() - y_radius = peak_cluster.y_radius.max() - max_x, min_x = get_limits_for_axis_in_points( - group_axis_points=peak_cluster.center_x, mask_radius_in_points=x_radius - ) - max_y, min_y = get_limits_for_axis_in_points( - group_axis_points=peak_cluster.center_y, mask_radius_in_points=y_radius - ) - max_x, min_x, max_y, min_y = deal_with_peaks_on_edge_of_spectrum( - pseudo3D.data.shape, max_x, min_x, max_y, min_y - ) + x_radius = peak_cluster.x_radius.max() + y_radius = peak_cluster.y_radius.max() + max_x, min_x = get_limits_for_axis_in_points( + group_axis_points=peak_cluster.center_x, mask_radius_in_points=x_radius + ) + max_y, min_y = get_limits_for_axis_in_points( + group_axis_points=peak_cluster.center_y, mask_radius_in_points=y_radius + ) + max_x, min_x, max_y, min_y = deal_with_peaks_on_edge_of_spectrum( + pseudo3D.data.shape, max_x, min_x, max_y, min_y + ) - empty_mask_array = np.zeros( - (pseudo3D.f1_size, pseudo3D.f2_size), dtype=bool - ) - first_plane = peak_cluster[peak_cluster.plane == selected_planes[0]] - individual_masks, mask = make_masks_from_plane_data( - empty_mask_array, first_plane - ) + empty_mask_array = np.zeros((pseudo3D.f1_size, pseudo3D.f2_size), dtype=bool) + first_plane = peak_cluster[peak_cluster.plane == selected_planes[0]] + individual_masks, mask = make_masks_from_plane_data( + empty_mask_array, first_plane + ) - # generate simulated data - for plane_id, plane in peak_cluster.groupby("plane"): - sim_data_singles = [] - sim_data = np.zeros((pseudo3D.f1_size, pseudo3D.f2_size)) - try: - ( - sim_data, - sim_data_singles, - ) = simulate_pv_pv_lineshapes_from_fitted_peak_parameters( - plane, XY, sim_data, sim_data_singles - ) - except: - ( - sim_data, - sim_data_singles, - ) = simulate_lineshapes_from_fitted_peak_parameters( - plane, XY, sim_data, sim_data_singles - ) - - plot_data = PlottingDataForPlane( - pseudo3D, - plane_id, - plane, - X, - Y, - mask, - individual_masks, + # generate simulated data + for plane_id, plane in peak_cluster.groupby("plane"): + sim_data_singles = [] + sim_data = np.zeros((pseudo3D.f1_size, pseudo3D.f2_size)) + try: + ( sim_data, sim_data_singles, - min_x, - max_x, - min_y, - max_y, - fit_color, - data_color, - rcount, - ccount, + ) = simulate_pv_pv_lineshapes_from_fitted_peak_parameters( + plane, XY, sim_data, sim_data_singles + ) + except: + ( + sim_data, + sim_data_singles, + ) = simulate_lineshapes_from_fitted_peak_parameters( + plane, XY, sim_data, sim_data_singles ) - if ccpn_flag: - plt = PlotterWidget() - # fig = create_plotly_figure(plot_data) - if plotly: - fig = create_plotly_figure(plot_data) - residual_fig = create_residual_figure(plot_data) - return fig, residual_fig - else: - plt = matplotlib.pyplot - create_matplotlib_figure( - plot_data, pdf, individual, label, ccpn_flag, show - ) - if first: - break - - run_log() - - -def make_yaml_file(name, yaml_file=yaml_file): - if os.path.exists(name): - print(f"Copying {name} to {name}.bak") - shutil.copy(name, f"{name}.bak") - - print(f"Making yaml file ... {name}") - with open(name, "w") as new_yaml_file: - new_yaml_file.write(yaml_file) - - -@app.command(help="Show first plane with clusters") -def spec(yaml_file: Path, new: bool = False): - if new: - make_yaml_file(name=yaml_file) - exit() - - if yaml_file.exists(): - params = yaml.load(open(yaml_file, "r"), Loader=yaml.FullLoader) - else: - print( - f"[red]{yaml_file} does not exist. Use 'peakipy spec --new' to create one[/red]" - ) - exit() - - fig = plt.figure() - ax = fig.add_subplot(111) - - cs_g = float(params["cs"]) - spectra = params["spectra"] - contour_num_g = params.get("contour_num", 10) - contour_factor_g = params.get("contour_factor", 1.2) - nspec = len(spectra) - notes = [] - legends = 0 - for num, spec in enumerate(spectra): - # unpack spec specific parameters - fname = spec["fname"] - - if params.get("colors"): - # currently overrides color option - color = np.linspace(0, 1, nspec)[num] - colors = cm.get_cmap(params.get("colors"))(color) - # print("Colors set to cycle though %s from Matplotlib"%params.get("colors")) - # print(colors) - colors = colors[:-1] - - else: - colors = spec["colors"] - - neg_colors = spec.get("neg_colors") - label = spec.get("label") - cs = float(spec.get("cs", cs_g)) - contour_num = spec.get("contour_num", contour_num_g) - contour_factor = spec.get("contour_factor", contour_factor_g) - #  append cs and colors to notes - notes.append((cs, colors)) - - # read spectra - dic, data = ng.pipe.read(fname) - udic = ng.pipe.guess_udic(dic, data) - - ndim = udic["ndim"] - - if ndim == 1: - uc_f1 = ng.pipe.make_uc(dic, data, dim=0) - - elif ndim == 2: - f1, f2 = params.get("dims", [0, 1]) - uc_f1 = ng.pipe.make_uc(dic, data, dim=f1) - uc_f2 = ng.pipe.make_uc(dic, data, dim=f2) - - ppm_f1 = uc_f1.ppm_scale() - ppm_f2 = uc_f2.ppm_scale() - - ppm_f1_0, ppm_f1_1 = uc_f1.ppm_limits() # max,min - ppm_f2_0, ppm_f2_1 = uc_f2.ppm_limits() # max,min - - elif ndim == 3: - dims = params.get("dims", [0, 1, 2]) - f1, f2, f3 = dims - uc_f1 = ng.pipe.make_uc(dic, data, dim=f1) - uc_f2 = ng.pipe.make_uc(dic, data, dim=f2) - uc_f3 = ng.pipe.make_uc(dic, data, dim=f3) - #  need to make more robust - ppm_f1 = uc_f2.ppm_scale() - ppm_f2 = uc_f3.ppm_scale() - - ppm_f1_0, ppm_f1_1 = uc_f2.ppm_limits() # max,min - ppm_f2_0, ppm_f2_1 = uc_f3.ppm_limits() # max,min - - # if f1 == 0: - # data = data[f1] - if dims != [1, 2, 3]: - data = np.transpose(data, dims) - data = data[0] - # x and y are set to f2 and f1 - f1, f2 = f2, f3 - # elif f1 == 1: - # data = data[:,0,:] - # else: - # data = data[:,:,0] - - # plot parameters - contour_start = cs # contour level start value - contour_num = contour_num # number of contour levels - contour_factor = contour_factor # scaling factor between contour levels - - # calculate contour levels - cl = contour_start * contour_factor ** np.arange(contour_num) - if len(cl) > 1 and np.min(np.diff(cl)) <= 0.0: - print(f"Setting contour levels to np.abs({cl})") - cl = np.abs(cl) - - ax.contour( - data, - cl, - colors=[colors for _ in cl], - linewidths=spec.get("linewidths", 0.5), - extent=(ppm_f2_0, ppm_f2_1, ppm_f1_0, ppm_f1_1), - ) - - if neg_colors: - ax.contour( - data * -1, - cl, - colors=[neg_colors for _ in cl], - linewidths=spec.get("linewidths", 0.5), - extent=(ppm_f2_0, ppm_f2_1, ppm_f1_0, ppm_f1_1), - ) - - else: # if no neg color given then plot with 0.5 alpha - ax.contour( - data * -1, - cl, - colors=[colors for _ in cl], - linewidths=spec.get("linewidths", 0.5), - extent=(ppm_f2_0, ppm_f2_1, ppm_f1_0, ppm_f1_1), - alpha=0.5, + plot_data = PlottingDataForPlane( + pseudo3D, + plane_id, + plane, + X, + Y, + mask, + individual_masks, + sim_data, + sim_data_singles, + min_x, + max_x, + min_y, + max_y, + fit_color, + data_color, + rcount, + ccount, ) + all_plot_data.append(plot_data) + if plotly: + fig = create_plotly_figure(plot_data) + residual_fig = create_residual_figure(plot_data) + return fig, residual_fig + if first: + break + + with PdfPages(outname) as pdf: + for plot_data in all_plot_data: + create_matplotlib_figure( + plot_data, pdf, individual, label, ccpn_flag, show, test + ) - # make legend - if label: - legends += 1 - # hack for legend - ax.plot([], [], c=colors, label=label) - - # plt.xlim(ppm_f2_0, ppm_f2_1) - ax.invert_xaxis() - ax.set_xlabel(udic[f2]["label"] + " ppm") - if params.get("xlim"): - ax.set_xlim(*params.get("xlim")) - - # plt.ylim(ppm_f1_0, ppm_f1_1) - ax.invert_yaxis() - ax.set_ylabel(udic[f1]["label"] + " ppm") - - if legends > 0: - plt.legend( - loc="upper center", bbox_to_anchor=(0.5, 1.20), ncol=params.get("ncol", 2) - ) - - plt.tight_layout() - - #  add a list of outfiles - y = 0.025 - # only write cs levels if show_cs: True in yaml file - if params.get("show_cs"): - for num, j in enumerate(notes): - col = j[1] - con_strt = j[0] - ax.text(0.025, y, "cs=%.2e" % con_strt, color=col, transform=ax.transAxes) - y += 0.05 - - if params.get("clusters"): - peaklist = params.get("clusters") - if os.path.splitext(peaklist)[-1] == ".csv": - clusters = pd.read_csv(peaklist) - else: - clusters = pd.read_pickle(peaklist) - groups = clusters.groupby("CLUSTID") - for ind, group in groups: - if len(group) == 1: - ax.plot(group.X_PPM, group.Y_PPM, "ko", markersize=1) # , picker=5) - else: - ax.plot(group.X_PPM, group.Y_PPM, "o", markersize=1) # , picker=5) - - if params.get("outname") and (type(params.get("outname")) == list): - for i in params.get("outname"): - plt.savefig(i, bbox_inches="tight", dpi=300) - else: - plt.savefig(params.get("outname", "test.pdf"), bbox_inches="tight") - - # fig.canvas.mpl_connect("pick_event", onpick) - # line, = ax.plot(np.random.rand(100), 'o', picker=5) # 5 points tolerance - plt.show() + run_log() if __name__ == "__main__": diff --git a/test/test_cli.py b/test/test_cli.py index d30404c4..3c14cd4b 100644 --- a/test/test_cli.py +++ b/test/test_cli.py @@ -4,6 +4,8 @@ import pytest import peakipy.cli.main +import peakipy.cli.check_panel +import peakipy.cli.edit_panel from peakipy.cli.main import PeaklistFormat, Lineshape @@ -49,6 +51,37 @@ def test_fit_main_with_default(protein_L): peakipy.cli.main.fit(**args) +def test_fit_main_with_centers_floated(protein_L): + args = dict( + peaklist_path=protein_L / Path("test.csv"), + data_path=protein_L / Path("test1.ft2"), + output_path=protein_L / Path("fits_PV_centers_floated.csv"), + fix=["fraction", "sigma"], + ) + peakipy.cli.main.fit(**args) + + +def test_fit_main_with_centers_bounded(protein_L): + args = dict( + peaklist_path=protein_L / Path("test.csv"), + data_path=protein_L / Path("test1.ft2"), + output_path=protein_L / Path("fits_PV_centers_bounded.csv"), + fix=["fraction", "sigma"], + xy_bounds=[0.01, 0.1], + ) + peakipy.cli.main.fit(**args) + + +def test_fit_main_with_sigmas_floated(protein_L): + args = dict( + peaklist_path=protein_L / Path("test.csv"), + data_path=protein_L / Path("test1.ft2"), + output_path=protein_L / Path("fits_PV_sigmas_floated.csv"), + fix=["fraction", "center"], + ) + peakipy.cli.main.fit(**args) + + def test_fit_main_with_vclist(protein_L): args = dict( peaklist_path=protein_L / Path("test.csv"), @@ -106,7 +139,8 @@ def test_check_main_with_default(protein_L): clusters=[1], first=True, label=True, - show=False, + show=True, + test=True, individual=True, ) peakipy.cli.main.check(**args) @@ -119,7 +153,8 @@ def test_check_main_with_gaussian(protein_L): clusters=[1], first=True, label=True, - show=False, + show=True, + test=True, individual=True, ) peakipy.cli.main.check(**args) @@ -132,7 +167,8 @@ def test_check_main_with_lorentzian(protein_L): clusters=[1], first=True, label=True, - show=False, + show=True, + test=True, individual=True, ) peakipy.cli.main.check(**args) @@ -145,7 +181,8 @@ def test_check_main_with_voigt(protein_L): clusters=[1], first=True, label=True, - show=False, + show=True, + test=True, individual=True, ) peakipy.cli.main.check(**args) @@ -158,7 +195,58 @@ def test_check_main_with_pv_pv(protein_L): clusters=[1], first=True, label=True, - show=False, + show=True, + test=True, individual=True, ) peakipy.cli.main.check(**args) + + +def test_check_panel_PVPV(protein_L): + args = dict( + fits_path=protein_L / Path("fits_PV_PV.csv"), + data_path=protein_L / Path("test1.ft2"), + config_path=protein_L / Path("peakipy.config"), + test=True, + ) + peakipy.cli.check_panel.create_check_panel(**args) + + +def test_check_panel_PV(protein_L): + args = dict( + fits_path=protein_L / Path("fits_PV.csv"), + data_path=protein_L / Path("test1.ft2"), + config_path=protein_L / Path("peakipy.config"), + test=True, + ) + peakipy.cli.check_panel.create_check_panel(**args) + + +def test_check_panel_V(protein_L): + args = dict( + fits_path=protein_L / Path("fits_V.csv"), + data_path=protein_L / Path("test1.ft2"), + config_path=protein_L / Path("peakipy.config"), + test=True, + ) + peakipy.cli.check_panel.create_check_panel(**args) + + +def test_check_panel_edit(protein_L): + args = dict( + fits_path=protein_L / Path("fits_V.csv"), + data_path=protein_L / Path("test1.ft2"), + config_path=protein_L / Path("peakipy.config"), + edit_panel=True, + test=True, + ) + peakipy.cli.check_panel.create_check_panel(**args) + + +def test_edit_panel(protein_L): + args = dict( + peaklist_path=protein_L / Path("test.csv"), + data_path=protein_L / Path("test1.ft2"), + test=True, + ) + peakipy.cli.edit_panel.main(**args) diff --git a/test/test_fitting.py b/test/test_fitting.py index 01c9ed38..7291885d 100644 --- a/test/test_fitting.py +++ b/test/test_fitting.py @@ -5,9 +5,11 @@ import numpy as np import pandas as pd import pytest +from pytest import fixture import nmrglue as ng from numpy.testing import assert_array_equal from lmfit import Model, Parameters +from lmfit.model import ModelResult from peakipy.io import Pseudo3D from peakipy.fitting import ( @@ -31,6 +33,24 @@ PeakLimits, update_params, make_masks_from_plane_data, + get_fit_peaks_result_validation_model, + FitPeaksResultRowPVPV, + FitPeaksResultRowVoigt, + FitPeaksResultRowGLPV, + filter_peak_clusters_by_max_cluster_size, + set_parameters_to_fix_during_fit, + unpack_fitted_parameters_for_lineshape, + get_default_lineshape_param_names, + split_parameter_sets_by_peak, + get_prefix_from_parameter_names, + create_parameter_dict, + perform_initial_lineshape_fit_on_cluster_of_peaks, + merge_unpacked_parameters_with_metadata, + add_vclist_to_df, + update_cluster_df_with_fit_statistics, + rename_columns_for_compatibility, + FitPeaksArgs, + FitPeaksInput, ) from peakipy.lineshapes import Lineshape, pvoigt2d, pv_pv @@ -690,3 +710,435 @@ def test_Pseudo3D(self): self.assertEqual(pseudo3D.f1_size, 256) self.assertEqual(pseudo3D.f2_size, 546) test_nu += 1 + + +def test_get_fit_peaks_result_validation_model_PVPV(): + validation_model = get_fit_peaks_result_validation_model(Lineshape.PV_PV) + assert validation_model == FitPeaksResultRowPVPV + + +def test_get_fit_peaks_result_validation_model_G(): + validation_model = get_fit_peaks_result_validation_model(Lineshape.G) + assert validation_model == FitPeaksResultRowGLPV + + +def test_get_fit_peaks_result_validation_model_L(): + validation_model = get_fit_peaks_result_validation_model(Lineshape.L) + assert validation_model == FitPeaksResultRowGLPV + + +def test_get_fit_peaks_result_validation_model_PV(): + validation_model = get_fit_peaks_result_validation_model(Lineshape.PV) + assert validation_model == FitPeaksResultRowGLPV + + +def test_get_fit_peaks_result_validation_model_V(): + validation_model = get_fit_peaks_result_validation_model(Lineshape.V) + assert validation_model == FitPeaksResultRowVoigt + + +def test_filter_groups_by_max_cluster_size(): + groups = pd.DataFrame( + dict( + col1=[1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 4, 5, 6, 7], + col2=[1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7], + ) + ).groupby("col1") + max_cluster_size = 3 + filtered_groups = filter_peak_clusters_by_max_cluster_size(groups, max_cluster_size) + filtered_group_names = filtered_groups.col1.unique() + expected_group_names = np.array([3, 4, 5, 6, 7]) + np.testing.assert_array_equal(filtered_group_names, expected_group_names) + + +def test_set_parameters_to_fix_during_fit(): + parameter_set = Parameters() + parameter_set.add("test1", vary=True) + modified_parameter_set, float_str = set_parameters_to_fix_during_fit( + parameter_set, ["test1"] + ) + assert modified_parameter_set["test1"].vary == False + + +@fixture +def parameters_set_with_two_variables(): + parameter_set = Parameters() + parameter_set.add("prefix1_test1", vary=True) + parameter_set.add("prefix1_test2", vary=True) + return parameter_set + + +def test_set_parameters_to_fix_during_fit_2(parameters_set_with_two_variables): + modified_parameter_set, float_str = set_parameters_to_fix_during_fit( + parameters_set_with_two_variables, ["prefix1_test1", "prefix1_test2"] + ) + assert ( + modified_parameter_set["prefix1_test2"].vary + == modified_parameter_set["prefix1_test1"].vary + == False + ) + + +def test_set_parameters_to_fix_during_fit_3(): + parameter_set = Parameters() + parameter_set.add("test1", vary=True) + parameter_set.add("test2", vary=True) + modified_parameter_set, float_str = set_parameters_to_fix_during_fit( + parameter_set, ["test2"] + ) + assert ( + modified_parameter_set["test1"].vary + != modified_parameter_set["test2"].vary + == False + ) + + +def test_set_parameters_to_fix_during_fit_None(): + parameter_set = Parameters() + parameter_set.add("test1", vary=True) + parameter_set.add("test2", vary=True) + modified_parameter_set, float_str = set_parameters_to_fix_during_fit( + parameter_set, None + ) + assert ( + modified_parameter_set["test1"].vary + == modified_parameter_set["test2"].vary + == True + ) + + +def test_set_parameters_to_fix_during_fit_None_str(): + parameter_set = Parameters() + parameter_set.add("test1", vary=True) + parameter_set.add("test2", vary=True) + modified_parameter_set, float_str = set_parameters_to_fix_during_fit( + parameter_set, ["None"] + ) + assert ( + modified_parameter_set["test1"].vary + == modified_parameter_set["test2"].vary + == True + ) + + +def test_update_cluster_df_with_fit_statistics(): + result = ModelResult(Model(pvoigt2d), None, None) + result.aic = None + result.bic = None + data = [ + dict( + chisqr=None, + redchi=None, + residual_sum=None, + aic=None, + bic=None, + nfev=0, + ndata=0, + ) + ] + expected_cluster_df = pd.DataFrame(data) + actual_cluster_df = update_cluster_df_with_fit_statistics( + expected_cluster_df, result + ) + pd.testing.assert_frame_equal(actual_cluster_df, expected_cluster_df) + + +def test_rename_columns_for_compatibility(): + df = pd.DataFrame( + [ + dict( + amplitude=1, + amplitude_stderr=1, + X_AXIS=1, + Y_AXIS=1, + ASS="None", + MEMCNT=1, + X_RADIUS=1, + Y_RADIUS=1, + ) + ] + ) + expected_columns = [ + "amp", + "amp_err", + "init_center_x", + "init_center_y", + "assignment", + "memcnt", + "x_radius", + "y_radius", + ] + actual_columns = rename_columns_for_compatibility(df).columns + assert all([i == j for i, j in zip(actual_columns, expected_columns)]) + + +def test_get_default_param_names_pseudo_voigt(): + assert get_default_lineshape_param_names(Lineshape.PV) == [ + "amplitude", + "center_x", + "center_y", + "sigma_x", + "sigma_y", + "fraction", + ] + + +def test_get_default_param_names_gaussian(): + assert get_default_lineshape_param_names(Lineshape.G) == [ + "amplitude", + "center_x", + "center_y", + "sigma_x", + "sigma_y", + "fraction", + ] + + +def test_get_default_param_names_lorentzian(): + assert get_default_lineshape_param_names(Lineshape.L) == [ + "amplitude", + "center_x", + "center_y", + "sigma_x", + "sigma_y", + "fraction", + ] + + +def test_get_default_param_names_pv_pv(): + assert get_default_lineshape_param_names(Lineshape.PV_PV) == [ + "amplitude", + "center_x", + "center_y", + "sigma_x", + "sigma_y", + "fraction_x", + "fraction_y", + ] + + +def test_get_default_param_names_voigt(): + assert get_default_lineshape_param_names(Lineshape.V) == [ + "amplitude", + "center_x", + "center_y", + "sigma_x", + "sigma_y", + "gamma_x", + "gamma_y", + "fraction", + ] + + +def test_split_parameter_sets_by_peak(default_pseudo_voigt_parameter_names): + # the second element of each tuple actually contains an + # lmfit.Parameter object + params = [ + ("p1_amplitude", "amplitude"), + ("p1_center_x", "center_x"), + ("p1_center_y", "center_y"), + ("p1_sigma_x", "sigma_x"), + ("p1_sigma_y", "sigma_y"), + ("p1_fraction", "fraction"), + ("p2_amplitude", "amplitude"), + ("p2_center_x", "center_x"), + ("p2_center_y", "center_y"), + ("p2_sigma_x", "sigma_x"), + ("p2_sigma_y", "sigma_y"), + ("p2_fraction", "fraction"), + ("p3_amplitude", "amplitude"), + ("p3_center_x", "center_x"), + ("p3_center_y", "center_y"), + ("p3_sigma_x", "sigma_x"), + ("p3_sigma_y", "sigma_y"), + ("p3_fraction", "fraction"), + ] + expected_result = [ + [ + ("p1_amplitude", "amplitude"), + ("p1_center_x", "center_x"), + ("p1_center_y", "center_y"), + ("p1_sigma_x", "sigma_x"), + ("p1_sigma_y", "sigma_y"), + ("p1_fraction", "fraction"), + ], + [ + ("p2_amplitude", "amplitude"), + ("p2_center_x", "center_x"), + ("p2_center_y", "center_y"), + ("p2_sigma_x", "sigma_x"), + ("p2_sigma_y", "sigma_y"), + ("p2_fraction", "fraction"), + ], + [ + ("p3_amplitude", "amplitude"), + ("p3_center_x", "center_x"), + ("p3_center_y", "center_y"), + ("p3_sigma_x", "sigma_x"), + ("p3_sigma_y", "sigma_y"), + ("p3_fraction", "fraction"), + ], + ] + expected_result_parameter_names = [[j[0] for j in i] for i in expected_result] + split_parameter_names = [ + [j[0] for j in i] + for i in split_parameter_sets_by_peak( + default_pseudo_voigt_parameter_names, params + ) + ] + assert split_parameter_names == expected_result_parameter_names + + +@fixture +def default_pseudo_voigt_parameter_names(): + return Model(pvoigt2d).param_names + + +def test_get_prefix_from_parameter_names(default_pseudo_voigt_parameter_names): + parameter_items_with_prefixes = [ + ("p1_amplitude", "amplitude"), + ("p1_center_x", "center_x"), + ("p1_center_y", "center_y"), + ("p1_sigma_x", "sigma_x"), + ("p1_sigma_y", "sigma_y"), + ("p1_fraction", "fraction"), + ] + expected_result = "p1_" + actual_result = get_prefix_from_parameter_names( + default_pseudo_voigt_parameter_names, parameter_items_with_prefixes + ) + assert expected_result == actual_result + + +@fixture +def pseudo_voigt_model_result(): + m1 = Model(pvoigt2d, prefix="p1_") + m2 = Model(pvoigt2d, prefix="p2_") + model = m1 + m2 + params = model.make_params() + model_result = ModelResult(model, params) + return model_result + + +def test_create_parameter_dict(pseudo_voigt_model_result): + prefix = "p1_" + params = list(pseudo_voigt_model_result.params.items())[:6] + expected_result = dict( + prefix="p1_", + amplitude=1.0, + amplitude_stderr=None, + center_x=0.5, + center_x_stderr=None, + center_y=0.5, + center_y_stderr=None, + sigma_x=1.0, + sigma_x_stderr=None, + sigma_y=1.0, + sigma_y_stderr=None, + fraction=0.5, + fraction_stderr=None, + ) + actual_result = create_parameter_dict(prefix, params) + assert expected_result == actual_result + + +def test_unpack_fitted_parameters_for_lineshape_PV(pseudo_voigt_model_result): + expected_params = [ + dict( + prefix="p1_", + plane=0, + amplitude=1.0, + amplitude_stderr=None, + center_x=0.5, + center_x_stderr=None, + center_y=0.5, + center_y_stderr=None, + sigma_x=1.0, + sigma_x_stderr=None, + sigma_y=1.0, + sigma_y_stderr=None, + fraction=0.5, + fraction_stderr=None, + ), + dict( + prefix="p2_", + plane=0, + amplitude=1.0, + amplitude_stderr=None, + center_x=0.5, + center_x_stderr=None, + center_y=0.5, + center_y_stderr=None, + sigma_x=1.0, + sigma_x_stderr=None, + sigma_y=1.0, + sigma_y_stderr=None, + fraction=0.5, + fraction_stderr=None, + ), + ] + unpacked_params = unpack_fitted_parameters_for_lineshape( + Lineshape.PV, list(pseudo_voigt_model_result.params.items()), plane_number=0 + ) + assert expected_params == unpacked_params + + +def test_merge_unpacked_parameters_with_metadata(): + cluster_fit_df = pd.DataFrame( + dict( + plane=[0, 1, 2, 3, 0, 1, 2, 3], + prefix=["_p1_", "_p1_", "_p1_", "_p1_", "_p2_", "_p2_", "_p2_", "_p2_"], + ) + ) + peak_df = pd.DataFrame(dict(ASS=["p1", "p2"], data=["p1_data", "p2_data"])) + expected_result = pd.DataFrame( + dict( + plane=[0, 1, 2, 3, 0, 1, 2, 3], + prefix=["_p1_", "_p1_", "_p1_", "_p1_", "_p2_", "_p2_", "_p2_", "_p2_"], + ASS=["p1", "p1", "p1", "p1", "p2", "p2", "p2", "p2"], + data=[ + "p1_data", + "p1_data", + "p1_data", + "p1_data", + "p2_data", + "p2_data", + "p2_data", + "p2_data", + ], + ) + ) + actual_result = merge_unpacked_parameters_with_metadata(cluster_fit_df, peak_df) + assert expected_result.equals(actual_result) + + +def test_add_vclist_to_df(): + args = FitPeaksArgs( + noise=0, uc_dics={}, lineshape=Lineshape.PV, vclist_data=np.array([1, 2, 3]) + ) + fit_peaks_input = FitPeaksInput( + args=args, data=None, config=None, plane_numbers=None + ) + df = pd.DataFrame(dict(plane=[0, 1, 2])) + expected_df = pd.DataFrame(dict(plane=[0, 1, 2], vclist=[1, 2, 3])) + actual_df = add_vclist_to_df(fit_peaks_input, df) + assert actual_df.equals(expected_df) + + +def test_add_vclist_to_df_plane_order(): + args = FitPeaksArgs( + noise=0, uc_dics={}, lineshape=Lineshape.PV, vclist_data=np.array([1, 2, 3]) + ) + fit_peaks_input = FitPeaksInput( + args=args, data=None, config=None, plane_numbers=None + ) + df = pd.DataFrame(dict(plane=[2, 1, 0])) + expected_df = pd.DataFrame(dict(plane=[2, 1, 0], vclist=[3, 2, 1])) + actual_df = add_vclist_to_df(fit_peaks_input, df) + assert actual_df.equals(expected_df) + + +# def test_perform_initial_lineshape_fit_on_cluster_of_peaks(pseudo_voigt_model_result): +# expected_result = pseudo_voigt_model_result +# actual_result = perform_initial_lineshape_fit_on_cluster_of_peaks() +# assert expected_result == actual_result diff --git a/test/test_io.py b/test/test_io.py index a945852d..313ed0e3 100644 --- a/test/test_io.py +++ b/test/test_io.py @@ -79,14 +79,6 @@ def test_read_pipe_peaklist(self): # self.assertEqual(peaklist.df.ASS.iloc[1], "None_dummy_1") -class TestSpecScript(unittest.TestCase): - @patch("peakipy.cli.main.spec") - def test_main(self, MockSpec): - args = {"": "hello", "": "data"} - spec = MockSpec(args) - self.assertIsNotNone(spec) - - def test_load_config_existing(): config_path = Path("test_config.json") # Create a dummy existing config file diff --git a/test/test_plotting.py b/test/test_plotting.py new file mode 100644 index 00000000..9a0182ed --- /dev/null +++ b/test/test_plotting.py @@ -0,0 +1,104 @@ +from dataclasses import dataclass + +import numpy as np + +from peakipy.plotting import ( + construct_surface_legend_string, + plot_data_is_valid, + df_to_rich_table, +) + + +@dataclass +class Row: + assignment: str + + +def test_construct_surface_legend_string(): + row = Row("assignment") + expected_legend = "assignment" + actual_legend = construct_surface_legend_string(row) + assert expected_legend == actual_legend + + +import pytest +from unittest.mock import MagicMock, patch + + +# Mock PlottingDataForPlane class for testing purposes +class PlottingDataForPlane: + def __init__( + self, x_plot, y_plot, masked_data, plane_lineshape_parameters, pseudo3D, plane + ): + self.x_plot = x_plot + self.y_plot = y_plot + self.masked_data = masked_data + self.plane_lineshape_parameters = plane_lineshape_parameters + self.pseudo3D = pseudo3D + self.plane = plane + + +@pytest.fixture +def valid_plot_data(): + return PlottingDataForPlane( + x_plot=np.array([[1, 2, 3]]), + y_plot=np.array([[4, 5, 6]]), + masked_data=np.array([[7, 8, 9]]), + plane_lineshape_parameters=MagicMock(clustid=1), + pseudo3D=MagicMock( + f1_ppm_limits=[0, 1], f2_ppm_limits=[0, 1], f1_label="F1", f2_label="F2" + ), + plane=MagicMock(clustid=1), + ) + + +@pytest.fixture +def invalid_plot_data_empty_x(): + return PlottingDataForPlane( + x_plot=np.array([]), + y_plot=np.array([[4, 5, 6]]), + masked_data=np.array([[7, 8, 9]]), + plane_lineshape_parameters=MagicMock(clustid=1), + pseudo3D=MagicMock( + f1_ppm_limits=[0, 1], f2_ppm_limits=[0, 1], f1_label="F1", f2_label="F2" + ), + plane=MagicMock(clustid=1), + ) + + +@pytest.fixture +def invalid_plot_data_empty_masked(): + return PlottingDataForPlane( + x_plot=np.array([[1, 2, 3]]), + y_plot=np.array([[4, 5, 6]]), + masked_data=np.array([[]]), + plane_lineshape_parameters=MagicMock(clustid=1), + pseudo3D=MagicMock( + f1_ppm_limits=[0, 1], f2_ppm_limits=[0, 1], f1_label="F1", f2_label="F2" + ), + plane=MagicMock(clustid=1), + ) + + +def test_plot_data_is_valid(valid_plot_data): + assert plot_data_is_valid(valid_plot_data) == True + + +@patch("peakipy.plotting.print") +@patch("peakipy.plotting.plt.close") +def test_plot_data_is_invalid_empty_x( + mock_close, mock_print, invalid_plot_data_empty_x +): + assert plot_data_is_valid(invalid_plot_data_empty_x) == False + assert mock_print.call_count == 3 + mock_close.assert_called_once() + + +@patch("peakipy.plotting.print") +@patch("peakipy.plotting.plt.close") +def test_plot_data_is_invalid_empty_masked( + mock_close, mock_print, invalid_plot_data_empty_masked +): + assert plot_data_is_valid(invalid_plot_data_empty_masked) == False + assert mock_print.call_count == 4 + mock_close.assert_called_once() From 2a5e97fcc97d95c59089b2a7b4a288d7a76c3d3a Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Sun, 26 May 2024 21:33:09 -0400 Subject: [PATCH 34/37] more tests and refactoring --- Makefile | 1 - peakipy/cli/edit_fits_app/main.py | 628 ------ .../cli/edit_fits_app/templates/index.html | 16 - peakipy/cli/edit_fits_app/theme.yaml | 18 - peakipy/cli/fit.py | 563 ----- peakipy/cli/spec.py | 92 - peakipy/core.py | 1950 ----------------- peakipy/fitting.py | 538 ++++- peakipy/plotting.py | 3 + test/test_core.py | 570 ----- test/test_fit.py | 460 ---- 11 files changed, 538 insertions(+), 4301 deletions(-) delete mode 100755 peakipy/cli/edit_fits_app/main.py delete mode 100644 peakipy/cli/edit_fits_app/templates/index.html delete mode 100644 peakipy/cli/edit_fits_app/theme.yaml delete mode 100644 peakipy/cli/fit.py delete mode 100644 peakipy/cli/spec.py delete mode 100644 peakipy/core.py delete mode 100644 test/test_core.py delete mode 100644 test/test_fit.py diff --git a/Makefile b/Makefile index 386f86c3..432dee0d 100644 --- a/Makefile +++ b/Makefile @@ -1,7 +1,6 @@ .PHONY: coverage coverage: - #coverage run -m pytest test/test_core.py test/test_main.py test/test_fit.py test/test_cli.py coverage run -m pytest test/test_fitting.py \ test/test_lineshapes.py \ test/test_io.py \ diff --git a/peakipy/cli/edit_fits_app/main.py b/peakipy/cli/edit_fits_app/main.py deleted file mode 100755 index de0d67fe..00000000 --- a/peakipy/cli/edit_fits_app/main.py +++ /dev/null @@ -1,628 +0,0 @@ -#!/usr/bin/env python3 -""" Script for checking fits and editing fit params - - Usage: - edit_fits_script.py [options] - - Arguments: - peaklist output from read_peaklist.py (csv, tab or pkl) - NMRPipe data - - Options: - --dims= order of dimensions [default: 0,1,2] - - - peakipy - deconvolute overlapping NMR peaks - Copyright (C) 2019 Jacob Peter Brady - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see . - -""" -import os -import json - -from docopt import docopt -from pathlib import Path - -import pandas as pd -import nmrglue as ng -import numpy as np -import matplotlib.pyplot as plt -from matplotlib.cm import magma, autumn - -from scipy import ndimage - -# from scipy.ndimage.morphology import binary_dilation -from skimage.filters import threshold_otsu -from skimage.morphology import binary_closing, square, rectangle, disk - -from bokeh.events import ButtonClick, DoubleTap -from bokeh.layouts import row, column, widgetbox -from bokeh.models import ColumnDataSource -from bokeh.models.tools import HoverTool -from bokeh.models.widgets import ( - Slider, - Select, - Button, - DataTable, - TableColumn, - NumberFormatter, - NumberEditor, - IntEditor, - StringEditor, - StringFormatter, - SelectEditor, - TextInput, - RadioButtonGroup, - Div, -) -from bokeh.plotting import figure -from bokeh.io import curdoc -from bokeh.palettes import PuBuGn9, Category20 - -from peakipy.core import Pseudo3D - - -def clusters( - df, data, thres=None, struc_el="square", struc_size=(3,), iterations=1, l_struc=None -): - """Find clusters of peaks - - Need to update these docs. - - thres : float - threshold for signals above which clusters are selected - ndil: int - number of iterations of ndimage.binary_dilation function if set to 0 then function not used - """ - - peaks = [[y, x] for y, x in zip(df.Y_AXIS, df.X_AXIS)] - - if thres == None: - thresh = threshold_otsu(data[0]) - else: - thresh = thres - - thresh_data = np.bitwise_or(data[0] < (thresh * -1.0), data[0] > thresh) - - if struc_el == "disk": - radius = struc_size[0] - print(f"using disk with {radius}") - closed_data = binary_closing(thresh_data, disk(int(radius))) - # closed_data = binary_dilation(thresh_data, disk(radius), iterations=iterations) - - elif struc_el == "square": - width = struc_size[0] - print(f"using square with {width}") - closed_data = binary_closing(thresh_data, square(int(width))) - # closed_data = binary_dilation(thresh_data, square(width), iterations=iterations) - - elif struc_el == "rectangle": - width, height = struc_size - print(f"using rectangle with {width} and {height}") - closed_data = binary_closing(thresh_data, rectangle(int(width), int(height))) - # closed_data = binary_dilation(thresh_data, rectangle(width, height), iterations=iterations) - - else: - closed_data = thresh_data - print(f"Not using any closing function") - - labeled_array, num_features = ndimage.label(closed_data, l_struc) - # print(labeled_array, num_features) - - df["CLUSTID"] = [labeled_array[i[0], i[1]] for i in peaks] - - #  renumber "0" clusters - max_clustid = df["CLUSTID"].max() - n_of_zeros = len(df[df["CLUSTID"] == 0]["CLUSTID"]) - df.loc[df[df["CLUSTID"] == 0].index, "CLUSTID"] = np.arange( - max_clustid + 1, n_of_zeros + max_clustid + 1, dtype=int - ) - - for ind, group in df.groupby("CLUSTID"): - df.loc[group.index, "MEMCNT"] = len(group) - - df["color"] = df.apply( - lambda x: Category20[20][int(x.CLUSTID) % 20] if x.MEMCNT > 1 else "black", - axis=1, - ) - - source.data = {col: df[col] for col in df.columns} - - return df - - -def recluster_peaks(event): - struc_size = tuple([int(i) for i in struct_el_size.value.split(",")]) - - print(struc_size) - clusters( - df, - data, - thres=eval(contour_start.value), - struc_el=struct_el.value, - struc_size=struc_size, - # iterations=int(iterations.value) - ) - # print("struct", struct_el.value) - # print("struct size", struct_el_size.value ) - # print(type(struct_el_size.value) ) - # print(type(eval(struct_el_size.value)) ) - # print(type([].extend(eval(struct_el_size.value))) - - -def update_memcnt(df): - for ind, group in df.groupby("CLUSTID"): - df.loc[group.index, "MEMCNT"] = len(group) - - # set cluster colors (set to black if singlet peaks) - df["color"] = df.apply( - lambda x: Category20[20][int(x.CLUSTID) % 20] if x.MEMCNT > 1 else "black", - axis=1, - ) - # update source data - source.data = {col: df[col] for col in df.columns} - return df - - -def fit_selected(event): - selectionIndex = source.selected.indices - current = df.iloc[selectionIndex] - - df.loc[selectionIndex, "X_RADIUS_PPM"] = slider_X_RADIUS.value - df.loc[selectionIndex, "Y_RADIUS_PPM"] = slider_Y_RADIUS.value - - df.loc[selectionIndex, "X_DIAMETER_PPM"] = current["X_RADIUS_PPM"] * 2.0 - df.loc[selectionIndex, "Y_DIAMETER_PPM"] = current["Y_RADIUS_PPM"] * 2.0 - - selected_df = df[df.CLUSTID.isin(list(current.CLUSTID))] - - selected_df.to_csv("~tmp.csv") - - lineshape = lineshapes[radio_button_group.active] - print("Using LS = ", lineshape) - print( - f"fit_peaks ~tmp.csv {data_path} ~tmp_out.csv --plot=out --show --lineshape={lineshape} --dims={_dims}" - ) - os.system( - f"fit_peaks ~tmp.csv {data_path} ~tmp_out.csv --plot=out --show --lineshape={lineshape} --dims={_dims}" - ) - - -def save_peaks(event): - if savefilename.value: - to_save = Path(savefilename.value) - else: - to_save = Path(savefilename.placeholder) - - if to_save.exists(): - os.system(f"cp {to_save} {to_save}.bak") - print(f"Making backup {to_save}.bak") - - print(f"Saving peaks to {to_save}") - if to_save.suffix == ".csv": - df.to_csv(to_save, float_format="%.4f", index=False) - else: - df.to_pickle(to_save) - - -def select_callback(attrname, old, new): - # print("Calling Select Callback") - selectionIndex = source.selected.indices - current = df.iloc[selectionIndex] - - # update memcnt - update_memcnt(df) - - -def peak_pick_callback(event): - print(event.x, event.y) - - -def slider_callback(attrname, old, new): - selectionIndex = source.selected.indices - current = df.iloc[selectionIndex] - - df.loc[selectionIndex, "X_RADIUS"] = slider_X_RADIUS.value * pt_per_ppm_f2 - df.loc[selectionIndex, "Y_RADIUS"] = slider_Y_RADIUS.value * pt_per_ppm_f1 - df.loc[selectionIndex, "X_RADIUS_PPM"] = slider_X_RADIUS.value - df.loc[selectionIndex, "Y_RADIUS_PPM"] = slider_Y_RADIUS.value - - df.loc[selectionIndex, "X_DIAMETER_PPM"] = current["X_RADIUS_PPM"] * 2.0 - df.loc[selectionIndex, "Y_DIAMETER_PPM"] = current["Y_RADIUS_PPM"] * 2.0 - df.loc[selectionIndex, "X_DIAMETER"] = current["X_RADIUS"] * 2.0 - df.loc[selectionIndex, "Y_DIAMETER"] = current["Y_RADIUS"] * 2.0 - - # set edited rows to True - df.loc[selectionIndex, "Edited"] = True - - # selected_df = df[df.CLUSTID.isin(list(current.CLUSTID))] - # print(list(selected_df)) - source.data = {col: df[col] for col in df.columns} - - -def get_contour_data(data, levels, **kwargs): - cs = plt.contour(data, levels, **kwargs) - xs = [] - ys = [] - xt = [] - yt = [] - col = [] - text = [] - isolevelid = 0 - for isolevel in cs.collections: - isocol = isolevel.get_color()[0] - thecol = 3 * [None] - theiso = str(cs.get_array()[isolevelid]) - isolevelid += 1 - for i in range(3): - thecol[i] = int(255 * isocol[i]) - thecol = "#%02x%02x%02x" % (thecol[0], thecol[1], thecol[2]) - - for path in isolevel.get_paths(): - v = path.vertices - x = v[:, 0] - y = v[:, 1] - xs.append(x.tolist()) - ys.append(y.tolist()) - indx = int(len(x) / 2) - indy = int(len(y) / 2) - xt.append(x[indx]) - yt.append(y[indy]) - text.append(theiso) - col.append(thecol) - - source = ColumnDataSource( - data={"xs": xs, "ys": ys, "line_color": col, "xt": xt, "yt": yt, "text": text} - ) - return source - - -def update_contour(attrname, old, new): - new_cs = eval(contour_start.value) - cl = new_cs * contour_factor ** np.arange(contour_num) - spec_source.data = get_contour_data(data[0], cl, extent=extent).data - - -def exit_edit_peaks(event): - exit() - - -#  Script starts here - -args = docopt(__doc__) -path = Path(args.get("")) - -if path.suffix == ".csv": - df = pd.read_csv(path) # , comment="#") -elif path.suffix == ".tab": - df = pd.read_csv(path, sep="\t") # comment="#") -else: - df = pd.read_pickle(path) - -# make diameter columns -if "X_DIAMETER_PPM" in df.columns: - pass -else: - df["X_DIAMETER_PPM"] = df["X_RADIUS_PPM"] * 2.0 - df["Y_DIAMETER_PPM"] = df["Y_RADIUS_PPM"] * 2.0 - -#  make a column to track edited peaks -if "Edited" in df.columns: - pass -else: - df["Edited"] = np.zeros(len(df), dtype=bool) - -if "include" in df.columns: - pass -else: - df["include"] = df.apply(lambda _: "yes", axis=1) -# df["color"] = df.Edited.apply(lambda x: 'red' if x else 'black') - -# color clusters -df["color"] = df.apply( - lambda x: Category20[20][int(x.CLUSTID) % 20] if x.MEMCNT > 1 else "black", axis=1 -) - -# make datasource -source = ColumnDataSource(data=dict()) -source.data = {col: df[col] for col in df.columns} - - -#  read dims from config -config_path = Path("peakipy.config") -if config_path.exists(): - config = json.load(open(config_path)) - print(f"Using config file with --dims={config.get('--dims')}") - dims = config.get("--dims", [0, 1, 2]) - _dims = ",".join(str(i) for i in dims) - -else: - # get dim numbers from commandline - _dims = args.get("--dims") - dims = [int(i) for i in _dims.split(",")] - - -# read pipe data -data_path = args.get("") -dic, data = ng.pipe.read(data_path) -pseudo3D = Pseudo3D(dic, data, dims) -data = pseudo3D.data -udic = pseudo3D.udic - -dims = pseudo3D.dims -planes, f1, f2 = dims -# size of f1 and f2 in points -f2pts = pseudo3D.f2_size -f1pts = pseudo3D.f1_size - -#  points per ppm -pt_per_ppm_f1 = pseudo3D.pt_per_ppm_f1 -pt_per_ppm_f2 = pseudo3D.pt_per_ppm_f2 - -# get ppm limits for ppm scales -uc_f1 = pseudo3D.uc_f1 -ppm_f1 = uc_f1.ppm_scale() -ppm_f1_0, ppm_f1_1 = uc_f1.ppm_limits() - -uc_f2 = pseudo3D.uc_f2 -ppm_f2 = uc_f2.ppm_scale() -ppm_f2_0, ppm_f2_1 = uc_f2.ppm_limits() - -f2_label = pseudo3D.f2_label -f1_label = pseudo3D.f1_label -#  make bokeh figure -tools = [ - "redo", - "undo", - "tap", - "box_zoom", - "lasso_select", - "box_select", - "wheel_zoom", - "pan", - "reset", -] -p = figure( - x_range=(ppm_f2_0, ppm_f2_1), - y_range=(ppm_f1_0, ppm_f1_1), - x_axis_label=f"{f2_label} - ppm", - y_axis_label=f"{f1_label} - ppm", - title="Check fits", - tools=tools, - active_drag="pan", - active_scroll="wheel_zoom", - active_tap=None, -) - -thres = threshold_otsu(data[0]) -contour_start = thres # contour level start value -contour_num = 20 # number of contour levels -contour_factor = 1.20 # scaling factor between contour levels -cl = contour_start * contour_factor ** np.arange(contour_num) -extent = (ppm_f2_0, ppm_f2_1, ppm_f1_0, ppm_f1_1) -spec_source = get_contour_data(data[0], cl, extent=extent) -#  negative contours -spec_source_neg = get_contour_data(data[0] * -1.0, cl, extent=extent, cmap=autumn) -p.multi_line(xs="xs", ys="ys", line_color="line_color", source=spec_source) -p.multi_line(xs="xs", ys="ys", line_color="line_color", source=spec_source_neg) -# contour_num = Slider(title="contour number", value=20, start=1, end=50,step=1) -# contour_start = Slider(title="contour start", value=100000, start=1000, end=10000000,step=1000) -contour_start = TextInput(value="%.2e" % thres, title="Contour level:") -# contour_factor = Slider(title="contour factor", value=1.20, start=1., end=2.,step=0.05) -contour_start.on_change("value", update_contour) -# for w in [contour_num,contour_start,contour_factor]: -# w.on_change("value",update_contour) - -#  plot mask outlines -el = p.ellipse( - x="X_PPM", - y="Y_PPM", - width="X_DIAMETER_PPM", - height="Y_DIAMETER_PPM", - source=source, - fill_color="color", - fill_alpha=0.1, - line_dash="dotted", - line_color="red", -) - -p.add_tools( - HoverTool( - tooltips=[ - ("Index", "$index"), - ("Assignment", "@ASS"), - ("CLUSTID", "@CLUSTID"), - ("RADII", "@X_RADIUS_PPM{0.000}, @Y_RADIUS_PPM{0.000}"), - (f"{f2_label},{f1_label}", "$x{0.000} ppm, $y{0.000} ppm"), - ], - mode="mouse", - # add renderers - renderers=[el], - ) -) -# p.toolbar.active_scroll = "auto" - -p.circle(x="X_PPM", y="Y_PPM", source=source, color="color") -# plot cluster numbers -p.text( - x="X_PPM", - y="Y_PPM", - text="CLUSTID", - text_color="color", - source=source, - text_font_size="8pt", - text_font_style="bold", -) - -p.on_event(DoubleTap, peak_pick_callback) - -# configure sliders -slider_X_RADIUS = Slider( - title="X_RADIUS - ppm", - start=0.001, - end=0.200, - value=0.040, - step=0.001, - format="0[.]000", -) -slider_Y_RADIUS = Slider( - title="Y_RADIUS - ppm", - start=0.010, - end=2.000, - value=0.400, - step=0.001, - format="0[.]000", -) - -slider_X_RADIUS.on_change( - "value", lambda attr, old, new: slider_callback(attr, old, new) -) -slider_Y_RADIUS.on_change( - "value", lambda attr, old, new: slider_callback(attr, old, new) -) - -# save file -savefilename = TextInput( - title="Save file as (.csv or .pkl)", placeholder="edited_peaks.csv" -) -button = Button(label="Save", button_type="success") -button.on_event(ButtonClick, save_peaks) -# call fit_peaks -fit_button = Button(label="Fit selected cluster", button_type="primary") -radio_button_group = RadioButtonGroup( - labels=["PV", "G", "L", "PV_L", "PV_G", "PV_PV", "G_L"], active=0 -) -lineshapes = {0: "PV", 1: "G", 2: "L", 3: "PV_L", 4: "PV_G", 5: "PV_PV", 6: "G_L"} -ls_div = Div( - text="Choose lineshape you wish to fit. This can be Pseudo-voigt (PV), Gaussian (G), Lorentzian (L), PV/G, PV/L, PV_PV, G/L. PV/G fits a PV lineshape to the direct dimension and a G lineshape to the indirect." -) -clust_div = Div( - text="""If you want to adjust how the peaks are automatically clustered then try changing the - width/diameter/height (integer values) of the structuring element used during the binary dilation step - (you can also remove it by selecting 'None'). Increasing the size of the structuring element will cause - peaks to be more readily incorporated into clusters.""" -) - -#  not sure this is needed -selected_df = df.copy() - -fit_button.on_event(ButtonClick, fit_selected) - -# selected_columns = [ -# "ASS", -# "CLUSTID", -# "X_PPM", -# "Y_PPM", -# "X_RADIUS_PPM", -# "Y_RADIUS_PPM", -# "XW_HZ", -# "YW_HZ", -# "VOL", -# "include", -# "MEMCNT", -# ] -# -# columns = [TableColumn(field=field, title=field) for field in selected_columns] -columns = [ - TableColumn(field="ASS", title="Assignment"), - TableColumn(field="CLUSTID", title="Cluster", editor=IntEditor()), - TableColumn( - field="X_PPM", - title=f"{f2_label}", - editor=NumberEditor(step=0.0001), - formatter=NumberFormatter(format="0.0000"), - ), - TableColumn( - field="Y_PPM", - title=f"{f1_label}", - editor=NumberEditor(step=0.0001), - formatter=NumberFormatter(format="0.0000"), - ), - TableColumn( - field="X_RADIUS_PPM", - title=f"{f2_label} radius (ppm)", - editor=NumberEditor(step=0.0001), - formatter=NumberFormatter(format="0.0000"), - ), - TableColumn( - field="Y_RADIUS_PPM", - title=f"{f1_label} radius (ppm)", - editor=NumberEditor(step=0.0001), - formatter=NumberFormatter(format="0.0000"), - ), - TableColumn( - field="XW_HZ", - title=f"{f2_label} LW (Hz)", - editor=NumberEditor(step=0.01), - formatter=NumberFormatter(format="0.00"), - ), - TableColumn( - field="YW_HZ", - title=f"{f1_label} LW (Hz)", - editor=NumberEditor(step=0.01), - formatter=NumberFormatter(format="0.00"), - ), - TableColumn(field="VOL", title="Volume", formatter=NumberFormatter(format="0.0")), - TableColumn( - field="include", title="Include", editor=SelectEditor(options=["yes", "no"]) - ), - TableColumn(field="MEMCNT", title="MEMCNT", editor=IntEditor()), -] - -data_table = DataTable( - source=source, columns=columns, editable=True, fit_columns=True, width=800 -) - -# callback for adding -# source.selected.on_change('indices', callback) -source.selected.on_change("indices", select_callback) - -# controls = column(slider, button) -exit_button = Button(label="Quit", button_type="warning") -exit_button.on_event(ButtonClick, exit_edit_peaks) - -controls = column( - row(slider_X_RADIUS, slider_Y_RADIUS), - row( - column(contour_start, fit_button, widgetbox(ls_div), radio_button_group), - column(savefilename, button, exit_button), - ), -) - -# widgetbox(radio_button_group) -struct_el = Select( - title="Structuring element:", - value="disk", - options=["square", "disk", "rectangle", "None"], -) - -struct_el_size = TextInput( - value="3", title="Size(width/radius or width,height for rectangle):" -) -# iterations = TextInput(value="1", title="Number of iterations of binary dilation") -recluster = Button(label="Re-cluster", button_type="warning") -recluster.on_event(ButtonClick, recluster_peaks) - -# cluster_widget = widgetbox(struct_el, struct_el_size) -# recluster) -curdoc().add_root( - row( - column(p, widgetbox(clust_div), row(struct_el, struct_el_size), recluster), - column(data_table, controls), - ) -) -curdoc().title = "peakipy: Edit Fits" -# curdoc().theme = 'dark_minimal' -# update() diff --git a/peakipy/cli/edit_fits_app/templates/index.html b/peakipy/cli/edit_fits_app/templates/index.html deleted file mode 100644 index ce7d5b89..00000000 --- a/peakipy/cli/edit_fits_app/templates/index.html +++ /dev/null @@ -1,16 +0,0 @@ -{% extends base %} - -{% block title %}Bokeh Crossfilter Example{% endblock %} - -{% block preamble %} - -{% endblock %} diff --git a/peakipy/cli/edit_fits_app/theme.yaml b/peakipy/cli/edit_fits_app/theme.yaml deleted file mode 100644 index b972fba4..00000000 --- a/peakipy/cli/edit_fits_app/theme.yaml +++ /dev/null @@ -1,18 +0,0 @@ -attrs: - Figure: - background_fill_color: 'white' - border_fill_color: '#2F2F2F' - outline_line_color: '#444444' - - Axis: - axis_line_color: "white" - axis_label_text_color: "white" - major_label_text_color: "white" - major_tick_line_color: "white" - minor_tick_line_color: "white" - minor_tick_line_color: "white" - Grid: - grid_line_dash: [6, 4] - grid_line_alpha: .3 - -text_color: "white" diff --git a/peakipy/cli/fit.py b/peakipy/cli/fit.py deleted file mode 100644 index 8ffc73e1..00000000 --- a/peakipy/cli/fit.py +++ /dev/null @@ -1,563 +0,0 @@ -#!/usr/bin/env python3 -"""Fit and deconvolute NMR peaks: Functions used for running peakipy fit -""" -from pathlib import Path -from typing import Optional, List, Tuple -from dataclasses import dataclass, field - -import numpy as np -import pandas as pd - -from rich import print -from rich.console import Console -from pydantic import BaseModel -from lmfit import Model, Parameter, Parameters -from lmfit.model import ModelResult - -from peakipy.lineshapes import ( - Lineshape, - pvoigt2d, - voigt2d, - pv_pv, - get_lineshape_function, -) -from peakipy.fitting import ( - fix_params, - to_prefix, - get_limits_for_axis_in_points, - deal_with_peaks_on_edge_of_spectrum, - select_planes_above_threshold_from_masked_data, - select_reference_planes_using_indices, - make_models, - make_meshgrid, - slice_peaks_from_data_using_mask, - make_mask_from_peak_cluster, -) - -console = Console() -π = np.pi -sqrt2 = np.sqrt(2.0) - -tmp_path = Path("tmp") -tmp_path.mkdir(exist_ok=True) -log_path = Path("log.txt") - - -@dataclass -class FitPeaksArgs: - noise: float - uc_dics: dict - lineshape: Lineshape - dims: List[int] = field(default_factory=lambda: [0, 1, 2]) - colors: Tuple[str] = ("#5e3c99", "#e66101") - max_cluster_size: Optional[int] = None - to_fix: List[str] = field(default_factory=lambda: ["fraction", "sigma", "center"]) - xy_bounds: Tuple[float, float] = ((0, 0),) - vclist: Optional[Path] = (None,) - plane: Optional[List[int]] = (None,) - exclude_plane: Optional[List[int]] = (None,) - reference_plane_indices: List[int] = ([],) - initial_fit_threshold: Optional[float] = (None,) - jack_knife_sample_errors: bool = False - mp: bool = (True,) - verbose: bool = (False,) - vclist_data: Optional[np.array] = None - - -@dataclass -class Config: - fit_method: str = "leastsq" - - -@dataclass -class FitPeaksInput: - """input data for the fit_peaks function""" - - args: FitPeaksArgs - data: np.array - config: Config - plane_numbers: list - - -@dataclass -class FitPeakClusterInput: - args: FitPeaksArgs - data: np.array - config: Config - plane_numbers: list - clustid: int - group: pd.DataFrame - last_peak: pd.DataFrame - mask: np.array - mod: Model - p_guess: Parameters - XY: np.array - peak_slices: np.array - XY_slices: np.array - min_x: float - max_x: float - min_y: float - max_y: float - uc_dics: dict - first_plane_data: np.array - weights: np.array - fit_method: str = "leastsq" - verbose: bool = False - masked_plane_data: np.array = field(init=False) - - def __post_init__(self): - self.masked_plane_data = np.array([d[self.mask] for d in self.data]) - - -@dataclass -class FitResult: - out: ModelResult - mask: np.array - fit_str: str - log: str - group: pd.core.groupby.generic.DataFrameGroupBy - uc_dics: dict - min_x: float - min_y: float - max_x: float - max_y: float - X: np.array - Y: np.array - Z: np.array - Z_sim: np.array - peak_slices: np.array - XY_slices: np.array - weights: np.array - mod: Model - - def check_shifts(self): - """Calculate difference between initial peak positions - and check whether they moved too much from original - position - - """ - pass - - -@dataclass -class FitPeaksResult: - df: pd.DataFrame - log: str - - -class FitPeaksResultDfRow(BaseModel): - fit_prefix: str - assignment: str - amp: float - amp_err: float - center_x: float - init_center_x: float - center_y: float - init_center_y: float - sigma_x: float - sigma_y: float - clustid: int - memcnt: int - plane: int - x_radius: float - y_radius: float - x_radius_ppm: float - y_radius_ppm: float - lineshape: str - aic: float - chisqr: float - redchi: float - residual_sum: float - height: float - height_err: float - fwhm_x: float - fwhm_y: float - center_x_ppm: float - center_y_ppm: float - init_center_x_ppm: float - init_center_y_ppm: float - sigma_x_ppm: float - sigma_y_ppm: float - fwhm_x_ppm: float - fwhm_y_ppm: float - fwhm_x_hz: float - fwhm_y_hz: float - jack_knife_sample_index: Optional[int] - - -class FitPeaksResultRowGLPV(FitPeaksResultDfRow): - fraction: float - - -class FitPeaksResultRowPVPV(FitPeaksResultDfRow): - fraction_x: float # for PV_PV model - fraction_y: float # for PV_PV model - - -class FitPeaksResultRowVoigt(FitPeaksResultDfRow): - gamma_x_ppm: float # for voigt - gamma_y_ppm: float # for voigt - - -def get_fit_peaks_result_validation_model(lineshape): - match lineshape: - case lineshape.V: - validation_model = FitPeaksResultRowVoigt - case lineshape.PV_PV: - validation_model = FitPeaksResultRowPVPV - case _: - validation_model = FitPeaksResultRowGLPV - return validation_model - - -def filter_peak_clusters_by_max_cluster_size(grouped_peak_clusters, max_cluster_size): - filtered_peak_clusters = grouped_peak_clusters.filter( - lambda x: len(x) <= max_cluster_size - ) - return filtered_peak_clusters - - -def set_parameters_to_fix_during_fit(first_plane_fit_params, to_fix): - # fix sigma center and fraction parameters - # could add an option to select params to fix - match to_fix: - case None | () | []: - float_str = "Floating all parameters" - parameter_set = first_plane_fit_params - case ["None"] | ["none"]: - float_str = "Floating all parameters" - parameter_set = first_plane_fit_params - case _: - float_str = f"Fixing parameters: {to_fix}" - parameter_set = fix_params(first_plane_fit_params, to_fix) - return parameter_set, float_str - - -def get_default_lineshape_param_names(lineshape: Lineshape): - match lineshape: - case Lineshape.PV | Lineshape.G | Lineshape.L: - param_names = Model(pvoigt2d).param_names - case Lineshape.V: - param_names = Model(voigt2d).param_names - case Lineshape.PV_PV: - param_names = Model(pv_pv).param_names - return param_names - - -def split_parameter_sets_by_peak( - default_param_names: List, params: List[Tuple[str, Parameter]] -): - """params is a list of tuples where the first element of each tuple is a - prefixed parameter name and the second element is the corresponding - Parameter object. This is created by calling .items() on a Parameters - object - """ - number_of_fitted_parameters = len(params) - number_of_default_params = len(default_param_names) - number_of_fitted_peaks = int(number_of_fitted_parameters / number_of_default_params) - split_param_items = [ - params[i : (i + number_of_default_params)] - for i in range(0, number_of_fitted_parameters, number_of_default_params) - ] - assert len(split_param_items) == number_of_fitted_peaks - return split_param_items - - -def create_parameter_dict(prefix, parameters: List[Tuple[str, Parameter]]): - parameter_dict = dict(prefix=prefix) - parameter_dict.update({k.replace(prefix, ""): v.value for k, v in parameters}) - parameter_dict.update( - {f"{k.replace(prefix,'')}_stderr": v.stderr for k, v in parameters} - ) - return parameter_dict - - -def get_prefix_from_parameter_names( - default_param_names: List, parameters: List[Tuple[str, Parameter]] -): - prefixes = [ - param_key_val[0].replace(default_param_name, "") - for param_key_val, default_param_name in zip(parameters, default_param_names) - ] - assert len(set(prefixes)) == 1 - return prefixes[0] - - -def unpack_fitted_parameters_for_lineshape( - lineshape: Lineshape, params: List[dict], plane_number: int -): - default_param_names = get_default_lineshape_param_names(lineshape) - split_parameter_names = split_parameter_sets_by_peak(default_param_names, params) - prefixes = [ - get_prefix_from_parameter_names(default_param_names, i) - for i in split_parameter_names - ] - unpacked_params = [] - for parameter_names, prefix in zip(split_parameter_names, prefixes): - parameter_dict = create_parameter_dict(prefix, parameter_names) - parameter_dict.update({"plane": plane_number}) - unpacked_params.append(parameter_dict) - return unpacked_params - - -def perform_initial_lineshape_fit_on_cluster_of_peaks( - fit_peak_cluster_input: FitPeakClusterInput, -) -> FitResult: - mod = fit_peak_cluster_input.mod - peak_slices = fit_peak_cluster_input.peak_slices - XY_slices = fit_peak_cluster_input.XY_slices - p_guess = fit_peak_cluster_input.p_guess - weights = fit_peak_cluster_input.weights - fit_method = fit_peak_cluster_input.fit_method - mask = fit_peak_cluster_input.mask - XY = fit_peak_cluster_input.XY - X, Y = XY - first_plane_data = fit_peak_cluster_input.first_plane_data - peak = fit_peak_cluster_input.last_peak - group = fit_peak_cluster_input.group - min_x = fit_peak_cluster_input.min_x - min_y = fit_peak_cluster_input.min_y - max_x = fit_peak_cluster_input.max_x - max_y = fit_peak_cluster_input.max_y - verbose = fit_peak_cluster_input.verbose - uc_dics = fit_peak_cluster_input.uc_dics - - out = mod.fit( - peak_slices, XY=XY_slices, params=p_guess, weights=weights, method=fit_method - ) - - if verbose: - console.print(out.fit_report(), style="bold") - - z_sim = mod.eval(XY=XY, params=out.params) - z_sim[~mask] = np.nan - z_plot = first_plane_data.copy() - z_plot[~mask] = np.nan - fit_str = "" - log = "" - - return FitResult( - out=out, - mask=mask, - fit_str=fit_str, - log=log, - group=group, - uc_dics=uc_dics, - min_x=min_x, - min_y=min_y, - max_x=max_x, - max_y=max_y, - X=X, - Y=Y, - Z=z_plot, - Z_sim=z_sim, - peak_slices=peak_slices, - XY_slices=XY_slices, - weights=weights, - mod=mod, - ) - - -def refit_peak_cluster_with_constraints( - fit_input: FitPeakClusterInput, fit_result: FitPeaksResult -): - fit_results = [] - for num, d in enumerate(fit_input.masked_plane_data): - plane_number = fit_input.plane_numbers[num] - fit_result.out.fit( - data=d, - params=fit_result.out.params, - weights=fit_result.weights, - ) - fit_results.extend( - unpack_fitted_parameters_for_lineshape( - fit_input.args.lineshape, - list(fit_result.out.params.items()), - plane_number, - ) - ) - return fit_results - - -def merge_unpacked_parameters_with_metadata(cluster_fit_df, group_of_peaks_df): - group_of_peaks_df["prefix"] = group_of_peaks_df.ASS.apply(to_prefix) - merged_cluster_fit_df = cluster_fit_df.merge(group_of_peaks_df, on="prefix") - return merged_cluster_fit_df - - -def update_cluster_df_with_fit_statistics(cluster_df, fit_result: ModelResult): - cluster_df["chisqr"] = fit_result.chisqr - cluster_df["redchi"] = fit_result.redchi - cluster_df["residual_sum"] = np.sum(fit_result.residual) - cluster_df["aic"] = fit_result.aic - cluster_df["bic"] = fit_result.bic - cluster_df["nfev"] = fit_result.nfev - cluster_df["ndata"] = fit_result.ndata - return cluster_df - - -def rename_columns_for_compatibility(df): - mapping = { - "amplitude": "amp", - "amplitude_stderr": "amp_err", - "X_AXIS": "init_center_x", - "Y_AXIS": "init_center_y", - "ASS": "assignment", - "MEMCNT": "memcnt", - "X_RADIUS": "x_radius", - "Y_RADIUS": "y_radius", - } - df = df.rename(columns=mapping) - return df - - -def add_vclist_to_df(fit_input: FitPeaksInput, df: pd.DataFrame): - vclist_data = fit_input.args.vclist_data - df["vclist"] = df.plane.apply(lambda x: vclist_data[x]) - return df - - -def prepare_group_of_peaks_for_fitting(clustid, group, fit_peaks_input: FitPeaksInput): - lineshape_function = get_lineshape_function(fit_peaks_input.args.lineshape) - - first_plane_data = fit_peaks_input.data[0] - mask, peak = make_mask_from_peak_cluster(group, first_plane_data) - - x_radius = group.X_RADIUS.max() - y_radius = group.Y_RADIUS.max() - - max_x, min_x = get_limits_for_axis_in_points( - group_axis_points=group.X_AXISf, mask_radius_in_points=x_radius - ) - max_y, min_y = get_limits_for_axis_in_points( - group_axis_points=group.Y_AXISf, mask_radius_in_points=y_radius - ) - max_x, min_x, max_y, min_y = deal_with_peaks_on_edge_of_spectrum( - fit_peaks_input.data.shape, max_x, min_x, max_y, min_y - ) - selected_data = select_reference_planes_using_indices( - fit_peaks_input.data, fit_peaks_input.args.reference_plane_indices - ).sum(axis=0) - mod, p_guess = make_models( - lineshape_function, - group, - selected_data, - lineshape=fit_peaks_input.args.lineshape, - xy_bounds=fit_peaks_input.args.xy_bounds, - ) - peak_slices = slice_peaks_from_data_using_mask(fit_peaks_input.data, mask) - peak_slices = select_reference_planes_using_indices( - peak_slices, fit_peaks_input.args.reference_plane_indices - ) - peak_slices = select_planes_above_threshold_from_masked_data( - peak_slices, fit_peaks_input.args.initial_fit_threshold - ) - peak_slices = peak_slices.sum(axis=0) - - XY = make_meshgrid(fit_peaks_input.data.shape) - X, Y = XY - - XY_slices = np.array([X.copy()[mask], Y.copy()[mask]]) - weights = 1.0 / np.array([fit_peaks_input.args.noise] * len(np.ravel(peak_slices))) - return FitPeakClusterInput( - args=fit_peaks_input.args, - data=fit_peaks_input.data, - config=fit_peaks_input.config, - plane_numbers=fit_peaks_input.plane_numbers, - clustid=clustid, - group=group, - last_peak=peak, - mask=mask, - mod=mod, - p_guess=p_guess, - XY=XY, - peak_slices=peak_slices, - XY_slices=XY_slices, - weights=weights, - fit_method=Config.fit_method, - first_plane_data=first_plane_data, - uc_dics=fit_peaks_input.args.uc_dics, - min_x=min_x, - min_y=min_y, - max_x=max_x, - max_y=max_y, - verbose=fit_peaks_input.args.verbose, - ) - - -def fit_cluster_of_peaks(data_for_fitting: FitPeakClusterInput) -> pd.DataFrame: - fit_result = perform_initial_lineshape_fit_on_cluster_of_peaks(data_for_fitting) - fit_result.out.params, float_str = set_parameters_to_fix_during_fit( - fit_result.out.params, data_for_fitting.args.to_fix - ) - fit_results = refit_peak_cluster_with_constraints(data_for_fitting, fit_result) - cluster_df = pd.DataFrame(fit_results) - cluster_df = update_cluster_df_with_fit_statistics(cluster_df, fit_result.out) - cluster_df["clustid"] = data_for_fitting.clustid - cluster_df = merge_unpacked_parameters_with_metadata( - cluster_df, data_for_fitting.group - ) - return cluster_df - - -def fit_peak_clusters(peaks: pd.DataFrame, fit_input: FitPeaksInput) -> FitPeaksResult: - """Fit set of peak clusters to lineshape model - - :param peaks: peaklist with generated by peakipy read or edit - :type peaks: pd.DataFrame - - :param fit_input: Data structure containing input parameters (args, config and NMR data) - :type fit_input: FitPeaksInput - - :returns: Data structure containing pd.DataFrame with the fitted results and a log - :rtype: FitPeaksResult - """ - peak_clusters = peaks.groupby("CLUSTID") - filtered_peaks = filter_peak_clusters_by_max_cluster_size( - peak_clusters, fit_input.args.max_cluster_size - ) - peak_clusters = filtered_peaks.groupby("CLUSTID") - out_str = "" - cluster_dfs = [] - for clustid, peak_cluster in peak_clusters: - data_for_fitting = prepare_group_of_peaks_for_fitting( - clustid, - peak_cluster, - fit_input, - ) - if fit_input.args.jack_knife_sample_errors: - cluster_df = jack_knife_sample_errors(data_for_fitting) - else: - cluster_df = fit_cluster_of_peaks(data_for_fitting) - cluster_dfs.append(cluster_df) - df = pd.concat(cluster_dfs, ignore_index=True) - - df["lineshape"] = fit_input.args.lineshape.value - - if fit_input.args.vclist: - df = add_vclist_to_df(fit_input, df) - df = rename_columns_for_compatibility(df) - return FitPeaksResult(df=df, log=out_str) - - -def jack_knife_sample_errors(fit_input: FitPeakClusterInput) -> pd.DataFrame: - peak_slices = fit_input.peak_slices.copy() - XY_slices = fit_input.XY_slices.copy() - weights = fit_input.weights.copy() - masked_plane_data = fit_input.masked_plane_data.copy() - jk_results = [] - # first fit without jackknife - jk_result = fit_cluster_of_peaks(data_for_fitting=fit_input) - jk_result["jack_knife_sample_index"] = 0 - jk_results.append(jk_result) - for i in np.arange(0, len(peak_slices), 10, dtype=int): - fit_input.peak_slices = np.delete(peak_slices, i, None) - XY_slices_0 = np.delete(XY_slices[0], i, None) - XY_slices_1 = np.delete(XY_slices[1], i, None) - fit_input.XY_slices = np.array([XY_slices_0, XY_slices_1]) - fit_input.weights = np.delete(weights, i, None) - fit_input.masked_plane_data = np.delete(masked_plane_data, i, axis=1) - jk_result = fit_cluster_of_peaks(data_for_fitting=fit_input) - jk_result["jack_knife_sample_index"] = i + 1 - jk_results.append(jk_result) - return pd.concat(jk_results, ignore_index=True) diff --git a/peakipy/cli/spec.py b/peakipy/cli/spec.py deleted file mode 100644 index 4ffbdf6b..00000000 --- a/peakipy/cli/spec.py +++ /dev/null @@ -1,92 +0,0 @@ -#!/usr/bin/env python3 -""" - Usage: spec.py - spec.py make - - Plot NMRPipe spectra overlays using nmrglue and matplotlib. This is my attempt to make a general script for - plotting NMR data. - - - Below is an example yaml file for input - - # This first block is global parameters which can be overridden by adding the desired argument - # to your list of spectra. One exception is "colors" which if set in global params overrides the - # color option set for individual spectra as the colors will now cycle through the chosen matplotlib - # colormap - cs: 10e5 # contour start - contour_num: 10 # number of contours - contour_factor: 1.2 # contour factor - colors: Set1 # must be matplotlib.cm colormap - - outname: ["overlay.pdf","overlay.png"] # either single value or list of output names - - # Here is where your list of spectra to plot goes - spectra: - - - fname: test.ft2 - label: write legend here - contour_num: 1 - linewidths: 1 - - Options: - -h --help - -v --version - - - Dependencies: - - -- python3 - -- matplotlib, pyyaml, numpy, nmrglue, pandas and docopt - - - peakipy - deconvolute overlapping NMR peaks - Copyright (C) 2019 Jacob Peter Brady - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see . - -""" - -yaml_file = """ -########################################################################################################## -# This first block is global parameters which can be overridden by adding the desired argument # -# to your list of spectra. One exception is "colors" which if set in global params overrides the # -# color option set for individual spectra as the colors will now cycle through the chosen matplotlib # -# colormap # -########################################################################################################## - -cs: 10e5 # contour start -contour_num: 10 # number of contours -contour_factor: 1.2 # contour factor -colors: Set1 # must be matplotlib.cm colormap - -outname: ["overlay.pdf","overlay.png"] # either single value or list of output names -ncol: 1 # tells matplotlib how many columns to give the figure legend - if not set defaults to 2 - -# Here is where your list of spectra to plot goes -spectra: - - - fname: test.ft2 - label: some information - contour_num: 1 - linewidths: 1 -""" - - -def onpick(event): - thisline = event.artist - xdata = thisline.get_xdata() - ydata = thisline.get_ydata() - ind = event.ind - points = tuple(zip(xdata[ind], ydata[ind])) - print("onpick points:", points) diff --git a/peakipy/core.py b/peakipy/core.py deleted file mode 100644 index d0473a61..00000000 --- a/peakipy/core.py +++ /dev/null @@ -1,1950 +0,0 @@ -""" - - peakipy - deconvolute overlapping NMR peaks - Copyright (C) 2019 Jacob Peter Brady - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see . - - -""" - -import sys -import json -from datetime import datetime -from pathlib import Path -from typing import List -from enum import Enum -from dataclasses import dataclass, field - -import numpy as np -import nmrglue as ng -import pandas as pd -import textwrap -from rich import print -from rich.table import Table -from rich.console import Console - -from numpy import sqrt, log, pi, exp, finfo - -from lmfit import Model -from scipy.special import wofz - -from bokeh.palettes import Category20 -from scipy import ndimage -from skimage.morphology import square, binary_closing, disk, rectangle -from skimage.filters import threshold_otsu - -console = Console() -# constants -log2 = log(2) -π = pi -tiny = finfo(float).eps - - -class StrucEl(str, Enum): - square = "square" - disk = "disk" - rectangle = "rectangle" - mask_method = "mask_method" - - -class PeaklistFormat(str, Enum): - a2 = "a2" - a3 = "a3" - sparky = "sparky" - pipe = "pipe" - peakipy = "peakipy" - - -class OutFmt(str, Enum): - csv = "csv" - pkl = "pkl" - - -class Lineshape(str, Enum): - PV = "PV" - V = "V" - G = "G" - L = "L" - PV_PV = "PV_PV" - G_L = "G_L" - PV_G = "PV_G" - PV_L = "PV_L" - - -def gaussian(x, center=0.0, sigma=1.0): - r"""1-dimensional Gaussian function. - - gaussian(x, center, sigma) = - (1/(s2pi*sigma)) * exp(-(1.0*x-center)**2 / (2*sigma**2)) - - :math:`\\frac{1}{ \sqrt{2\pi} } exp \left( \\frac{-(x-center)^2}{2 \sigma^2} \\right)` - - :param x: x - :param center: center - :param sigma: sigma - :type x: numpy.array - :type center: float - :type sigma: float - - :return: 1-dimensional Gaussian - :rtype: numpy.array - - """ - return (1.0 / max(tiny, (sqrt(2 * π) * sigma))) * exp( - -((1.0 * x - center) ** 2) / max(tiny, (2 * sigma**2)) - ) - - -def lorentzian(x, center=0.0, sigma=1.0): - r"""1-dimensional Lorentzian function. - - lorentzian(x, center, sigma) = - (1/(1 + ((1.0*x-center)/sigma)**2)) / (pi*sigma) - - :math:`\\frac{1}{ 1+ \left( \\frac{x-center}{\sigma}\\right)^2} / (\pi\sigma)` - - :param x: x - :param center: center - :param sigma: sigma - :type x: numpy.array - :type center: float - :type sigma: float - - :return: 1-dimensional Lorenztian - :rtype: numpy.array - - """ - return (1.0 / (1 + ((1.0 * x - center) / max(tiny, sigma)) ** 2)) / max( - tiny, (π * sigma) - ) - - -def voigt(x, center=0.0, sigma=1.0, gamma=None): - r"""Return a 1-dimensional Voigt function. - - voigt(x, center, sigma, gamma) = - amplitude*wofz(z).real / (sigma*sqrt(2.0 * π)) - - :math:`V(x,\sigma,\gamma) = (\\frac{Re[\omega(z)]}{\sigma \sqrt{2\pi}})` - - :math:`z=\\frac{x+i\gamma}{\sigma\sqrt{2}}` - - see Voigt_ wiki - - .. _Voigt: https://en.wikipedia.org/wiki/Voigt_profile - - - :param x: x values - :type x: numpy array 1d - :param center: center of lineshape in points - :type center: float - :param sigma: sigma of gaussian - :type sigma: float - :param gamma: gamma of lorentzian - :type gamma: float - - :returns: Voigt lineshape - :rtype: numpy.array - - """ - if gamma is None: - gamma = sigma - - z = (x - center + 1j * gamma) / max(tiny, (sigma * sqrt(2.0))) - return wofz(z).real / max(tiny, (sigma * sqrt(2.0 * π))) - - -def pseudo_voigt(x, center=0.0, sigma=1.0, fraction=0.5): - r"""1-dimensional Pseudo-voigt function - - Superposition of Gaussian and Lorentzian function - - :math:`(1-\phi) G(x,center,\sigma_g) + \phi L(x, center, \sigma)` - - Where :math:`\phi` is the fraction of Lorentzian lineshape and :math:`G` and :math:`L` are Gaussian and - Lorentzian functions, respectively. - - :param x: data - :type x: numpy.array - :param center: center of peak - :type center: float - :param sigma: sigma of lineshape - :type sigma: float - :param fraction: fraction of lorentzian lineshape (between 0 and 1) - :type fraction: float - - :return: pseudo-voigt function - :rtype: numpy.array - - """ - sigma_g = sigma / sqrt(2 * log2) - pv = (1 - fraction) * gaussian(x, center, sigma_g) + fraction * lorentzian( - x, center, sigma - ) - return pv - - -def pvoigt2d( - XY, - amplitude=1.0, - center_x=0.5, - center_y=0.5, - sigma_x=1.0, - sigma_y=1.0, - fraction=0.5, -): - r"""2D pseudo-voigt model - - :math:`(1-fraction) G(x,center,\sigma_{gx}) + (fraction) L(x, center, \sigma_x) * (1-fraction) G(y,center,\sigma_{gy}) + (fraction) L(y, center, \sigma_y)` - - :param XY: meshgrid of X and Y coordinates [X,Y] each with shape Z - :type XY: numpy.array - - :param amplitude: amplitude of peak - :type amplitude: float - - :param center_x: center of peak in x - :type center_x: float - - :param center_y: center of peak in x - :type center_y: float - - :param sigma_x: sigma of lineshape in x - :type sigma_x: float - - :param sigma_y: sigma of lineshape in y - :type sigma_y: float - - :param fraction: fraction of lorentzian lineshape (between 0 and 1) - :type fraction: float - - :return: flattened array of Z values (use Z.reshape(X.shape) for recovery) - :rtype: numpy.array - - """ - x, y = XY - pv_x = pseudo_voigt(x, center_x, sigma_x, fraction) - pv_y = pseudo_voigt(y, center_y, sigma_y, fraction) - return amplitude * pv_x * pv_y - - -def pv_l( - XY, - amplitude=1.0, - center_x=0.5, - center_y=0.5, - sigma_x=1.0, - sigma_y=1.0, - fraction=0.5, -): - """2D lineshape model with pseudo-voigt in x and lorentzian in y - - Arguments - ========= - - -- XY: meshgrid of X and Y coordinates [X,Y] each with shape Z - -- amplitude: peak amplitude (gaussian and lorentzian) - -- center_x: position of peak in x - -- center_y: position of peak in y - -- sigma_x: linewidth in x - -- sigma_y: linewidth in y - -- fraction: fraction of lorentzian in fit - - Returns - ======= - - -- flattened array of Z values (use Z.reshape(X.shape) for recovery) - - """ - - x, y = XY - pv_x = pseudo_voigt(x, center_x, sigma_x, fraction) - pv_y = pseudo_voigt(y, center_y, sigma_y, 1.0) # lorentzian - return amplitude * pv_x * pv_y - - -def pv_g( - XY, - amplitude=1.0, - center_x=0.5, - center_y=0.5, - sigma_x=1.0, - sigma_y=1.0, - fraction=0.5, -): - """2D lineshape model with pseudo-voigt in x and gaussian in y - - Arguments - --------- - - -- XY: meshgrid of X and Y coordinates [X,Y] each with shape Z - -- amplitude: peak amplitude (gaussian and lorentzian) - -- center_x: position of peak in x - -- center_y: position of peak in y - -- sigma_x: linewidth in x - -- sigma_y: linewidth in y - -- fraction: fraction of lorentzian in fit - - Returns - ------- - - -- flattened array of Z values (use Z.reshape(X.shape) for recovery) - - """ - x, y = XY - pv_x = pseudo_voigt(x, center_x, sigma_x, fraction) - pv_y = pseudo_voigt(y, center_y, sigma_y, 0.0) # gaussian - return amplitude * pv_x * pv_y - - -def pv_pv( - XY, - amplitude=1.0, - center_x=0.5, - center_y=0.5, - sigma_x=1.0, - sigma_y=1.0, - fraction_x=0.5, - fraction_y=0.5, -): - """2D lineshape model with pseudo-voigt in x and pseudo-voigt in y - i.e. fraction_x and fraction_y params - - Arguments - ========= - - -- XY: meshgrid of X and Y coordinates [X,Y] each with shape Z - -- amplitude: peak amplitude (gaussian and lorentzian) - -- center_x: position of peak in x - -- center_y: position of peak in y - -- sigma_x: linewidth in x - -- sigma_y: linewidth in y - -- fraction_x: fraction of lorentzian in x - -- fraction_y: fraction of lorentzian in y - - Returns - ======= - - -- flattened array of Z values (use Z.reshape(X.shape) for recovery) - - """ - - x, y = XY - pv_x = pseudo_voigt(x, center_x, sigma_x, fraction_x) - pv_y = pseudo_voigt(y, center_y, sigma_y, fraction_y) - return amplitude * pv_x * pv_y - - -def gaussian_lorentzian( - XY, - amplitude=1.0, - center_x=0.5, - center_y=0.5, - sigma_x=1.0, - sigma_y=1.0, - fraction=0.5, -): - """2D lineshape model with gaussian in x and lorentzian in y - - Arguments - ========= - - -- XY: meshgrid of X and Y coordinates [X,Y] each with shape Z - -- amplitude: peak amplitude (gaussian and lorentzian) - -- center_x: position of peak in x - -- center_y: position of peak in y - -- sigma_x: linewidth in x - -- sigma_y: linewidth in y - -- fraction: fraction of lorentzian in fit - - Returns - ======= - - -- flattened array of Z values (use Z.reshape(X.shape) for recovery) - - """ - x, y = XY - pv_x = pseudo_voigt(x, center_x, sigma_x, 0.0) # gaussian - pv_y = pseudo_voigt(y, center_y, sigma_y, 1.0) # lorentzian - return amplitude * pv_x * pv_y - - -def voigt2d( - XY, - amplitude=1.0, - center_x=0.5, - center_y=0.5, - sigma_x=1.0, - sigma_y=1.0, - gamma_x=1.0, - gamma_y=1.0, - fraction=0.5, -): - fraction = 0.5 - gamma_x = None - gamma_y = None - x, y = XY - voigt_x = voigt(x, center_x, sigma_x, gamma_x) - voigt_y = voigt(y, center_y, sigma_y, gamma_y) - return amplitude * voigt_x * voigt_y - - -def make_mask(data, c_x, c_y, r_x, r_y): - """Create and elliptical mask - - Generate an elliptical boolean mask with center c_x/c_y in points - with radii r_x and r_y. Used to generate fit mask - - :param data: 2D array - :type data: np.array - - :param c_x: x center - :type c_x: float - - :param c_y: y center - :type c_y: float - - :param r_x: radius in x - :type r_x: float - - :param r_y: radius in y - :type r_y: float - - :return: boolean mask of data.shape - :rtype: numpy.array - - """ - a, b = c_y, c_x - n_y, n_x = data.shape - y, x = np.ogrid[-a : n_y - a, -b : n_x - b] - mask = x**2.0 / r_x**2.0 + y**2.0 / r_y**2.0 <= 1.0 - return mask - - -def rmsd(residuals): - return np.sqrt(np.sum(residuals**2.0) / len(residuals)) - - -def fix_params(params, to_fix): - """Set parameters to fix - - - :param params: lmfit parameters - :type params: lmfit.Parameters - - :param to_fix: list of parameter name to fix - :type to_fix: list - - :return: updated parameter object - :rtype: lmfit.Parameters - - """ - for k in params: - for p in to_fix: - if p in k: - params[k].vary = False - - return params - - -def get_params(params, name): - ps = [] - ps_err = [] - names = [] - prefixes = [] - for k in params: - if name in k: - ps.append(params[k].value) - ps_err.append(params[k].stderr) - names.append(k) - prefixes.append(k.split(name)[0]) - return ps, ps_err, names, prefixes - - -@dataclass -class PeakLimits: - """Given a peak position and linewidth in points determine - the limits based on the data - - Arguments - --------- - peak: pd.DataFrame - peak is a row from a pandas dataframe - data: np.array - 2D numpy array - """ - - peak: pd.DataFrame - data: np.array - min_x: int = field(init=False) - max_x: int = field(init=False) - min_y: int = field(init=False) - max_y: int = field(init=False) - - def __post_init__(self): - assert self.peak.Y_AXIS <= self.data.shape[0] - assert self.peak.X_AXIS <= self.data.shape[1] - self.max_y = int(np.ceil(self.peak.Y_AXIS + self.peak.YW)) + 1 - if self.max_y > self.data.shape[0]: - self.max_y = self.data.shape[0] - self.max_x = int(np.ceil(self.peak.X_AXIS + self.peak.XW)) + 1 - if self.max_x > self.data.shape[1]: - self.max_x = self.data.shape[1] - - self.min_y = int(self.peak.Y_AXIS - self.peak.YW) - if self.min_y < 0: - self.min_y = 0 - self.min_x = int(self.peak.X_AXIS - self.peak.XW) - if self.min_x < 0: - self.min_x = 0 - - -def estimate_amplitude(peak, data): - assert len(data.shape) == 2 - limits = PeakLimits(peak, data) - amplitude_est = data[limits.min_y : limits.max_y, limits.min_x : limits.max_x].sum() - return amplitude_est - - -def make_param_dict(peaks, data, lineshape: Lineshape = Lineshape.PV): - """Make dict of parameter names using prefix""" - - param_dict = {} - - for _, peak in peaks.iterrows(): - str_form = lambda x: "%s%s" % (to_prefix(peak.ASS), x) - # using exact value of points (i.e decimal) - param_dict[str_form("center_x")] = peak.X_AXISf - param_dict[str_form("center_y")] = peak.Y_AXISf - # estimate peak volume - amplitude_est = estimate_amplitude(peak, data) - param_dict[str_form("amplitude")] = amplitude_est - # sigma linewidth esimate - param_dict[str_form("sigma_x")] = peak.XW / 2.0 - param_dict[str_form("sigma_y")] = peak.YW / 2.0 - - match lineshape: - case lineshape.V: - #  Voigt G sigma from linewidth esimate - param_dict[str_form("sigma_x")] = peak.XW / ( - 2.0 * sqrt(2.0 * log2) - ) # 3.6013 - param_dict[str_form("sigma_y")] = peak.YW / ( - 2.0 * sqrt(2.0 * log2) - ) # 3.6013 - #  Voigt L gamma from linewidth esimate - param_dict[str_form("gamma_x")] = peak.XW / 2.0 - param_dict[str_form("gamma_y")] = peak.YW / 2.0 - # height - # add height here - - case lineshape.G: - param_dict[str_form("fraction")] = 0.0 - case lineshape.L: - param_dict[str_form("fraction")] = 1.0 - case lineshape.PV_PV: - param_dict[str_form("fraction_x")] = 0.5 - param_dict[str_form("fraction_y")] = 0.5 - case _: - param_dict[str_form("fraction")] = 0.5 - - return param_dict - - -def to_prefix(x): - """ - Peak assignments with characters that are not compatible lmfit model naming - are converted to lmfit "safe" names. - - :param x: Peak assignment to be used as prefix for lmfit model - :type x: str - - :returns: lmfit model prefix (_Peak_assignment_) - :rtype: str - - """ - # must be string - if type(x) != str: - x = str(x) - - prefix = "_" + x - to_replace = [ - [".", "_"], - [" ", ""], - ["{", "_"], - ["}", "_"], - ["[", "_"], - ["]", "_"], - ["-", ""], - ["/", "or"], - ["?", "maybe"], - ["\\", ""], - ["(", "_"], - [")", "_"], - ["@", "_at_"], - ] - for p in to_replace: - prefix = prefix.replace(*p) - return prefix + "_" - - -def make_models( - model, - peaks, - data, - lineshape: Lineshape = Lineshape.PV, - xy_bounds=None, -): - """Make composite models for multiple peaks - - :param model: lineshape function - :type model: function - - :param peaks: instance of pandas.df.groupby("CLUSTID") - :type peaks: pandas.df.groupby("CLUSTID") - - :param data: NMR data - :type data: numpy.array - - :param lineshape: lineshape to use for fit (PV/G/L/PV_PV) - :type lineshape: str - - :param xy_bounds: bounds for peak centers (+/-x, +/-y) - :type xy_bounds: tuple - - :return mod: Composite lmfit model containing all peaks - :rtype mod: lmfit.CompositeModel - - :return p_guess: params for composite model with starting values - :rtype p_guess: lmfit.Parameters - - """ - if len(peaks) == 1: - # make model for first peak - mod = Model(model, prefix="%s" % to_prefix(peaks.ASS.iloc[0])) - # add parameters - param_dict = make_param_dict( - peaks, - data, - lineshape=lineshape, - ) - p_guess = mod.make_params(**param_dict) - - elif len(peaks) > 1: - # make model for first peak - first_peak, *remaining_peaks = peaks.iterrows() - mod = Model(model, prefix="%s" % to_prefix(first_peak[1].ASS)) - for _, peak in remaining_peaks: - mod += Model(model, prefix="%s" % to_prefix(peak.ASS)) - - param_dict = make_param_dict( - peaks, - data, - lineshape=lineshape, - ) - p_guess = mod.make_params(**param_dict) - # add Peak params to p_guess - - update_params(p_guess, param_dict, lineshape=lineshape, xy_bounds=xy_bounds) - - return mod, p_guess - - -def update_params( - params, param_dict, lineshape: Lineshape = Lineshape.PV, xy_bounds=None -): - """Update lmfit parameters with values from Peak - - :param params: lmfit parameters - :type params: lmfit.Parameters object - :param param_dict: parameters corresponding to each peak in fit - :type param_dict: dict - :param lineshape: Lineshape (PV, G, L, PV_PV etc.) - :type lineshape: Lineshape - :param xy_bounds: bounds on xy peak positions - :type xy_bounds: tuple - - :returns: None - :rtype: None - - ToDo - -- deal with boundaries - -- currently positions in points - - """ - for k, v in param_dict.items(): - params[k].value = v - # print("update", k, v) - if "center" in k: - if xy_bounds == None: - # no bounds set - pass - else: - if "center_x" in k: - # set x bounds - x_bound = xy_bounds[0] - params[k].min = v - x_bound - params[k].max = v + x_bound - elif "center_y" in k: - # set y bounds - y_bound = xy_bounds[1] - params[k].min = v - y_bound - params[k].max = v + y_bound - # pass - # print( - # "setting limit of %s, min = %.3e, max = %.3e" - # % (k, params[k].min, params[k].max) - # ) - elif "sigma" in k: - params[k].min = 0.0 - params[k].max = 1e4 - - elif "gamma" in k: - params[k].min = 0.0 - params[k].max = 1e4 - # print( - # "setting limit of %s, min = %.3e, max = %.3e" - # % (k, params[k].min, params[k].max) - # ) - elif "fraction" in k: - # fix weighting between 0 and 1 - params[k].min = 0.0 - params[k].max = 1.0 - - #  fix fraction of G or L - match lineshape: - case lineshape.G | lineshape.L: - params[k].vary = False - case lineshape.PV | lineshape.PV_PV: - params[k].vary = True - case _: - pass - - # return params - - -def run_log(log_name="run_log.txt"): - """Write log file containing time script was run and with which arguments""" - with open(log_name, "a") as log: - sys_argv = sys.argv - sys_argv[0] = Path(sys_argv[0]).name - run_args = " ".join(sys_argv) - time_stamp = datetime.now() - time_stamp = time_stamp.strftime("%A %d %B %Y at %H:%M") - log.write(f"# Script run on {time_stamp}:\n{run_args}\n") - - -def df_to_rich_table(df, title: str, columns: List[str], styles: str): - """Print dataframe using rich library - - Parameters - ---------- - df : pandas.DataFrame - title : str - title of table - columns : List[str] - list of column names (must be in df) - styles : List[str] - list of styles in same order as columns - """ - table = Table(title=title) - for col, style in zip(columns, styles): - table.add_column(col, style=style) - for _, row in df.iterrows(): - row = row[columns].values - str_row = [] - for i in row: - match i: - case str(): - str_row.append(f"{i}") - case float() if i > 1e5: - str_row.append(f"{i:.1e}") - case float(): - str_row.append(f"{i:.3f}") - case bool(): - str_row.append(f"{i}") - case int(): - str_row.append(f"{i}") - table.add_row(*str_row) - return table - - -def make_mask_from_peak_cluster(group, data): - mask = np.zeros(data.shape, dtype=bool) - for _, peak in group.iterrows(): - mask += make_mask( - data, peak.X_AXISf, peak.Y_AXISf, peak.X_RADIUS, peak.Y_RADIUS - ) - return mask, peak - - -def select_reference_planes_using_indices(data, indices: List[int]): - n_planes = data.shape[0] - if indices == []: - return data - - max_index = max(indices) - min_index = min(indices) - - if max_index >= n_planes: - raise IndexError( - f"Your data has {n_planes}. You selected plane {max_index} (allowed indices between 0 and {n_planes-1})" - ) - elif min_index < (-1 * n_planes): - raise IndexError( - f"Your data has {n_planes}. You selected plane {min_index} (allowed indices between -{n_planes} and {n_planes-1})" - ) - else: - data = data[indices] - return data - - -def select_planes_above_threshold_from_masked_data(data, threshold=None): - """This function returns planes with data above the threshold. - - It currently uses absolute intensity values. - Negative thresholds just result in return of the orignal data. - - """ - if threshold == None: - selected_data = data - else: - selected_data = data[np.abs(data).max(axis=1) > threshold] - - if selected_data.shape[0] == 0: - selected_data = data - - return selected_data - - -def get_lineshape_function(lineshape: Lineshape): - match lineshape: - case lineshape.PV | lineshape.G | lineshape.L: - lineshape_function = pvoigt2d - case lineshape.V: - lineshape_function = voigt2d - case lineshape.PV_PV: - lineshape_function = pv_pv - case lineshape.G_L: - lineshape_function = gaussian_lorentzian - case lineshape.PV_G: - lineshape_function = pv_g - case lineshape.PV_L: - lineshape_function = pv_l - case _: - raise Exception("No lineshape was selected!") - return lineshape_function - - -def slice_peaks_from_data_using_mask(data, mask): - peak_slices = np.array([d[mask] for d in data]) - return peak_slices - - -def get_limits_for_axis_in_points(group_axis_points, mask_radius_in_points): - max_point, min_point = ( - int(np.ceil(max(group_axis_points) + mask_radius_in_points + 1)), - int(np.floor(min(group_axis_points) - mask_radius_in_points)), - ) - return max_point, min_point - - -def deal_with_peaks_on_edge_of_spectrum(data_shape, max_x, min_x, max_y, min_y): - if min_y < 0: - min_y = 0 - - if min_x < 0: - min_x = 0 - - if max_y > data_shape[-2]: - max_y = data_shape[-2] - - if max_x > data_shape[-1]: - max_x = data_shape[-1] - return max_x, min_x, max_y, min_y - - -def make_meshgrid(data_shape): - # must be a better way to make the meshgrid - x = np.arange(data_shape[-1]) - y = np.arange(data_shape[-2]) - XY = np.meshgrid(x, y) - return XY - - -class Pseudo3D: - """Read dic, data from NMRGlue and dims from input to create a Pseudo3D dataset - - :param dic: from nmrglue.pipe.read - :type dic: dict - - :param data: data from nmrglue.pipe.read - :type data: numpy.array - - :param dims: dimension order i.e [0,1,2] where 0 = planes, 1 = f1, 2 = f2 - :type dims: list - """ - - def __init__(self, dic, data, dims): - # check dimensions - self._udic = ng.pipe.guess_udic(dic, data) - self._ndim = self._udic["ndim"] - - if self._ndim == 1: - err = f"""[red] - ########################################## - NMR Data should be either 2D or 3D - ########################################## - [/red]""" - # raise TypeError(err) - sys.exit(err) - - # check that spectrum has correct number of dims - elif self._ndim != len(dims): - err = f"""[red] - ################################################################# - Your spectrum has {self._ndim} dimensions with shape {data.shape} - but you have given a dimension order of {dims}... - ################################################################# - [/red]""" - # raise ValueError(err) - sys.exit(err) - - elif (self._ndim == 2) and (len(dims) == 2): - self._f1_dim, self._f2_dim = dims - self._planes = 0 - self._uc_f1 = ng.pipe.make_uc(dic, data, dim=self._f1_dim) - self._uc_f2 = ng.pipe.make_uc(dic, data, dim=self._f2_dim) - # make data pseudo3d - self._data = data.reshape((1, data.shape[0], data.shape[1])) - self._dims = [self._planes, self._f1_dim + 1, self._f2_dim + 1] - - else: - self._planes, self._f1_dim, self._f2_dim = dims - self._dims = dims - self._data = data - # make unit conversion dicts - self._uc_f2 = ng.pipe.make_uc(dic, data, dim=self._f2_dim) - self._uc_f1 = ng.pipe.make_uc(dic, data, dim=self._f1_dim) - - #  rearrange data if dims not in standard order - if self._dims != [0, 1, 2]: - # np.argsort returns indices of array for order 0,1,2 to transpose data correctly - # self._dims = np.argsort(self._dims) - self._data = np.transpose(data, self._dims) - - self._dic = dic - - self._f1_label = self._udic[self._f1_dim]["label"] - self._f2_label = self._udic[self._f2_dim]["label"] - - @property - def uc_f1(self): - """Return unit conversion dict for F1""" - return self._uc_f1 - - @property - def uc_f2(self): - """Return unit conversion dict for F2""" - return self._uc_f2 - - @property - def dims(self): - """Return dimension order""" - return self._dims - - @property - def data(self): - """Return array containing data""" - return self._data - - @data.setter - def data(self, data): - self._data = data - - @property - def dic(self): - return self._dic - - @property - def udic(self): - return self._udic - - @property - def ndim(self): - return self._ndim - - @property - def f1_label(self): - # dim label - return self._f1_label - - @property - def f2_label(self): - # dim label - return self._f2_label - - @property - def planes(self): - return self.dims[0] - - @property - def n_planes(self): - return self.data.shape[self.planes] - - @property - def f1(self): - return self.dims[1] - - @property - def f2(self): - return self.dims[2] - - # size of f1 and f2 in points - @property - def f2_size(self): - """Return size of f2 dimension in points""" - return self._udic[self._f2_dim]["size"] - - @property - def f1_size(self): - """Return size of f1 dimension in points""" - return self._udic[self._f1_dim]["size"] - - # points per ppm - @property - def pt_per_ppm_f1(self): - return self.f1_size / ( - self._udic[self._f1_dim]["sw"] / self._udic[self._f1_dim]["obs"] - ) - - @property - def pt_per_ppm_f2(self): - return self.f2_size / ( - self._udic[self._f2_dim]["sw"] / self._udic[self._f2_dim]["obs"] - ) - - # points per hz - @property - def pt_per_hz_f1(self): - return self.f1_size / self._udic[self._f1_dim]["sw"] - - @property - def pt_per_hz_f2(self): - return self.f2_size / self._udic[self._f2_dim]["sw"] - - # hz per point - @property - def hz_per_pt_f1(self): - return 1.0 / self.pt_per_hz_f1 - - @property - def hz_per_pt_f2(self): - return 1.0 / self.pt_per_hz_f2 - - # ppm per point - @property - def ppm_per_pt_f1(self): - return 1.0 / self.pt_per_ppm_f1 - - @property - def ppm_per_pt_f2(self): - return 1.0 / self.pt_per_ppm_f2 - - # get ppm limits for ppm scales - @property - def f2_ppm_scale(self): - return self.uc_f2.ppm_scale() - - @property - def f1_ppm_scale(self): - return self.uc_f1.ppm_scale() - - @property - def f2_ppm_limits(self): - return self.uc_f2.ppm_limits() - - @property - def f1_ppm_limits(self): - return self.uc_f1.ppm_limits() - - @property - def f1_ppm_max(self): - return max(self.f1_ppm_limits) - - @property - def f1_ppm_min(self): - return min(self.f1_ppm_limits) - - @property - def f2_ppm_max(self): - return max(self.f2_ppm_limits) - - @property - def f2_ppm_min(self): - return min(self.f2_ppm_limits) - - @property - def f2_ppm_0(self): - return self.f2_ppm_limits[0] - - @property - def f2_ppm_1(self): - return self.f2_ppm_limits[1] - - @property - def f1_ppm_0(self): - return self.f1_ppm_limits[0] - - @property - def f1_ppm_1(self): - return self.f1_ppm_limits[1] - - -class UnknownFormat(Exception): - pass - - -class Peaklist(Pseudo3D): - """Read analysis, sparky or NMRPipe peak list and convert to NMRPipe-ish format also find peak clusters - - Parameters - ---------- - path : path-like or str - path to peaklist - data_path : ndarray - NMRPipe format data - fmt : str - a2|a3|sparky|pipe - dims: list - [planes,y,x] - radii: list - [x,y] Mask radii in ppm - - - Methods - ------- - - clusters : - mask_method : - adaptive_clusters : - - Returns - ------- - df : pandas DataFrame - dataframe containing peaklist - - """ - - def __init__( - self, - path, - data_path, - fmt: PeaklistFormat = PeaklistFormat.a2, - dims=[0, 1, 2], - radii=[0.04, 0.4], - posF1="Position F2", - posF2="Position F1", - verbose=False, - ): - dic, data = ng.pipe.read(data_path) - Pseudo3D.__init__(self, dic, data, dims) - self.fmt = fmt - self.peaklist_path = path - self.data_path = data_path - self.verbose = verbose - self._radii = radii - self._thres = None - if self.verbose: - print( - "Points per hz f1 = %.3f, f2 = %.3f" - % (self.pt_per_hz_f1, self.pt_per_hz_f2) - ) - - self._analysis_to_pipe_dic = { - "#": "INDEX", - "Position F1": "X_PPM", - "Position F2": "Y_PPM", - "Line Width F1 (Hz)": "XW_HZ", - "Line Width F2 (Hz)": "YW_HZ", - "Height": "HEIGHT", - "Volume": "VOL", - } - self._assign_to_pipe_dic = { - "#": "INDEX", - "Pos F1": "X_PPM", - "Pos F2": "Y_PPM", - "LW F1 (Hz)": "XW_HZ", - "LW F2 (Hz)": "YW_HZ", - "Height": "HEIGHT", - "Volume": "VOL", - } - - self._sparky_to_pipe_dic = { - "index": "INDEX", - "w1": "X_PPM", - "w2": "Y_PPM", - "lw1 (hz)": "XW_HZ", - "lw2 (hz)": "YW_HZ", - "Height": "HEIGHT", - "Volume": "VOL", - "Assignment": "ASS", - } - - self._analysis_to_pipe_dic[posF1] = "Y_PPM" - self._analysis_to_pipe_dic[posF2] = "X_PPM" - - self._df = self.read_peaklist() - - def read_peaklist(self): - match self.fmt: - case self.fmt.a2: - self._df = self._read_analysis() - - case self.fmt.a3: - self._df = self._read_assign() - - case self.fmt.sparky: - self._df = self._read_sparky() - - case self.fmt.pipe: - self._df = self._read_pipe() - - case _: - raise UnknownFormat("I don't know this format: {self.fmt}") - - return self._df - - @property - def df(self): - return self._df - - @df.setter - def df(self, df): - self._df = df - return self._df - - @property - def radii(self): - return self._radii - - @property - def f2_radius(self): - """radius for fitting mask in f2""" - return self.radii[0] - - @property - def f1_radius(self): - """radius for fitting mask in f1""" - return self.radii[1] - - @property - def analysis_to_pipe_dic(self): - return self._analysis_to_pipe_dic - - @property - def assign_to_pipe_dic(self): - return self._assign_to_pipe_dic - - @property - def sparky_to_pipe_dic(self): - return self._sparky_to_pipe_dic - - @property - def thres(self): - if self._thres == None: - self._thres = abs(threshold_otsu(self.data[0])) - return self._thres - else: - return self._thres - - def update_df(self): - # int point value - self.df["X_AXIS"] = self.df.X_PPM.apply(lambda x: self.uc_f2(x, "ppm")) - self.df["Y_AXIS"] = self.df.Y_PPM.apply(lambda x: self.uc_f1(x, "ppm")) - # decimal point value - self.df["X_AXISf"] = self.df.X_PPM.apply(lambda x: self.uc_f2.f(x, "ppm")) - self.df["Y_AXISf"] = self.df.Y_PPM.apply(lambda x: self.uc_f1.f(x, "ppm")) - # in case of missing values (should estimate though) - self.df["XW_HZ"] = self.df.XW_HZ.replace("None", "20.0") - self.df["YW_HZ"] = self.df.YW_HZ.replace("None", "20.0") - self.df["XW_HZ"] = self.df.XW_HZ.replace(np.NaN, "20.0") - self.df["YW_HZ"] = self.df.YW_HZ.replace(np.NaN, "20.0") - # convert linewidths to float - self.df["XW_HZ"] = self.df.XW_HZ.apply(lambda x: float(x)) - self.df["YW_HZ"] = self.df.YW_HZ.apply(lambda x: float(x)) - # convert Hz lw to points - self.df["XW"] = self.df.XW_HZ.apply(lambda x: x * self.pt_per_hz_f2) - self.df["YW"] = self.df.YW_HZ.apply(lambda x: x * self.pt_per_hz_f1) - # makes an assignment column from Assign F1 and Assign F2 columns - # in analysis2.x and ccpnmr v3 assign peak lists - if self.fmt in [PeaklistFormat.a2, PeaklistFormat.a3]: - self.df["ASS"] = self.df.apply( - # lambda i: "".join([i["Assign F1"], i["Assign F2"]]), axis=1 - lambda i: f"{i['Assign F1']}_{i['Assign F2']}", - axis=1, - ) - - # make default values for X and Y radii for fit masks - self.df["X_RADIUS_PPM"] = np.zeros(len(self.df)) + self.f2_radius - self.df["Y_RADIUS_PPM"] = np.zeros(len(self.df)) + self.f1_radius - self.df["X_RADIUS"] = self.df.X_RADIUS_PPM.apply( - lambda x: x * self.pt_per_ppm_f2 - ) - self.df["Y_RADIUS"] = self.df.Y_RADIUS_PPM.apply( - lambda x: x * self.pt_per_ppm_f1 - ) - # add include column - if "include" in self.df.columns: - pass - else: - self.df["include"] = self.df.apply(lambda x: "yes", axis=1) - - # check assignments for duplicates - self.check_assignments() - # check that peaks are within the bounds of the data - self.check_peak_bounds() - - def add_fix_bound_columns(self): - """add columns containing parameter bounds (param_upper/param_lower) - and whether or not parameter should be fixed (yes/no) - - For parameter bounding: - - Column names are _upper and _lower for upper and lower bounds respectively. - Values are given as floating point. Value of 0.0 indicates that parameter is unbounded - X/Y positions are given in ppm - Linewidths are given in Hz - - For parameter fixing: - - Column names are _fix. - Values are given as a string 'yes' or 'no' - - """ - pass - - def _read_analysis(self): - df = pd.read_csv(self.peaklist_path, delimiter="\t") - new_columns = [self.analysis_to_pipe_dic.get(i, i) for i in df.columns] - pipe_columns = dict(zip(df.columns, new_columns)) - df = df.rename(index=str, columns=pipe_columns) - - return df - - def _read_assign(self): - df = pd.read_csv(self.peaklist_path, delimiter="\t") - new_columns = [self.assign_to_pipe_dic.get(i, i) for i in df.columns] - pipe_columns = dict(zip(df.columns, new_columns)) - df = df.rename(index=str, columns=pipe_columns) - - return df - - def _read_sparky(self): - df = pd.read_csv( - self.peaklist_path, - skiprows=1, - sep=r"\s+", - names=["ASS", "Y_PPM", "X_PPM", "VOLUME", "HEIGHT", "YW_HZ", "XW_HZ"], - ) - df["INDEX"] = df.index - - return df - - def _read_pipe(self): - to_skip = 0 - with open(self.peaklist_path) as f: - lines = f.readlines() - for line in lines: - if line.startswith("VARS"): - columns = line.strip().split()[1:] - elif line[:5].strip(" ").isdigit(): - break - else: - to_skip += 1 - df = pd.read_csv( - self.peaklist_path, skiprows=to_skip, names=columns, sep=r"\s+" - ) - return df - - def check_assignments(self): - # self.df["ASS"] = self.df. - self.df["ASS"] = self.df.ASS.astype(object) - self.df.loc[self.df["ASS"].isnull(), "ASS"] = "None_dummy_0" - self.df["ASS"] = self.df.ASS.astype(str) - duplicates_bool = self.df.ASS.duplicated() - duplicates = self.df.ASS[duplicates_bool] - if len(duplicates) > 0: - console.print( - textwrap.dedent( - """ - ############################################################################# - You have duplicated assignments in your list... - Currently each peak needs a unique assignment. Sorry about that buddy... - ############################################################################# - """ - ), - style="yellow", - ) - self.df.loc[duplicates_bool, "ASS"] = [ - f"{i}_dummy_{num+1}" for num, i in enumerate(duplicates) - ] - if self.verbose: - print("Here are the duplicates") - print(duplicates) - print(self.df.ASS) - - print( - textwrap.dedent( - """ - Creating dummy assignments for duplicates - - """ - ) - ) - - def check_peak_bounds(self): - columns_to_print = ["INDEX", "ASS", "X_AXIS", "Y_AXIS", "X_PPM", "Y_PPM"] - # check that peaks are within the bounds of spectrum - within_x = (self.df.X_PPM < self.f2_ppm_max) & (self.df.X_PPM > self.f2_ppm_min) - within_y = (self.df.Y_PPM < self.f1_ppm_max) & (self.df.Y_PPM > self.f1_ppm_min) - self.excluded = self.df[~(within_x & within_y)] - self.df = self.df[within_x & within_y] - if len(self.excluded) > 0: - print( - textwrap.dedent( - f"""[red] - ################################################################################# - - Excluding the following peaks as they are not within the spectrum which has shape - - {self.data.shape} - [/red]""" - ) - ) - table_to_print = df_to_rich_table( - self.excluded, - title="Excluded", - columns=columns_to_print, - styles=["red" for i in columns_to_print], - ) - print(table_to_print) - print( - "[red]#################################################################################[/red]" - ) - - def clusters( - self, - thres=None, - struc_el: StrucEl = StrucEl.disk, - struc_size=(3,), - l_struc=None, - ): - """Find clusters of peaks - - :param thres: threshold for positive signals above which clusters are selected. If None then threshold_otsu is used - :type thres: float - - :param struc_el: 'square'|'disk'|'rectangle' - structuring element for binary_closing of thresholded data can be square, disc or rectangle - :type struc_el: str - - :param struc_size: size/dimensions of structuring element - for square and disk first element of tuple is used (for disk value corresponds to radius) - for rectangle, tuple corresponds to (width,height). - :type struc_size: tuple - - - """ - peaks = [[y, x] for y, x in zip(self.df.Y_AXIS, self.df.X_AXIS)] - - if thres == None: - thres = self.thres - self._thres = abs(threshold_otsu(self.data[0])) - else: - self._thres = thres - - # get positive and negative - thresh_data = np.bitwise_or( - self.data[0] < (self._thres * -1.0), self.data[0] > self._thres - ) - - match struc_el: - case struc_el.disk: - radius = struc_size[0] - if self.verbose: - print(f"using disk with {radius}") - closed_data = binary_closing(thresh_data, disk(int(radius))) - - case struc_el.square: - width = struc_size[0] - if self.verbose: - print(f"using square with {width}") - closed_data = binary_closing(thresh_data, square(int(width))) - - case struc_el.rectangle: - width, height = struc_size - if self.verbose: - print(f"using rectangle with {width} and {height}") - closed_data = binary_closing( - thresh_data, rectangle(int(width), int(height)) - ) - - case _: - if self.verbose: - print(f"Not using any closing function") - closed_data = thresh_data - - labeled_array, num_features = ndimage.label(closed_data, l_struc) - - self.df.loc[:, "CLUSTID"] = [labeled_array[i[0], i[1]] for i in peaks] - - #  renumber "0" clusters - max_clustid = self.df["CLUSTID"].max() - n_of_zeros = len(self.df[self.df["CLUSTID"] == 0]["CLUSTID"]) - self.df.loc[self.df[self.df["CLUSTID"] == 0].index, "CLUSTID"] = np.arange( - max_clustid + 1, n_of_zeros + max_clustid + 1, dtype=int - ) - - # count how many peaks per cluster - for ind, group in self.df.groupby("CLUSTID"): - self.df.loc[group.index, "MEMCNT"] = len(group) - - self.df.loc[:, "color"] = self.df.apply( - lambda x: Category20[20][int(x.CLUSTID) % 20] if x.MEMCNT > 1 else "black", - axis=1, - ) - return ClustersResult(labeled_array, num_features, closed_data, peaks) - - def mask_method(self, overlap=1.0, l_struc=None): - """connect clusters based on overlap of fitting masks - - :param overlap: fraction of mask for which overlaps are calculated - :type overlap: float - - :returns ClusterResult: Instance of ClusterResult - :rtype: ClustersResult - """ - # overlap is positive - overlap = abs(overlap) - - self._thres = threshold_otsu(self.data[0]) - - mask = np.zeros(self.data[0].shape, dtype=bool) - - for ind, peak in self.df.iterrows(): - mask += make_mask( - self.data[0], - peak.X_AXISf, - peak.Y_AXISf, - peak.X_RADIUS * overlap, - peak.Y_RADIUS * overlap, - ) - - peaks = [[y, x] for y, x in zip(self.df.Y_AXIS, self.df.X_AXIS)] - labeled_array, num_features = ndimage.label(mask, l_struc) - - self.df.loc[:, "CLUSTID"] = [labeled_array[i[0], i[1]] for i in peaks] - - #  renumber "0" clusters - max_clustid = self.df["CLUSTID"].max() - n_of_zeros = len(self.df[self.df["CLUSTID"] == 0]["CLUSTID"]) - self.df.loc[self.df[self.df["CLUSTID"] == 0].index, "CLUSTID"] = np.arange( - max_clustid + 1, n_of_zeros + max_clustid + 1, dtype=int - ) - - # count how many peaks per cluster - for ind, group in self.df.groupby("CLUSTID"): - self.df.loc[group.index, "MEMCNT"] = len(group) - - self.df.loc[:, "color"] = self.df.apply( - lambda x: Category20[20][int(x.CLUSTID) % 20] if x.MEMCNT > 1 else "black", - axis=1, - ) - - return ClustersResult(labeled_array, num_features, mask, peaks) - - def to_fuda(self, fname="params.fuda"): - with open("peaks.fuda", "w") as peaks_fuda: - for ass, f1_ppm, f2_ppm in zip(self.df.ASS, self.df.Y_PPM, self.df.X_PPM): - peaks_fuda.write(f"{ass}\t{f1_ppm:.3f}\t{f2_ppm:.3f}\n") - groups = self.df.groupby("CLUSTID") - fuda_params = Path(fname) - overlap_peaks = "" - - for ind, group in groups: - if len(group) > 1: - overlap_peaks_str = ";".join(group.ASS) - overlap_peaks += f"OVERLAP_PEAKS=({overlap_peaks_str})\n" - - fuda_file = textwrap.dedent( - f"""\ - -# Read peaklist and spectrum info -PEAKLIST=peaks.fuda -SPECFILE={self.data_path} -PARAMETERFILE=(bruker;vclist) -ZCORR=ncyc -NOISE={self.thres} # you'll need to adjust this -BASELINE=N -VERBOSELEVEL=5 -PRINTDATA=Y -LM=(MAXFEV=250;TOL=1e-5) -#Specify the default values. All values are in ppm: -DEF_LINEWIDTH_F1={self.f1_radius} -DEF_LINEWIDTH_F2={self.f2_radius} -DEF_RADIUS_F1={self.f1_radius} -DEF_RADIUS_F2={self.f2_radius} -SHAPE=GLORE -# OVERLAP PEAKS -{overlap_peaks}""" - ) - with open(fuda_params, "w") as f: - print(f"Writing FuDA file {fuda_file}") - f.write(fuda_file) - if self.verbose: - print(overlap_peaks) - - -class ClustersResult: - """Class to store results of clusters function""" - - def __init__(self, labeled_array, num_features, closed_data, peaks): - self._labeled_array = labeled_array - self._num_features = num_features - self._closed_data = closed_data - self._peaks = peaks - - @property - def labeled_array(self): - return self._labeled_array - - @property - def num_features(self): - return self._num_features - - @property - def closed_data(self): - return self._closed_data - - @property - def peaks(self): - return self._peaks - - -class LoadData(Peaklist): - """Load peaklist data from peakipy .csv file output from either peakipy read or edit - - read_peaklist is redefined to just read a .csv file - - check_data_frame makes sure data frame is in good shape for setting up fits - - """ - - def read_peaklist(self): - if self.peaklist_path.suffix == ".csv": - self.df = pd.read_csv(self.peaklist_path) # , comment="#") - - elif self.peaklist_path.suffix == ".tab": - self.df = pd.read_csv(self.peaklist_path, sep="\t") # comment="#") - - else: - self.df = pd.read_pickle(self.peaklist_path) - - self._thres = threshold_otsu(self.data[0]) - - return self.df - - def check_data_frame(self): - # make diameter columns - if "X_DIAMETER_PPM" in self.df.columns: - pass - else: - self.df["X_DIAMETER_PPM"] = self.df["X_RADIUS_PPM"] * 2.0 - self.df["Y_DIAMETER_PPM"] = self.df["Y_RADIUS_PPM"] * 2.0 - - #  make a column to track edited peaks - if "Edited" in self.df.columns: - pass - else: - self.df["Edited"] = np.zeros(len(self.df), dtype=bool) - - # create include column if it doesn't exist - if "include" in self.df.columns: - pass - else: - self.df["include"] = self.df.apply(lambda _: "yes", axis=1) - - # color clusters - self.df["color"] = self.df.apply( - lambda x: Category20[20][int(x.CLUSTID) % 20] if x.MEMCNT > 1 else "black", - axis=1, - ) - - # get rid of unnamed columns - unnamed_cols = [i for i in self.df.columns if "Unnamed:" in i] - self.df = self.df.drop(columns=unnamed_cols) - - def update_df(self): - """Slightly modified to retain previous configurations""" - # int point value - self.df["X_AXIS"] = self.df.X_PPM.apply(lambda x: self.uc_f2(x, "ppm")) - self.df["Y_AXIS"] = self.df.Y_PPM.apply(lambda x: self.uc_f1(x, "ppm")) - # decimal point value - self.df["X_AXISf"] = self.df.X_PPM.apply(lambda x: self.uc_f2.f(x, "ppm")) - self.df["Y_AXISf"] = self.df.Y_PPM.apply(lambda x: self.uc_f1.f(x, "ppm")) - # in case of missing values (should estimate though) - self.df["XW_HZ"] = self.df.XW_HZ.replace(np.NaN, "20.0") - self.df["YW_HZ"] = self.df.YW_HZ.replace(np.NaN, "20.0") - # convert linewidths to float - self.df["XW_HZ"] = self.df.XW_HZ.apply(lambda x: float(x)) - self.df["YW_HZ"] = self.df.YW_HZ.apply(lambda x: float(x)) - # convert Hz lw to points - self.df["XW"] = self.df.XW_HZ.apply(lambda x: x * self.pt_per_hz_f2) - self.df["YW"] = self.df.YW_HZ.apply(lambda x: x * self.pt_per_hz_f1) - # makes an assignment column - if self.fmt == "a2": - self.df["ASS"] = self.df.apply( - lambda i: "".join([i["Assign F1"], i["Assign F2"]]), axis=1 - ) - - # make default values for X and Y radii for fit masks - # self.df["X_RADIUS_PPM"] = np.zeros(len(self.df)) + self.f2_radius - # self.df["Y_RADIUS_PPM"] = np.zeros(len(self.df)) + self.f1_radius - self.df["X_RADIUS"] = self.df.X_RADIUS_PPM.apply( - lambda x: x * self.pt_per_ppm_f2 - ) - self.df["Y_RADIUS"] = self.df.Y_RADIUS_PPM.apply( - lambda x: x * self.pt_per_ppm_f1 - ) - # add include column - if "include" in self.df.columns: - pass - else: - self.df["include"] = self.df.apply(lambda x: "yes", axis=1) - - # check assignments for duplicates - self.check_assignments() - # check that peaks are within the bounds of the data - self.check_peak_bounds() - - -def load_config(config_path): - if config_path.exists(): - with open(config_path) as opened_config: - config_dic = json.load(opened_config) - return config_dic - else: - return {} - - -def write_config(config_path, config_dic): - with open(config_path, "w") as config: - config.write(json.dumps(config_dic, sort_keys=True, indent=4)) - - -def update_config_file(config_path, config_kvs): - config_dic = load_config(config_path) - config_dic.update(config_kvs) - write_config(config_path, config_dic) - return config_dic - - -def update_args_with_values_from_config_file(args, config_path="peakipy.config"): - """read a peakipy config file, extract params and update args dict - - :param args: dict containing params extracted from docopt command line - :type args: dict - :param config_path: path to peakipy config file [default: peakipy.config] - :type config_path: str - - :returns args: updated args dict - :rtype args: dict - :returns config: dict that resulted from reading config file - :rtype config: dict - - """ - # update args with values from peakipy.config file - config_path = Path(config_path) - if config_path.exists(): - try: - config = load_config(config_path) - print( - f"[green]Using config file with dims [yellow]{config.get('dims')}[/yellow][/green]" - ) - args["dims"] = config.get("dims", (0, 1, 2)) - noise = config.get("noise") - if noise: - noise = float(noise) - - colors = config.get("colors", ["#5e3c99", "#e66101"]) - except json.decoder.JSONDecodeError: - print( - "[red]Your peakipy.config file is corrupted - maybe your JSON is not correct...[/red]" - ) - print("[red]Not using[/red]") - noise = False - colors = args.get("colors", "#5e3c99,#e66101").strip().split(",") - else: - print( - "[red]No peakipy.config found - maybe you need to generate one with peakipy read or see docs[/red]" - ) - noise = False - colors = args.get("colors", "#5e3c99,#e66101").strip().split(",") - config = {} - - args["noise"] = noise - args["colors"] = colors - - return args, config - - -def calculate_height_for_voigt_lineshape(df): - df["height"] = df.apply( - lambda x: voigt2d( - XY=[0, 0], - center_x=0.0, - center_y=0.0, - sigma_x=x.sigma_x, - sigma_y=x.sigma_y, - gamma_x=x.gamma_x, - gamma_y=x.gamma_y, - amplitude=x.amp, - ), - axis=1, - ) - df["height_err"] = df.apply( - lambda x: x.amp_err * (x.height / x.amp) if x.amp_err != None else 0.0, - axis=1, - ) - return df - - -def calculate_fwhm_for_voigt_lineshape(df): - df["fwhm_g_x"] = df.sigma_x.apply( - lambda x: 2.0 * x * np.sqrt(2.0 * np.log(2.0)) - ) # fwhm of gaussian - df["fwhm_g_y"] = df.sigma_y.apply(lambda x: 2.0 * x * np.sqrt(2.0 * np.log(2.0))) - df["fwhm_l_x"] = df.gamma_x.apply(lambda x: 2.0 * x) # fwhm of lorentzian - df["fwhm_l_y"] = df.gamma_y.apply(lambda x: 2.0 * x) - df["fwhm_x"] = df.apply( - lambda x: 0.5346 * x.fwhm_l_x - + np.sqrt(0.2166 * x.fwhm_l_x**2.0 + x.fwhm_g_x**2.0), - axis=1, - ) - df["fwhm_y"] = df.apply( - lambda x: 0.5346 * x.fwhm_l_y - + np.sqrt(0.2166 * x.fwhm_l_y**2.0 + x.fwhm_g_y**2.0), - axis=1, - ) - return df - - -def calculate_height_for_pseudo_voigt_lineshape(df): - df["height"] = df.apply( - lambda x: pvoigt2d( - XY=[0, 0], - center_x=0.0, - center_y=0.0, - sigma_x=x.sigma_x, - sigma_y=x.sigma_y, - amplitude=x.amp, - fraction=x.fraction, - ), - axis=1, - ) - df["height_err"] = df.apply(lambda x: x.amp_err * (x.height / x.amp), axis=1) - return df - - -def calculate_fwhm_for_pseudo_voigt_lineshape(df): - df["fwhm_x"] = df.sigma_x.apply(lambda x: x * 2.0) - df["fwhm_y"] = df.sigma_y.apply(lambda x: x * 2.0) - return df - - -def calculate_height_for_gaussian_lineshape(df): - df["height"] = df.apply( - lambda x: pvoigt2d( - XY=[0, 0], - center_x=0.0, - center_y=0.0, - sigma_x=x.sigma_x, - sigma_y=x.sigma_y, - amplitude=x.amp, - fraction=0.0, # gaussian - ), - axis=1, - ) - df["height_err"] = df.apply(lambda x: x.amp_err * (x.height / x.amp), axis=1) - return df - - -def calculate_height_for_lorentzian_lineshape(df): - df["height"] = df.apply( - lambda x: pvoigt2d( - XY=[0, 0], - center_x=0.0, - center_y=0.0, - sigma_x=x.sigma_x, - sigma_y=x.sigma_y, - amplitude=x.amp, - fraction=1.0, # lorentzian - ), - axis=1, - ) - df["height_err"] = df.apply(lambda x: x.amp_err * (x.height / x.amp), axis=1) - return df - - -def calculate_height_for_pv_pv_lineshape(df): - df["height"] = df.apply( - lambda x: pv_pv( - XY=[0, 0], - center_x=0.0, - center_y=0.0, - sigma_x=x.sigma_x, - sigma_y=x.sigma_y, - amplitude=x.amp, - fraction_x=x.fraction_x, - fraction_y=x.fraction_y, - ), - axis=1, - ) - df["height_err"] = df.apply(lambda x: x.amp_err * (x.height / x.amp), axis=1) - return df - - -def calculate_peak_centers_in_ppm(df, peakipy_data): - #  convert values to ppm - df["center_x_ppm"] = df.center_x.apply(lambda x: peakipy_data.uc_f2.ppm(x)) - df["center_y_ppm"] = df.center_y.apply(lambda x: peakipy_data.uc_f1.ppm(x)) - df["init_center_x_ppm"] = df.init_center_x.apply( - lambda x: peakipy_data.uc_f2.ppm(x) - ) - df["init_center_y_ppm"] = df.init_center_y.apply( - lambda x: peakipy_data.uc_f1.ppm(x) - ) - return df - - -def calculate_peak_linewidths_in_hz(df, peakipy_data): - df["sigma_x_ppm"] = df.sigma_x.apply(lambda x: x * peakipy_data.ppm_per_pt_f2) - df["sigma_y_ppm"] = df.sigma_y.apply(lambda x: x * peakipy_data.ppm_per_pt_f1) - df["fwhm_x_ppm"] = df.fwhm_x.apply(lambda x: x * peakipy_data.ppm_per_pt_f2) - df["fwhm_y_ppm"] = df.fwhm_y.apply(lambda x: x * peakipy_data.ppm_per_pt_f1) - df["fwhm_x_hz"] = df.fwhm_x.apply(lambda x: x * peakipy_data.hz_per_pt_f2) - df["fwhm_y_hz"] = df.fwhm_y.apply(lambda x: x * peakipy_data.hz_per_pt_f1) - return df diff --git a/peakipy/fitting.py b/peakipy/fitting.py index 915ebfae..e99f6aa4 100644 --- a/peakipy/fitting.py +++ b/peakipy/fitting.py @@ -1,13 +1,25 @@ +from pathlib import Path from dataclasses import dataclass, field -from typing import List +from typing import List, Tuple, Optional import numpy as np from numpy import sqrt import pandas as pd -from lmfit import Model +from rich import console +from lmfit import Model, Parameters, Parameter +from lmfit.model import ModelResult from pydantic import BaseModel -from peakipy.lineshapes import Lineshape, pvoigt2d, pv_pv, pv_g, pv_l, voigt2d +from peakipy.lineshapes import ( + Lineshape, + pvoigt2d, + pv_pv, + pv_g, + pv_l, + voigt2d, + gaussian_lorentzian, + get_lineshape_function, +) from peakipy.constants import log2 @@ -643,3 +655,523 @@ def simulate_lineshapes_from_fitted_peak_parameters( sim_data += sim_data_i sim_data_singles.append(sim_data_i) return sim_data, sim_data_singles + + +@dataclass +class FitPeaksArgs: + noise: float + uc_dics: dict + lineshape: Lineshape + dims: List[int] = field(default_factory=lambda: [0, 1, 2]) + colors: Tuple[str] = ("#5e3c99", "#e66101") + max_cluster_size: Optional[int] = None + to_fix: List[str] = field(default_factory=lambda: ["fraction", "sigma", "center"]) + xy_bounds: Tuple[float, float] = ((0, 0),) + vclist: Optional[Path] = (None,) + plane: Optional[List[int]] = (None,) + exclude_plane: Optional[List[int]] = (None,) + reference_plane_indices: List[int] = ([],) + initial_fit_threshold: Optional[float] = (None,) + jack_knife_sample_errors: bool = False + mp: bool = (True,) + verbose: bool = (False,) + vclist_data: Optional[np.array] = None + + +@dataclass +class Config: + fit_method: str = "leastsq" + + +@dataclass +class FitPeaksInput: + """input data for the fit_peaks function""" + + args: FitPeaksArgs + data: np.array + config: Config + plane_numbers: list + + +@dataclass +class FitPeakClusterInput: + args: FitPeaksArgs + data: np.array + config: Config + plane_numbers: list + clustid: int + group: pd.DataFrame + last_peak: pd.DataFrame + mask: np.array + mod: Model + p_guess: Parameters + XY: np.array + peak_slices: np.array + XY_slices: np.array + min_x: float + max_x: float + min_y: float + max_y: float + uc_dics: dict + first_plane_data: np.array + weights: np.array + fit_method: str = "leastsq" + verbose: bool = False + masked_plane_data: np.array = field(init=False) + + def __post_init__(self): + self.masked_plane_data = np.array([d[self.mask] for d in self.data]) + + +@dataclass +class FitResult: + out: ModelResult + mask: np.array + fit_str: str + log: str + group: pd.core.groupby.generic.DataFrameGroupBy + uc_dics: dict + min_x: float + min_y: float + max_x: float + max_y: float + X: np.array + Y: np.array + Z: np.array + Z_sim: np.array + peak_slices: np.array + XY_slices: np.array + weights: np.array + mod: Model + + def check_shifts(self): + """Calculate difference between initial peak positions + and check whether they moved too much from original + position + + """ + pass + + +@dataclass +class FitPeaksResult: + df: pd.DataFrame + log: str + + +class FitPeaksResultDfRow(BaseModel): + fit_prefix: str + assignment: str + amp: float + amp_err: float + center_x: float + init_center_x: float + center_y: float + init_center_y: float + sigma_x: float + sigma_y: float + clustid: int + memcnt: int + plane: int + x_radius: float + y_radius: float + x_radius_ppm: float + y_radius_ppm: float + lineshape: str + aic: float + chisqr: float + redchi: float + residual_sum: float + height: float + height_err: float + fwhm_x: float + fwhm_y: float + center_x_ppm: float + center_y_ppm: float + init_center_x_ppm: float + init_center_y_ppm: float + sigma_x_ppm: float + sigma_y_ppm: float + fwhm_x_ppm: float + fwhm_y_ppm: float + fwhm_x_hz: float + fwhm_y_hz: float + jack_knife_sample_index: Optional[int] + + +class FitPeaksResultRowGLPV(FitPeaksResultDfRow): + fraction: float + + +class FitPeaksResultRowPVPV(FitPeaksResultDfRow): + fraction_x: float # for PV_PV model + fraction_y: float # for PV_PV model + + +class FitPeaksResultRowVoigt(FitPeaksResultDfRow): + gamma_x_ppm: float # for voigt + gamma_y_ppm: float # for voigt + + +def get_fit_peaks_result_validation_model(lineshape): + match lineshape: + case lineshape.V: + validation_model = FitPeaksResultRowVoigt + case lineshape.PV_PV: + validation_model = FitPeaksResultRowPVPV + case _: + validation_model = FitPeaksResultRowGLPV + return validation_model + + +def filter_peak_clusters_by_max_cluster_size(grouped_peak_clusters, max_cluster_size): + filtered_peak_clusters = grouped_peak_clusters.filter( + lambda x: len(x) <= max_cluster_size + ) + return filtered_peak_clusters + + +def set_parameters_to_fix_during_fit(first_plane_fit_params, to_fix): + # fix sigma center and fraction parameters + # could add an option to select params to fix + match to_fix: + case None | () | []: + float_str = "Floating all parameters" + parameter_set = first_plane_fit_params + case ["None"] | ["none"]: + float_str = "Floating all parameters" + parameter_set = first_plane_fit_params + case _: + float_str = f"Fixing parameters: {to_fix}" + parameter_set = fix_params(first_plane_fit_params, to_fix) + return parameter_set, float_str + + +def get_default_lineshape_param_names(lineshape: Lineshape): + match lineshape: + case Lineshape.PV | Lineshape.G | Lineshape.L: + param_names = Model(pvoigt2d).param_names + case Lineshape.V: + param_names = Model(voigt2d).param_names + case Lineshape.PV_PV: + param_names = Model(pv_pv).param_names + return param_names + + +def split_parameter_sets_by_peak( + default_param_names: List, params: List[Tuple[str, Parameter]] +): + """params is a list of tuples where the first element of each tuple is a + prefixed parameter name and the second element is the corresponding + Parameter object. This is created by calling .items() on a Parameters + object + """ + number_of_fitted_parameters = len(params) + number_of_default_params = len(default_param_names) + number_of_fitted_peaks = int(number_of_fitted_parameters / number_of_default_params) + split_param_items = [ + params[i : (i + number_of_default_params)] + for i in range(0, number_of_fitted_parameters, number_of_default_params) + ] + assert len(split_param_items) == number_of_fitted_peaks + return split_param_items + + +def create_parameter_dict(prefix, parameters: List[Tuple[str, Parameter]]): + parameter_dict = dict(prefix=prefix) + parameter_dict.update({k.replace(prefix, ""): v.value for k, v in parameters}) + parameter_dict.update( + {f"{k.replace(prefix,'')}_stderr": v.stderr for k, v in parameters} + ) + return parameter_dict + + +def get_prefix_from_parameter_names( + default_param_names: List, parameters: List[Tuple[str, Parameter]] +): + prefixes = [ + param_key_val[0].replace(default_param_name, "") + for param_key_val, default_param_name in zip(parameters, default_param_names) + ] + assert len(set(prefixes)) == 1 + return prefixes[0] + + +def unpack_fitted_parameters_for_lineshape( + lineshape: Lineshape, params: List[dict], plane_number: int +): + default_param_names = get_default_lineshape_param_names(lineshape) + split_parameter_names = split_parameter_sets_by_peak(default_param_names, params) + prefixes = [ + get_prefix_from_parameter_names(default_param_names, i) + for i in split_parameter_names + ] + unpacked_params = [] + for parameter_names, prefix in zip(split_parameter_names, prefixes): + parameter_dict = create_parameter_dict(prefix, parameter_names) + parameter_dict.update({"plane": plane_number}) + unpacked_params.append(parameter_dict) + return unpacked_params + + +def perform_initial_lineshape_fit_on_cluster_of_peaks( + fit_peak_cluster_input: FitPeakClusterInput, +) -> FitResult: + mod = fit_peak_cluster_input.mod + peak_slices = fit_peak_cluster_input.peak_slices + XY_slices = fit_peak_cluster_input.XY_slices + p_guess = fit_peak_cluster_input.p_guess + weights = fit_peak_cluster_input.weights + fit_method = fit_peak_cluster_input.fit_method + mask = fit_peak_cluster_input.mask + XY = fit_peak_cluster_input.XY + X, Y = XY + first_plane_data = fit_peak_cluster_input.first_plane_data + peak = fit_peak_cluster_input.last_peak + group = fit_peak_cluster_input.group + min_x = fit_peak_cluster_input.min_x + min_y = fit_peak_cluster_input.min_y + max_x = fit_peak_cluster_input.max_x + max_y = fit_peak_cluster_input.max_y + verbose = fit_peak_cluster_input.verbose + uc_dics = fit_peak_cluster_input.uc_dics + + out = mod.fit( + peak_slices, XY=XY_slices, params=p_guess, weights=weights, method=fit_method + ) + + if verbose: + console.print(out.fit_report(), style="bold") + + z_sim = mod.eval(XY=XY, params=out.params) + z_sim[~mask] = np.nan + z_plot = first_plane_data.copy() + z_plot[~mask] = np.nan + fit_str = "" + log = "" + + return FitResult( + out=out, + mask=mask, + fit_str=fit_str, + log=log, + group=group, + uc_dics=uc_dics, + min_x=min_x, + min_y=min_y, + max_x=max_x, + max_y=max_y, + X=X, + Y=Y, + Z=z_plot, + Z_sim=z_sim, + peak_slices=peak_slices, + XY_slices=XY_slices, + weights=weights, + mod=mod, + ) + + +def refit_peak_cluster_with_constraints( + fit_input: FitPeakClusterInput, fit_result: FitPeaksResult +): + fit_results = [] + for num, d in enumerate(fit_input.masked_plane_data): + plane_number = fit_input.plane_numbers[num] + fit_result.out.fit( + data=d, + params=fit_result.out.params, + weights=fit_result.weights, + ) + fit_results.extend( + unpack_fitted_parameters_for_lineshape( + fit_input.args.lineshape, + list(fit_result.out.params.items()), + plane_number, + ) + ) + return fit_results + + +def merge_unpacked_parameters_with_metadata(cluster_fit_df, group_of_peaks_df): + group_of_peaks_df["prefix"] = group_of_peaks_df.ASS.apply(to_prefix) + merged_cluster_fit_df = cluster_fit_df.merge(group_of_peaks_df, on="prefix") + return merged_cluster_fit_df + + +def update_cluster_df_with_fit_statistics(cluster_df, fit_result: ModelResult): + cluster_df["chisqr"] = fit_result.chisqr + cluster_df["redchi"] = fit_result.redchi + cluster_df["residual_sum"] = np.sum(fit_result.residual) + cluster_df["aic"] = fit_result.aic + cluster_df["bic"] = fit_result.bic + cluster_df["nfev"] = fit_result.nfev + cluster_df["ndata"] = fit_result.ndata + return cluster_df + + +def rename_columns_for_compatibility(df): + mapping = { + "amplitude": "amp", + "amplitude_stderr": "amp_err", + "X_AXIS": "init_center_x", + "Y_AXIS": "init_center_y", + "ASS": "assignment", + "MEMCNT": "memcnt", + "X_RADIUS": "x_radius", + "Y_RADIUS": "y_radius", + } + df = df.rename(columns=mapping) + return df + + +def add_vclist_to_df(fit_input: FitPeaksInput, df: pd.DataFrame): + vclist_data = fit_input.args.vclist_data + df["vclist"] = df.plane.apply(lambda x: vclist_data[x]) + return df + + +def prepare_group_of_peaks_for_fitting(clustid, group, fit_peaks_input: FitPeaksInput): + lineshape_function = get_lineshape_function(fit_peaks_input.args.lineshape) + + first_plane_data = fit_peaks_input.data[0] + mask, peak = make_mask_from_peak_cluster(group, first_plane_data) + + x_radius = group.X_RADIUS.max() + y_radius = group.Y_RADIUS.max() + + max_x, min_x = get_limits_for_axis_in_points( + group_axis_points=group.X_AXISf, mask_radius_in_points=x_radius + ) + max_y, min_y = get_limits_for_axis_in_points( + group_axis_points=group.Y_AXISf, mask_radius_in_points=y_radius + ) + max_x, min_x, max_y, min_y = deal_with_peaks_on_edge_of_spectrum( + fit_peaks_input.data.shape, max_x, min_x, max_y, min_y + ) + selected_data = select_reference_planes_using_indices( + fit_peaks_input.data, fit_peaks_input.args.reference_plane_indices + ).sum(axis=0) + mod, p_guess = make_models( + lineshape_function, + group, + selected_data, + lineshape=fit_peaks_input.args.lineshape, + xy_bounds=fit_peaks_input.args.xy_bounds, + ) + peak_slices = slice_peaks_from_data_using_mask(fit_peaks_input.data, mask) + peak_slices = select_reference_planes_using_indices( + peak_slices, fit_peaks_input.args.reference_plane_indices + ) + peak_slices = select_planes_above_threshold_from_masked_data( + peak_slices, fit_peaks_input.args.initial_fit_threshold + ) + peak_slices = peak_slices.sum(axis=0) + + XY = make_meshgrid(fit_peaks_input.data.shape) + X, Y = XY + + XY_slices = np.array([X.copy()[mask], Y.copy()[mask]]) + weights = 1.0 / np.array([fit_peaks_input.args.noise] * len(np.ravel(peak_slices))) + return FitPeakClusterInput( + args=fit_peaks_input.args, + data=fit_peaks_input.data, + config=fit_peaks_input.config, + plane_numbers=fit_peaks_input.plane_numbers, + clustid=clustid, + group=group, + last_peak=peak, + mask=mask, + mod=mod, + p_guess=p_guess, + XY=XY, + peak_slices=peak_slices, + XY_slices=XY_slices, + weights=weights, + fit_method=Config.fit_method, + first_plane_data=first_plane_data, + uc_dics=fit_peaks_input.args.uc_dics, + min_x=min_x, + min_y=min_y, + max_x=max_x, + max_y=max_y, + verbose=fit_peaks_input.args.verbose, + ) + + +def fit_cluster_of_peaks(data_for_fitting: FitPeakClusterInput) -> pd.DataFrame: + fit_result = perform_initial_lineshape_fit_on_cluster_of_peaks(data_for_fitting) + fit_result.out.params, float_str = set_parameters_to_fix_during_fit( + fit_result.out.params, data_for_fitting.args.to_fix + ) + fit_results = refit_peak_cluster_with_constraints(data_for_fitting, fit_result) + cluster_df = pd.DataFrame(fit_results) + cluster_df = update_cluster_df_with_fit_statistics(cluster_df, fit_result.out) + cluster_df["clustid"] = data_for_fitting.clustid + cluster_df = merge_unpacked_parameters_with_metadata( + cluster_df, data_for_fitting.group + ) + return cluster_df + + +def fit_peak_clusters(peaks: pd.DataFrame, fit_input: FitPeaksInput) -> FitPeaksResult: + """Fit set of peak clusters to lineshape model + + :param peaks: peaklist with generated by peakipy read or edit + :type peaks: pd.DataFrame + + :param fit_input: Data structure containing input parameters (args, config and NMR data) + :type fit_input: FitPeaksInput + + :returns: Data structure containing pd.DataFrame with the fitted results and a log + :rtype: FitPeaksResult + """ + peak_clusters = peaks.groupby("CLUSTID") + filtered_peaks = filter_peak_clusters_by_max_cluster_size( + peak_clusters, fit_input.args.max_cluster_size + ) + peak_clusters = filtered_peaks.groupby("CLUSTID") + out_str = "" + cluster_dfs = [] + for clustid, peak_cluster in peak_clusters: + data_for_fitting = prepare_group_of_peaks_for_fitting( + clustid, + peak_cluster, + fit_input, + ) + if fit_input.args.jack_knife_sample_errors: + cluster_df = jack_knife_sample_errors(data_for_fitting) + else: + cluster_df = fit_cluster_of_peaks(data_for_fitting) + cluster_dfs.append(cluster_df) + df = pd.concat(cluster_dfs, ignore_index=True) + + df["lineshape"] = fit_input.args.lineshape.value + + if fit_input.args.vclist: + df = add_vclist_to_df(fit_input, df) + df = rename_columns_for_compatibility(df) + return FitPeaksResult(df=df, log=out_str) + + +def jack_knife_sample_errors(fit_input: FitPeakClusterInput) -> pd.DataFrame: + peak_slices = fit_input.peak_slices.copy() + XY_slices = fit_input.XY_slices.copy() + weights = fit_input.weights.copy() + masked_plane_data = fit_input.masked_plane_data.copy() + jk_results = [] + # first fit without jackknife + jk_result = fit_cluster_of_peaks(data_for_fitting=fit_input) + jk_result["jack_knife_sample_index"] = 0 + jk_results.append(jk_result) + for i in np.arange(0, len(peak_slices), 10, dtype=int): + fit_input.peak_slices = np.delete(peak_slices, i, None) + XY_slices_0 = np.delete(XY_slices[0], i, None) + XY_slices_1 = np.delete(XY_slices[1], i, None) + fit_input.XY_slices = np.array([XY_slices_0, XY_slices_1]) + fit_input.weights = np.delete(weights, i, None) + fit_input.masked_plane_data = np.delete(masked_plane_data, i, axis=1) + jk_result = fit_cluster_of_peaks(data_for_fitting=fit_input) + jk_result["jack_knife_sample_index"] = i + 1 + jk_results.append(jk_result) + return pd.concat(jk_results, ignore_index=True) diff --git a/peakipy/plotting.py b/peakipy/plotting.py index 479f4ace..738c3712 100644 --- a/peakipy/plotting.py +++ b/peakipy/plotting.py @@ -122,6 +122,7 @@ def create_matplotlib_figure( label=False, ccpn_flag=False, show=True, + test=False, ): fig = plt.figure(figsize=(10, 6)) ax = fig.add_subplot(projection="3d") @@ -223,6 +224,8 @@ def next_plot(event): axnext = plt.axes([0.71, 0.05, 0.1, 0.075]) bnnext = Button(axnext, "Next") bnnext.on_clicked(next_plot) + if test: + return if ccpn_flag: plt.show(windowTitle="", size=(1000, 500)) else: diff --git a/test/test_core.py b/test/test_core.py deleted file mode 100644 index ed9dec4c..00000000 --- a/test/test_core.py +++ /dev/null @@ -1,570 +0,0 @@ -import unittest -from unittest.mock import patch -from collections import namedtuple -from pathlib import Path -import json - -import pytest -import numpy as np -from numpy.testing import assert_array_equal -import pandas as pd -import nmrglue as ng -from lmfit import Model - - -from peakipy.core import ( - make_mask, - fix_params, - gaussian_lorentzian, - pv_g, - pv_l, - voigt2d, - pvoigt2d, - pv_pv, - get_params, - make_param_dict, - to_prefix, - make_models, - get_lineshape_function, - Pseudo3D, - Peaklist, - Lineshape, - PeaklistFormat, - PeakLimits, - select_reference_planes_using_indices, - select_planes_above_threshold_from_masked_data, - slice_peaks_from_data_using_mask, - estimate_amplitude, - load_config, - write_config, - update_config_file, -) - - -@pytest.fixture -def test_directory(): - return Path(__file__).parent - - -def test_select_reference_planes_using_indices(): - data = np.zeros((6, 100, 200)) - indices = [] - np.testing.assert_array_equal( - select_reference_planes_using_indices(data, indices), data - ) - indices = [1] - assert select_reference_planes_using_indices(data, indices).shape == (1, 100, 200) - indices = [1, -1] - assert select_reference_planes_using_indices(data, indices).shape == (2, 100, 200) - - -def test_slice_peaks_from_data_using_mask(): - data = np.array( - [ - np.array( - [ - [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 1, 1, 0, 0, 0, 0], - [0, 0, 0, 1, 2, 2, 1, 0, 0, 0], - [0, 0, 1, 2, 3, 3, 2, 1, 0, 0], - [0, 1, 2, 3, 4, 4, 3, 2, 1, 0], - [1, 2, 3, 4, 5, 5, 4, 3, 2, 1], - [0, 1, 2, 3, 4, 4, 3, 2, 1, 0], - [0, 0, 1, 2, 3, 3, 2, 1, 0, 0], - [0, 0, 0, 1, 2, 2, 1, 0, 0, 0], - [0, 0, 0, 0, 1, 1, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - ] - ) - for i in range(5) - ] - ) - mask = data[0] > 0 - assert data.shape == (5, 11, 10) - assert mask.shape == (11, 10) - peak_slices = slice_peaks_from_data_using_mask(data, mask) - # array is flattened by application of mask - assert peak_slices.shape == (5, 50) - - -def test_select_planes_above_threshold_from_masked_data(): - peak_slices = np.array( - [ - [1, 1, 1, 1, 1, 1], - [2, 2, 2, 2, 2, 2], - [-1, -1, -1, -1, -1, -1], - [-2, -2, -2, -2, -2, -2], - ] - ) - assert peak_slices.shape == (4, 6) - threshold = -1 - assert select_planes_above_threshold_from_masked_data( - peak_slices, threshold - ).shape == ( - 4, - 6, - ) - threshold = 2 - assert_array_equal( - select_planes_above_threshold_from_masked_data(peak_slices, threshold), - peak_slices, - ) - threshold = 1 - assert select_planes_above_threshold_from_masked_data( - peak_slices, threshold - ).shape == (2, 6) - - threshold = None - assert_array_equal( - select_planes_above_threshold_from_masked_data(peak_slices, threshold), - peak_slices, - ) - threshold = 10 - assert_array_equal( - select_planes_above_threshold_from_masked_data(peak_slices, threshold), - peak_slices, - ) - - -def test_make_param_dict(): - selected_planes = [1, 2] - data = np.ones((4, 10, 5)) - expected_shape = (2, 10, 5) - actual_shape = data[np.array(selected_planes)].shape - assert expected_shape == actual_shape - - -def test_make_param_dict_sum(): - data = np.ones((4, 10, 5)) - expected_sum = 200 - actual_sum = data.sum() - assert expected_sum == actual_sum - - -def test_make_param_dict_selected(): - selected_planes = [1, 2] - data = np.ones((4, 10, 5)) - data = data[np.array(selected_planes)] - expected_sum = 100 - actual_sum = data.sum() - assert expected_sum == actual_sum - - -def test_estimate_amplitude(): - peak = namedtuple("peak", ["X_AXIS", "XW", "Y_AXIS", "YW"]) - p = peak(5, 2, 3, 2) - data = np.ones((20, 10)) - expected_result = 25 - actual_result = estimate_amplitude(p, data) - assert expected_result == actual_result - - -def test_estimate_amplitude_invalid_indices(): - peak = namedtuple("peak", ["X_AXIS", "XW", "Y_AXIS", "YW"]) - p = peak(1, 2, 3, 2) - data = np.ones((20, 10)) - expected_result = 20 - actual_result = estimate_amplitude(p, data) - assert expected_result == actual_result - - -class TestCoreFunctions(unittest.TestCase): - test_directory = Path(__file__).parent - test_directory = "./" - - def test_make_mask(self): - data = np.ones((10, 10)) - c_x = 5 - c_y = 5 - r_x = 3 - r_y = 2 - - expected_result = np.array( - [ - [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], - [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], - [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], - [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0], - [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0], - [0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0], - [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0], - [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0], - [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], - [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], - ] - ) - - result = np.array(make_mask(data, c_x, c_y, r_x, r_y), dtype=int) - test = result - expected_result - # print(test) - # print(test.sum()) - # print(result) - self.assertEqual(test.sum(), 0) - - def test_make_mask_2(self): - data = np.ones((10, 10)) - c_x = 5 - c_y = 8 - r_x = 3 - r_y = 2 - - expected_result = np.array( - [ - [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], - [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], - [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], - [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], - [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], - [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], - [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0], - [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0], - [0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0], - [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0], - ] - ) - - result = np.array(make_mask(data, c_x, c_y, r_x, r_y), dtype=int) - test = result - expected_result - # print(test) - # print(test.sum()) - # print(result) - self.assertEqual(test.sum(), 0) - - def test_fix_params(self): - mod = Model(pvoigt2d) - pars = mod.make_params() - to_fix = ["center", "sigma", "fraction"] - fix_params(pars, to_fix) - - self.assertEqual(pars["center_x"].vary, False) - self.assertEqual(pars["center_y"].vary, False) - self.assertEqual(pars["sigma_x"].vary, False) - self.assertEqual(pars["sigma_y"].vary, False) - self.assertEqual(pars["fraction"].vary, False) - - def test_get_params(self): - mod = Model(pvoigt2d, prefix="p1_") - pars = mod.make_params(p1_center_x=20.0, p1_center_y=30.0) - pars["p1_center_x"].stderr = 1.0 - pars["p1_center_y"].stderr = 2.0 - ps, ps_err, names, prefixes = get_params(pars, "center") - #  get index of values - cen_x = names.index("p1_center_x") - cen_y = names.index("p1_center_y") - - self.assertEqual(ps[cen_x], 20.0) - self.assertEqual(ps[cen_y], 30.0) - self.assertEqual(ps_err[cen_x], 1.0) - self.assertEqual(ps_err[cen_y], 2.0) - self.assertEqual(prefixes[cen_y], "p1_") - - def test_make_param_dict(self): - peaks = pd.DataFrame( - { - "ASS": ["one", "two", "three"], - "X_AXISf": [5.0, 10.0, 15.0], - "X_AXIS": [5, 10, 15], - "Y_AXISf": [15.0, 10.0, 5.0], - "Y_AXIS": [15, 10, 5], - "XW": [2.5, 2.5, 2.5], - "YW": [2.5, 2.5, 2.5], - } - ) - data = np.ones((20, 20)) - - for ls, frac in zip([Lineshape.PV, Lineshape.G, Lineshape.L], [0.5, 0.0, 1.0]): - params = make_param_dict(peaks, data, ls) - self.assertEqual(params["_one_fraction"], frac) - self.assertEqual(params["_two_fraction"], frac) - self.assertEqual(params["_three_fraction"], frac) - - self.assertEqual(params["_one_center_x"], 5.0) - self.assertEqual(params["_two_center_x"], 10.0) - self.assertEqual(params["_two_sigma_x"], 1.25) - self.assertEqual(params["_two_sigma_y"], 1.25) - - voigt_params = make_param_dict(peaks, data, Lineshape.V) - self.assertEqual( - voigt_params["_one_sigma_x"], 2.5 / (2.0 * np.sqrt(2.0 * np.log(2))) - ) - self.assertEqual(voigt_params["_one_gamma_x"], 2.5 / 2.0) - - def test_to_prefix(self): - names = [ - (1, "_1_"), - (1.0, "_1_0_"), - (" one", "_one_"), - (" one/two", "_oneortwo_"), - (" one?two", "_onemaybetwo_"), - (r" [{one?two\}][", "___onemaybetwo____"), - ] - for test, expect in names: - prefix = to_prefix(test) - # print(prefix) - self.assertEqual(prefix, expect) - - def test_make_models(self): - peaks = pd.DataFrame( - { - "ASS": ["one", "two", "three"], - "X_AXISf": [5.0, 10.0, 15.0], - "X_AXIS": [5, 10, 15], - "Y_AXISf": [15.0, 10.0, 5.0], - "Y_AXIS": [15, 10, 5], - "XW": [2.5, 2.5, 2.5], - "YW": [2.5, 2.5, 2.5], - "CLUSTID": [1, 1, 1], - } - ) - - group = peaks.groupby("CLUSTID") - - data = np.ones((20, 20)) - - lineshapes = [Lineshape.PV, Lineshape.L, Lineshape.G, Lineshape.PV_PV] - - for lineshape in lineshapes: - match lineshape: - case lineshape.PV: - mod, p_guess = make_models(pvoigt2d, peaks, data, lineshape) - self.assertEqual(p_guess["_one_fraction"].vary, True) - self.assertEqual(p_guess["_one_fraction"].value, 0.5) - - case lineshape.G: - mod, p_guess = make_models(pvoigt2d, peaks, data, lineshape) - self.assertEqual(p_guess["_one_fraction"].vary, False) - self.assertEqual(p_guess["_one_fraction"].value, 0.0) - - case lineshape.L: - mod, p_guess = make_models(pvoigt2d, peaks, data, lineshape) - self.assertEqual(p_guess["_one_fraction"].vary, False) - self.assertEqual(p_guess["_one_fraction"].value, 1.0) - - case lineshape.PV_PV: - mod, p_guess = make_models(pv_pv, peaks, data, lineshape) - self.assertEqual(p_guess["_one_fraction_x"].vary, True) - self.assertEqual(p_guess["_one_fraction_x"].value, 0.5) - self.assertEqual(p_guess["_one_fraction_y"].vary, True) - self.assertEqual(p_guess["_one_fraction_y"].value, 0.5) - - def test_Pseudo3D(self): - datasets = [ - (f"{self.test_directory}/test_protein_L/test1.ft2", [0, 1, 2]), - (f"{self.test_directory}/test_protein_L/test_tp.ft2", [2, 1, 0]), - (f"{self.test_directory}/test_protein_L/test_tp2.ft2", [1, 2, 0]), - ] - - # expected shape - data_shape = (4, 256, 546) - test_nu = 1 - for dataset, dims in datasets: - with self.subTest(i=test_nu): - dic, data = ng.pipe.read(dataset) - pseudo3D = Pseudo3D(dic, data, dims) - self.assertEqual(dims, pseudo3D.dims) - self.assertEqual(pseudo3D.data.shape, data_shape) - self.assertEqual(pseudo3D.f1_label, "15N") - self.assertEqual(pseudo3D.f2_label, "HN") - self.assertEqual(pseudo3D.dims, dims) - self.assertEqual(pseudo3D.f1_size, 256) - self.assertEqual(pseudo3D.f2_size, 546) - test_nu += 1 - - -# test for read, edit, fit, check and spec scripts -# need to actually write proper tests -class TestBokehScript(unittest.TestCase): - @patch("peakipy.cli.edit.BokehScript") - def test_BokehScript(self, MockBokehScript): - args = {"": "hello", "": "data"} - bokeh_plots = MockBokehScript(args) - self.assertIsNotNone(bokeh_plots) - - -class TestCheckScript(unittest.TestCase): - @patch("peakipy.cli.main.check") - def test_main(self, MockCheck): - args = {"": "hello", "": "data"} - check = MockCheck(args) - self.assertIsNotNone(check) - - -class TestFitScript(unittest.TestCase): - @patch("peakipy.cli.main.fit") - def test_main(self, MockFit): - args = {"": "hello", "": "data"} - fit = MockFit(args) - self.assertIsNotNone(fit) - - -class TestReadScript(unittest.TestCase): - test_directory = "./" - - @patch("peakipy.cli.main.read") - def test_main(self, MockRead): - args = {"": "hello", "": "data"} - read = MockRead(args) - self.assertIsNotNone(read) - - def test_read_pipe_peaklist(self): - args = { - "path": f"{self.test_directory}/test_pipe.tab", - "data_path": f"{self.test_directory}/test_pipe.ft2", - "dims": [0, 1, 2], - "fmt": PeaklistFormat.pipe, - } - peaklist = Peaklist(**args) - self.assertIsNotNone(peaklist) - self.assertIs(len(peaklist.df), 3) - # self.assertIs(peaklist.df.X_AXISf.iloc[0], 323.019) - self.assertIs(peaklist.fmt.value, "pipe") - # self.assertEqual(peaklist.df.ASS.iloc[0], "None") - # self.assertEqual(peaklist.df.ASS.iloc[1], "None_dummy_1") - - -class TestSpecScript(unittest.TestCase): - @patch("peakipy.cli.main.spec") - def test_main(self, MockSpec): - args = {"": "hello", "": "data"} - spec = MockSpec(args) - self.assertIsNotNone(spec) - - -def test_load_config_existing(): - config_path = Path("test_config.json") - # Create a dummy existing config file - with open(config_path, "w") as f: - json.dump({"key1": "value1"}, f) - - loaded_config = load_config(config_path) - - assert loaded_config == {"key1": "value1"} - - # Clean up - config_path.unlink() - - -def test_load_config_nonexistent(): - config_path = Path("test_config.json") - - loaded_config = load_config(config_path) - - assert loaded_config == {} - - -def test_write_config(): - config_path = Path("test_config.json") - config_kvs = {"key1": "value1", "key2": "value2"} - - write_config(config_path, config_kvs) - - # Check if the config file is created correctly - assert config_path.exists() - - # Check if the config file content is correct - with open(config_path) as f: - created_config = json.load(f) - assert created_config == {"key1": "value1", "key2": "value2"} - - # Clean up - config_path.unlink() - - -def test_update_config_file_existing(): - config_path = Path("test_config.json") - # Create a dummy existing config file - with open(config_path, "w") as f: - json.dump({"key1": "value1"}, f) - - config_kvs = {"key2": "value2", "key3": "value3"} - updated_config = update_config_file(config_path, config_kvs) - - assert updated_config == {"key1": "value1", "key2": "value2", "key3": "value3"} - - # Clean up - config_path.unlink() - - -def test_update_config_file_nonexistent(): - config_path = Path("test_config.json") - config_kvs = {"key1": "value1", "key2": "value2"} - updated_config = update_config_file(config_path, config_kvs) - - assert updated_config == {"key1": "value1", "key2": "value2"} - - # Clean up - config_path.unlink() - - -@pytest.fixture -def sample_data(): - return np.zeros((10, 10)) - - -@pytest.fixture -def sample_peak(): - peak_data = {"X_AXIS": [5], "Y_AXIS": [5], "XW": [2], "YW": [2]} - return pd.DataFrame(peak_data).iloc[0] - - -def test_peak_limits_max_min(sample_peak, sample_data): - limits = PeakLimits(sample_peak, sample_data) - - assert limits.max_x == 8 - assert limits.max_y == 8 - assert limits.min_x == 3 - assert limits.min_y == 3 - - -def test_peak_limits_boundary(sample_data): - peak_data = {"X_AXIS": [8], "Y_AXIS": [8], "XW": [2], "YW": [2]} - peak = pd.DataFrame(peak_data).iloc[0] - limits = PeakLimits(peak, sample_data) - - assert limits.max_x == 10 - assert limits.max_y == 10 - assert limits.min_x == 6 - assert limits.min_y == 6 - - -def test_peak_limits_at_boundary(sample_data): - peak_data = {"X_AXIS": [0], "Y_AXIS": [0], "XW": [2], "YW": [2]} - peak = pd.DataFrame(peak_data).iloc[0] - limits = PeakLimits(peak, sample_data) - - assert limits.max_x == 3 - assert limits.max_y == 3 - assert limits.min_x == 0 - assert limits.min_y == 0 - - -def test_peak_limits_outside_boundary(sample_data): - peak_data = {"X_AXIS": [15], "Y_AXIS": [15], "XW": [2], "YW": [2]} - peak = pd.DataFrame(peak_data).iloc[0] - with pytest.raises(AssertionError): - limits = PeakLimits(peak, sample_data) - - -def test_peak_limits_1d_data(): - data = np.zeros(10) - peak_data = {"X_AXIS": [5], "Y_AXIS": [0], "XW": [2], "YW": [0]} - peak = pd.DataFrame(peak_data).iloc[0] - with pytest.raises(IndexError): - limits = PeakLimits(peak, data) - - -def test_get_lineshape_function(): - assert get_lineshape_function(Lineshape.PV) == pvoigt2d - assert get_lineshape_function(Lineshape.L) == pvoigt2d - assert get_lineshape_function(Lineshape.G) == pvoigt2d - assert get_lineshape_function(Lineshape.G_L) == gaussian_lorentzian - assert get_lineshape_function(Lineshape.PV_G) == pv_g - assert get_lineshape_function(Lineshape.PV_L) == pv_l - assert get_lineshape_function(Lineshape.PV_PV) == pv_pv - assert get_lineshape_function(Lineshape.V) == voigt2d - with pytest.raises(Exception): - get_lineshape_function("bla") - - -if __name__ == "__main__": - unittest.main(verbosity=2) diff --git a/test/test_fit.py b/test/test_fit.py deleted file mode 100644 index c41d0ebf..00000000 --- a/test/test_fit.py +++ /dev/null @@ -1,460 +0,0 @@ -from pytest import fixture - -import pandas as pd -import numpy as np -from lmfit import Parameters, Model -from lmfit.model import ModelResult - -from peakipy.cli.fit import ( - get_fit_peaks_result_validation_model, - FitPeaksResultRowPVPV, - FitPeaksResultRowVoigt, - FitPeaksResultRowGLPV, - filter_peak_clusters_by_max_cluster_size, - set_parameters_to_fix_during_fit, - unpack_fitted_parameters_for_lineshape, - get_default_lineshape_param_names, - split_parameter_sets_by_peak, - get_prefix_from_parameter_names, - create_parameter_dict, - perform_initial_lineshape_fit_on_cluster_of_peaks, - merge_unpacked_parameters_with_metadata, - add_vclist_to_df, - update_cluster_df_with_fit_statistics, - rename_columns_for_compatibility, - FitPeaksArgs, - FitPeaksInput, -) -from peakipy.lineshapes import Lineshape, pvoigt2d - - -def test_get_fit_peaks_result_validation_model_PVPV(): - validation_model = get_fit_peaks_result_validation_model(Lineshape.PV_PV) - assert validation_model == FitPeaksResultRowPVPV - - -def test_get_fit_peaks_result_validation_model_G(): - validation_model = get_fit_peaks_result_validation_model(Lineshape.G) - assert validation_model == FitPeaksResultRowGLPV - - -def test_get_fit_peaks_result_validation_model_L(): - validation_model = get_fit_peaks_result_validation_model(Lineshape.L) - assert validation_model == FitPeaksResultRowGLPV - - -def test_get_fit_peaks_result_validation_model_PV(): - validation_model = get_fit_peaks_result_validation_model(Lineshape.PV) - assert validation_model == FitPeaksResultRowGLPV - - -def test_get_fit_peaks_result_validation_model_V(): - validation_model = get_fit_peaks_result_validation_model(Lineshape.V) - assert validation_model == FitPeaksResultRowVoigt - - -def test_filter_groups_by_max_cluster_size(): - groups = pd.DataFrame( - dict( - col1=[1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 4, 5, 6, 7], - col2=[1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7], - ) - ).groupby("col1") - max_cluster_size = 3 - filtered_groups = filter_peak_clusters_by_max_cluster_size(groups, max_cluster_size) - filtered_group_names = filtered_groups.col1.unique() - expected_group_names = np.array([3, 4, 5, 6, 7]) - np.testing.assert_array_equal(filtered_group_names, expected_group_names) - - -def test_set_parameters_to_fix_during_fit(): - parameter_set = Parameters() - parameter_set.add("test1", vary=True) - modified_parameter_set, float_str = set_parameters_to_fix_during_fit( - parameter_set, ["test1"] - ) - assert modified_parameter_set["test1"].vary == False - - -@fixture -def parameters_set_with_two_variables(): - parameter_set = Parameters() - parameter_set.add("prefix1_test1", vary=True) - parameter_set.add("prefix1_test2", vary=True) - return parameter_set - - -def test_set_parameters_to_fix_during_fit_2(parameters_set_with_two_variables): - modified_parameter_set, float_str = set_parameters_to_fix_during_fit( - parameters_set_with_two_variables, ["prefix1_test1", "prefix1_test2"] - ) - assert ( - modified_parameter_set["prefix1_test2"].vary - == modified_parameter_set["prefix1_test1"].vary - == False - ) - - -def test_set_parameters_to_fix_during_fit_3(): - parameter_set = Parameters() - parameter_set.add("test1", vary=True) - parameter_set.add("test2", vary=True) - modified_parameter_set, float_str = set_parameters_to_fix_during_fit( - parameter_set, ["test2"] - ) - assert ( - modified_parameter_set["test1"].vary - != modified_parameter_set["test2"].vary - == False - ) - - -def test_set_parameters_to_fix_during_fit_None(): - parameter_set = Parameters() - parameter_set.add("test1", vary=True) - parameter_set.add("test2", vary=True) - modified_parameter_set, float_str = set_parameters_to_fix_during_fit( - parameter_set, None - ) - assert ( - modified_parameter_set["test1"].vary - == modified_parameter_set["test2"].vary - == True - ) - - -def test_set_parameters_to_fix_during_fit_None_str(): - parameter_set = Parameters() - parameter_set.add("test1", vary=True) - parameter_set.add("test2", vary=True) - modified_parameter_set, float_str = set_parameters_to_fix_during_fit( - parameter_set, ["None"] - ) - assert ( - modified_parameter_set["test1"].vary - == modified_parameter_set["test2"].vary - == True - ) - - -def test_update_cluster_df_with_fit_statistics(): - result = ModelResult(Model(pvoigt2d), None, None) - result.aic = None - result.bic = None - data = [ - dict( - chisqr=None, - redchi=None, - residual_sum=None, - aic=None, - bic=None, - nfev=0, - ndata=0, - ) - ] - expected_cluster_df = pd.DataFrame(data) - actual_cluster_df = update_cluster_df_with_fit_statistics( - expected_cluster_df, result - ) - pd.testing.assert_frame_equal(actual_cluster_df, expected_cluster_df) - - -def test_rename_columns_for_compatibility(): - df = pd.DataFrame( - [ - dict( - amplitude=1, - amplitude_stderr=1, - X_AXIS=1, - Y_AXIS=1, - ASS="None", - MEMCNT=1, - X_RADIUS=1, - Y_RADIUS=1, - ) - ] - ) - expected_columns = [ - "amp", - "amp_err", - "init_center_x", - "init_center_y", - "assignment", - "memcnt", - "x_radius", - "y_radius", - ] - actual_columns = rename_columns_for_compatibility(df).columns - assert all([i == j for i, j in zip(actual_columns, expected_columns)]) - - -def test_get_default_param_names_pseudo_voigt(): - assert get_default_lineshape_param_names(Lineshape.PV) == [ - "amplitude", - "center_x", - "center_y", - "sigma_x", - "sigma_y", - "fraction", - ] - - -def test_get_default_param_names_gaussian(): - assert get_default_lineshape_param_names(Lineshape.G) == [ - "amplitude", - "center_x", - "center_y", - "sigma_x", - "sigma_y", - "fraction", - ] - - -def test_get_default_param_names_lorentzian(): - assert get_default_lineshape_param_names(Lineshape.L) == [ - "amplitude", - "center_x", - "center_y", - "sigma_x", - "sigma_y", - "fraction", - ] - - -def test_get_default_param_names_pv_pv(): - assert get_default_lineshape_param_names(Lineshape.PV_PV) == [ - "amplitude", - "center_x", - "center_y", - "sigma_x", - "sigma_y", - "fraction_x", - "fraction_y", - ] - - -def test_get_default_param_names_voigt(): - assert get_default_lineshape_param_names(Lineshape.V) == [ - "amplitude", - "center_x", - "center_y", - "sigma_x", - "sigma_y", - "gamma_x", - "gamma_y", - "fraction", - ] - - -def test_split_parameter_sets_by_peak(default_pseudo_voigt_parameter_names): - # the second element of each tuple actually contains an - # lmfit.Parameter object - params = [ - ("p1_amplitude", "amplitude"), - ("p1_center_x", "center_x"), - ("p1_center_y", "center_y"), - ("p1_sigma_x", "sigma_x"), - ("p1_sigma_y", "sigma_y"), - ("p1_fraction", "fraction"), - ("p2_amplitude", "amplitude"), - ("p2_center_x", "center_x"), - ("p2_center_y", "center_y"), - ("p2_sigma_x", "sigma_x"), - ("p2_sigma_y", "sigma_y"), - ("p2_fraction", "fraction"), - ("p3_amplitude", "amplitude"), - ("p3_center_x", "center_x"), - ("p3_center_y", "center_y"), - ("p3_sigma_x", "sigma_x"), - ("p3_sigma_y", "sigma_y"), - ("p3_fraction", "fraction"), - ] - expected_result = [ - [ - ("p1_amplitude", "amplitude"), - ("p1_center_x", "center_x"), - ("p1_center_y", "center_y"), - ("p1_sigma_x", "sigma_x"), - ("p1_sigma_y", "sigma_y"), - ("p1_fraction", "fraction"), - ], - [ - ("p2_amplitude", "amplitude"), - ("p2_center_x", "center_x"), - ("p2_center_y", "center_y"), - ("p2_sigma_x", "sigma_x"), - ("p2_sigma_y", "sigma_y"), - ("p2_fraction", "fraction"), - ], - [ - ("p3_amplitude", "amplitude"), - ("p3_center_x", "center_x"), - ("p3_center_y", "center_y"), - ("p3_sigma_x", "sigma_x"), - ("p3_sigma_y", "sigma_y"), - ("p3_fraction", "fraction"), - ], - ] - expected_result_parameter_names = [[j[0] for j in i] for i in expected_result] - split_parameter_names = [ - [j[0] for j in i] - for i in split_parameter_sets_by_peak( - default_pseudo_voigt_parameter_names, params - ) - ] - assert split_parameter_names == expected_result_parameter_names - - -@fixture -def default_pseudo_voigt_parameter_names(): - return Model(pvoigt2d).param_names - - -def test_get_prefix_from_parameter_names(default_pseudo_voigt_parameter_names): - parameter_items_with_prefixes = [ - ("p1_amplitude", "amplitude"), - ("p1_center_x", "center_x"), - ("p1_center_y", "center_y"), - ("p1_sigma_x", "sigma_x"), - ("p1_sigma_y", "sigma_y"), - ("p1_fraction", "fraction"), - ] - expected_result = "p1_" - actual_result = get_prefix_from_parameter_names( - default_pseudo_voigt_parameter_names, parameter_items_with_prefixes - ) - assert expected_result == actual_result - - -@fixture -def pseudo_voigt_model_result(): - m1 = Model(pvoigt2d, prefix="p1_") - m2 = Model(pvoigt2d, prefix="p2_") - model = m1 + m2 - params = model.make_params() - model_result = ModelResult(model, params) - return model_result - - -def test_create_parameter_dict(pseudo_voigt_model_result): - prefix = "p1_" - params = list(pseudo_voigt_model_result.params.items())[:6] - expected_result = dict( - prefix="p1_", - amplitude=1.0, - amplitude_stderr=None, - center_x=0.5, - center_x_stderr=None, - center_y=0.5, - center_y_stderr=None, - sigma_x=1.0, - sigma_x_stderr=None, - sigma_y=1.0, - sigma_y_stderr=None, - fraction=0.5, - fraction_stderr=None, - ) - actual_result = create_parameter_dict(prefix, params) - assert expected_result == actual_result - - -def test_unpack_fitted_parameters_for_lineshape_PV(pseudo_voigt_model_result): - expected_params = [ - dict( - prefix="p1_", - plane=0, - amplitude=1.0, - amplitude_stderr=None, - center_x=0.5, - center_x_stderr=None, - center_y=0.5, - center_y_stderr=None, - sigma_x=1.0, - sigma_x_stderr=None, - sigma_y=1.0, - sigma_y_stderr=None, - fraction=0.5, - fraction_stderr=None, - ), - dict( - prefix="p2_", - plane=0, - amplitude=1.0, - amplitude_stderr=None, - center_x=0.5, - center_x_stderr=None, - center_y=0.5, - center_y_stderr=None, - sigma_x=1.0, - sigma_x_stderr=None, - sigma_y=1.0, - sigma_y_stderr=None, - fraction=0.5, - fraction_stderr=None, - ), - ] - unpacked_params = unpack_fitted_parameters_for_lineshape( - Lineshape.PV, list(pseudo_voigt_model_result.params.items()), plane_number=0 - ) - assert expected_params == unpacked_params - - -def test_merge_unpacked_parameters_with_metadata(): - cluster_fit_df = pd.DataFrame( - dict( - plane=[0, 1, 2, 3, 0, 1, 2, 3], - prefix=["_p1_", "_p1_", "_p1_", "_p1_", "_p2_", "_p2_", "_p2_", "_p2_"], - ) - ) - peak_df = pd.DataFrame(dict(ASS=["p1", "p2"], data=["p1_data", "p2_data"])) - expected_result = pd.DataFrame( - dict( - plane=[0, 1, 2, 3, 0, 1, 2, 3], - prefix=["_p1_", "_p1_", "_p1_", "_p1_", "_p2_", "_p2_", "_p2_", "_p2_"], - ASS=["p1", "p1", "p1", "p1", "p2", "p2", "p2", "p2"], - data=[ - "p1_data", - "p1_data", - "p1_data", - "p1_data", - "p2_data", - "p2_data", - "p2_data", - "p2_data", - ], - ) - ) - actual_result = merge_unpacked_parameters_with_metadata(cluster_fit_df, peak_df) - assert expected_result.equals(actual_result) - - -def test_add_vclist_to_df(): - args = FitPeaksArgs( - noise=0, uc_dics={}, lineshape=Lineshape.PV, vclist_data=np.array([1, 2, 3]) - ) - fit_peaks_input = FitPeaksInput( - args=args, data=None, config=None, plane_numbers=None - ) - df = pd.DataFrame(dict(plane=[0, 1, 2])) - expected_df = pd.DataFrame(dict(plane=[0, 1, 2], vclist=[1, 2, 3])) - actual_df = add_vclist_to_df(fit_peaks_input, df) - assert actual_df.equals(expected_df) - - -def test_add_vclist_to_df_plane_order(): - args = FitPeaksArgs( - noise=0, uc_dics={}, lineshape=Lineshape.PV, vclist_data=np.array([1, 2, 3]) - ) - fit_peaks_input = FitPeaksInput( - args=args, data=None, config=None, plane_numbers=None - ) - df = pd.DataFrame(dict(plane=[2, 1, 0])) - expected_df = pd.DataFrame(dict(plane=[2, 1, 0], vclist=[3, 2, 1])) - actual_df = add_vclist_to_df(fit_peaks_input, df) - assert actual_df.equals(expected_df) - - -# def test_perform_initial_lineshape_fit_on_cluster_of_peaks(pseudo_voigt_model_result): -# expected_result = pseudo_voigt_model_result -# actual_result = perform_initial_lineshape_fit_on_cluster_of_peaks() -# assert expected_result == actual_result From d901e91d8b86ddd0c1058f620a1c8752597f5868 Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Mon, 27 May 2024 22:37:27 -0400 Subject: [PATCH 35/37] added simulation and tests --- Makefile | 1 + peakipy/cli/edit.py | 2 +- peakipy/cli/main.py | 23 ++++--- peakipy/fitting.py | 8 ++- peakipy/utils.py | 10 +++ test/test_cli.py | 45 +++++++++++++ test/test_fitting.py | 112 +++++++++++++++++++++++++++++++ test/test_simulation.py | 143 ++++++++++++++++++++++++++++++++++++++++ 8 files changed, 332 insertions(+), 12 deletions(-) create mode 100644 test/test_simulation.py diff --git a/Makefile b/Makefile index 432dee0d..908cac1e 100644 --- a/Makefile +++ b/Makefile @@ -7,6 +7,7 @@ coverage: test/test_utils.py \ test/test_main.py \ test/test_cli.py \ + test/test_simulation.py \ test/test_plotting.py coverage-html: diff --git a/peakipy/cli/edit.py b/peakipy/cli/edit.py index 3f4d31ef..2e2d6cb0 100644 --- a/peakipy/cli/edit.py +++ b/peakipy/cli/edit.py @@ -98,7 +98,7 @@ def peakipy_data(self): def make_temp_files(self): # Temp files - self.TEMP_PATH = Path("tmp") + self.TEMP_PATH = self.path.parent / Path("tmp") self.TEMP_PATH.mkdir(parents=True, exist_ok=True) self.TEMP_OUT_CSV = self.TEMP_PATH / Path("tmp_out.csv") diff --git a/peakipy/cli/main.py b/peakipy/cli/main.py index 6b3b0aed..f22998c3 100644 --- a/peakipy/cli/main.py +++ b/peakipy/cli/main.py @@ -34,6 +34,8 @@ get_vclist, ) from peakipy.utils import ( + mkdir_tmp_dir, + create_log_path, run_log, df_to_rich_table, write_config, @@ -81,9 +83,6 @@ ) app = typer.Typer() -tmp_path = Path("tmp") -tmp_path.mkdir(exist_ok=True) -log_path = Path("log.txt") peaklist_path_help = "Path to peaklist" @@ -185,6 +184,8 @@ def read( Clusters of peaks are selected """ + mkdir_tmp_dir(peaklist_path.parent) + log_path = create_log_path(peaklist_path.parent) clust_args = { "struc_el": struc_el, @@ -251,7 +252,10 @@ def read( thres = peaks.thres if cluster: - peaks.clusters(thres=thres, **clust_args, l_struc=None) + if struc_el == StrucEl.mask_method: + peaks.mask_method(overlap=struc_size[0]) + else: + peaks.clusters(thres=thres, **clust_args, l_struc=None) else: pass @@ -288,7 +292,7 @@ def read( config_dic = dict(config_kvs) write_config(config_path, config_dic) - run_log() + run_log(log_path) print( f"""[green] @@ -382,6 +386,8 @@ def fit( verb : bool Print what's going on """ + tmp_path = mkdir_tmp_dir(peaklist_path.parent) + log_path = create_log_path(peaklist_path.parent) # number of CPUs n_cpu = cpu_count() @@ -412,7 +418,7 @@ def fit( args = get_vclist(vclist, args) # plot results or not - log_file = open(tmp_path / log_path, "w") + log_file = open(log_path, "w") uc_dics = {"f1": peakipy_data.uc_f1, "f2": peakipy_data.uc_f2} args["uc_dics"] = uc_dics @@ -488,7 +494,7 @@ def fit( [/green] """ ) - run_log() + run_log(log_path) fits_help = "CSV file containing peakipy fits" @@ -547,6 +553,7 @@ def check( verb : bool verbose mode """ + log_path = create_log_path(fits.parent) columns_to_print = [ "assignment", "clustid", @@ -673,7 +680,7 @@ def check( plot_data, pdf, individual, label, ccpn_flag, show, test ) - run_log() + run_log(log_path) if __name__ == "__main__": diff --git a/peakipy/fitting.py b/peakipy/fitting.py index e99f6aa4..1bd23263 100644 --- a/peakipy/fitting.py +++ b/peakipy/fitting.py @@ -5,7 +5,7 @@ import numpy as np from numpy import sqrt import pandas as pd -from rich import console +from rich import print from lmfit import Model, Parameters, Parameter from lmfit.model import ModelResult from pydantic import BaseModel @@ -941,7 +941,7 @@ def perform_initial_lineshape_fit_on_cluster_of_peaks( ) if verbose: - console.print(out.fit_report(), style="bold") + print(out.fit_report()) z_sim = mod.eval(XY=XY, params=out.params) z_sim[~mask] = np.nan @@ -995,7 +995,9 @@ def refit_peak_cluster_with_constraints( def merge_unpacked_parameters_with_metadata(cluster_fit_df, group_of_peaks_df): group_of_peaks_df["prefix"] = group_of_peaks_df.ASS.apply(to_prefix) - merged_cluster_fit_df = cluster_fit_df.merge(group_of_peaks_df, on="prefix") + merged_cluster_fit_df = cluster_fit_df.merge( + group_of_peaks_df, on="prefix", suffixes=["", "_init"] + ) return merged_cluster_fit_df diff --git a/peakipy/utils.py b/peakipy/utils.py index 310ee656..5f6d68b3 100644 --- a/peakipy/utils.py +++ b/peakipy/utils.py @@ -29,6 +29,16 @@ ] +def mkdir_tmp_dir(base_path: Path = Path("./")): + tmp_dir = base_path / "tmp" + tmp_dir.mkdir(exist_ok=True) + return tmp_dir + + +def create_log_path(base_path: Path = Path("./")): + return base_path / "run_log.txt" + + def run_log(log_name="run_log.txt"): """Write log file containing time script was run and with which arguments""" with open(log_name, "a") as log: diff --git a/test/test_cli.py b/test/test_cli.py index 3c14cd4b..d89454bd 100644 --- a/test/test_cli.py +++ b/test/test_cli.py @@ -7,6 +7,7 @@ import peakipy.cli.check_panel import peakipy.cli.edit_panel from peakipy.cli.main import PeaklistFormat, Lineshape +from peakipy.io import StrucEl @pytest.fixture @@ -42,6 +43,50 @@ def test_read_main_with_default_sparky(protein_L): peakipy.cli.main.read(**args) +def test_read_main_with_strucel_square(protein_L): + args = dict( + peaklist_path=protein_L / Path("peaks.sparky"), + data_path=protein_L / Path("test1.ft2"), + peaklist_format=PeaklistFormat.sparky, + struc_el=StrucEl.square, + ) + peakipy.cli.main.read(**args) + + +def test_read_main_with_strucel_rectangle(protein_L): + args = dict( + peaklist_path=protein_L / Path("peaks.sparky"), + data_path=protein_L / Path("test1.ft2"), + peaklist_format=PeaklistFormat.sparky, + struc_el=StrucEl.rectangle, + struc_size=(3, 3), + ) + peakipy.cli.main.read(**args) + + +def test_read_main_with_mask_method(protein_L): + args = dict( + peaklist_path=protein_L / Path("peaks.sparky"), + data_path=protein_L / Path("test1.ft2"), + peaklist_format=PeaklistFormat.sparky, + struc_el=StrucEl.mask_method, + struc_size=(1, 1), + ) + peakipy.cli.main.read(**args) + + +def test_read_main_with_mask_method_fuda(protein_L): + args = dict( + peaklist_path=protein_L / Path("peaks.sparky"), + data_path=protein_L / Path("test1.ft2"), + peaklist_format=PeaklistFormat.sparky, + struc_el=StrucEl.mask_method, + struc_size=(1, 1), + fuda=True, + ) + peakipy.cli.main.read(**args) + + def test_fit_main_with_default(protein_L): args = dict( peaklist_path=protein_L / Path("test.csv"), diff --git a/test/test_fitting.py b/test/test_fitting.py index 7291885d..05855a66 100644 --- a/test/test_fitting.py +++ b/test/test_fitting.py @@ -1,4 +1,5 @@ import unittest +from unittest.mock import MagicMock from pathlib import Path from collections import namedtuple @@ -51,6 +52,7 @@ rename_columns_for_compatibility, FitPeaksArgs, FitPeaksInput, + FitResult, ) from peakipy.lineshapes import Lineshape, pvoigt2d, pv_pv @@ -1142,3 +1144,113 @@ def test_add_vclist_to_df_plane_order(): # expected_result = pseudo_voigt_model_result # actual_result = perform_initial_lineshape_fit_on_cluster_of_peaks() # assert expected_result == actual_result +# Mock FitPeakClusterInput class for testing purposes +class MockFitPeakClusterInput: + def __init__( + self, + mod, + peak_slices, + XY_slices, + p_guess, + weights, + fit_method, + mask, + XY, + first_plane_data, + last_peak, + group, + min_x, + min_y, + max_x, + max_y, + verbose, + uc_dics, + ): + self.mod = mod + self.peak_slices = peak_slices + self.XY_slices = XY_slices + self.p_guess = p_guess + self.weights = weights + self.fit_method = fit_method + self.mask = mask + self.XY = XY + self.first_plane_data = first_plane_data + self.last_peak = last_peak + self.group = group + self.min_x = min_x + self.min_y = min_y + self.max_x = max_x + self.max_y = max_y + self.verbose = verbose + self.uc_dics = uc_dics + + +@pytest.fixture +def fit_peak_cluster_input(): + mod = MagicMock() + mod.fit = MagicMock( + return_value=MagicMock( + params="params", fit_report=MagicMock(return_value="fit_report") + ) + ) + mod.eval = MagicMock(return_value=np.array([2.0, 1.0, 2.0])) + + return MockFitPeakClusterInput( + mod=mod, + peak_slices="peak_slices", + XY_slices="XY_slices", + p_guess="p_guess", + weights="weights", + fit_method="fit_method", + mask=np.array([True, False, True]), + XY=(np.array([0, 1, 2]), np.array([0, 1, 2])), + first_plane_data=np.array([2.0, 1.0, 2.0]), + last_peak="last_peak", + group="group", + min_x="min_x", + min_y="min_y", + max_x="max_x", + max_y="max_y", + verbose=True, + uc_dics="uc_dics", + ) + + +def test_perform_initial_lineshape_fit_on_cluster_of_peaks(fit_peak_cluster_input): + + result = perform_initial_lineshape_fit_on_cluster_of_peaks(fit_peak_cluster_input) + + # Check if result is an instance of FitResult + assert isinstance(result, FitResult) + + # Verify returned values + assert result.out.params == "params" + np.testing.assert_array_equal(result.mask, np.array([True, False, True])) + assert result.fit_str == "" + assert result.log == "" + assert result.group == "group" + assert result.uc_dics == "uc_dics" + assert result.min_x == "min_x" + assert result.min_y == "min_y" + assert result.max_x == "max_x" + assert result.max_y == "max_y" + np.testing.assert_array_equal(result.X, np.array([0, 1, 2])) + np.testing.assert_array_equal(result.Y, np.array([0, 1, 2])) + np.testing.assert_array_equal(result.Z, np.array([2.0, np.nan, 2.0])) + np.testing.assert_array_equal(result.Z_sim, np.array([2.0, np.nan, 2.0])) + assert result.peak_slices == "peak_slices" + assert result.XY_slices == "XY_slices" + assert result.weights == "weights" + assert result.mod == fit_peak_cluster_input.mod + + # Check if mod.fit and mod.eval were called with correct arguments + fit_peak_cluster_input.mod.fit.assert_called_once_with( + "peak_slices", + XY="XY_slices", + params="p_guess", + weights="weights", + method="fit_method", + ) + # fit_peak_cluster_input.mod.eval.assert_called_once_with( + # XY=(np.array([0,1,2]), np.array([0,1,2])), params='params' + # ) diff --git a/test/test_simulation.py b/test/test_simulation.py new file mode 100644 index 00000000..26c11a5b --- /dev/null +++ b/test/test_simulation.py @@ -0,0 +1,143 @@ +""" Simultate some peaks add noise and fit using peakipy """ + +import numpy as np +import pandas as pd +from lmfit import Model +from skimage.filters import threshold_otsu +from nmrglue.fileio.fileiobase import unit_conversion +from mpl_toolkits.mplot3d import axes3d +import matplotlib.pyplot as plt + +from peakipy.lineshapes import pvoigt2d, Lineshape +from peakipy.fitting import ( + simulate_lineshapes_from_fitted_peak_parameters, + make_models, + make_meshgrid, + FitPeaksArgs, + FitPeaksInput, + fit_peak_clusters, +) + + +uc_x = unit_conversion(400, False, 2400, 600, 4800) +uc_y = unit_conversion(200, False, 1200, 60, 7200) +uc_dics = {"f1": uc_y, "f2": uc_x} + +p1 = dict( + amp=10, + center_x=200.0, + center_y=100.0, + sigma_x=10.0, + sigma_y=18.0, + fraction=0.5, + lineshape="PV", + ASS="one", +) + +p2 = dict( + amp=12, + center_x=220.0, + center_y=130.0, + sigma_x=20.0, + sigma_y=15.0, + fraction=0.5, + lineshape="PV", + ASS="two", +) + +peak_parameters = pd.DataFrame([p1, p2]) +peak_parameters["X_AXIS"] = peak_parameters.center_x +peak_parameters["Y_AXIS"] = peak_parameters.center_y +peak_parameters["X_AXISf"] = peak_parameters.center_x +peak_parameters["Y_AXISf"] = peak_parameters.center_y +peak_parameters["X_RADIUS"] = 60 +peak_parameters["Y_RADIUS"] = 30 +peak_parameters["X_PPM"] = peak_parameters.center_x.apply(uc_x.ppm) +peak_parameters["Y_PPM"] = peak_parameters.center_x.apply(uc_x.ppm) +peak_parameters["XW"] = peak_parameters.sigma_x +peak_parameters["YW"] = peak_parameters.sigma_y +peak_parameters["CLUSTID"] = 1 +peak_parameters["MEMCNT"] = peak_parameters.shape[0] +peak_parameters["plane"] = 0 + +x = 400 +y = 200 +data_shape_Y_X = (y, x) +data_shape_X_Y = (x, y) +XY = make_meshgrid(data_shape_Y_X) +X, Y = XY +Z_sim = np.random.normal(loc=0.0, scale=0.0001, size=data_shape_Y_X) +Z_sim_singles = [] +Z_sim, Z_sim_singles = simulate_lineshapes_from_fitted_peak_parameters( + peak_parameters=peak_parameters, + XY=XY, + sim_data=Z_sim, + sim_data_singles=Z_sim_singles, +) + +fit_peaks_args = FitPeaksArgs( + noise=threshold_otsu(Z_sim), + uc_dics=uc_dics, + lineshape=Lineshape.PV, + max_cluster_size=10, + reference_plane_indices=[], + xy_bounds=None, + initial_fit_threshold=None, + vclist=None, +) + +fit_peaks_input = FitPeaksInput( + fit_peaks_args, Z_sim.reshape(1, y, x), dict(dims=[0, 1]), plane_numbers=[0] +) + +fit_peaks_result = fit_peak_clusters(peak_parameters, fit_peaks_input) + + +def test_fit_from_simulated_data(): + pd.testing.assert_series_equal( + fit_peaks_result.df.center_x, + fit_peaks_result.df.center_x_init, + check_exact=False, + check_names=False, + rtol=1e-3, + ) + pd.testing.assert_series_equal( + fit_peaks_result.df.center_y, + fit_peaks_result.df.center_y_init, + check_exact=False, + check_names=False, + rtol=1e-3, + ) + pd.testing.assert_series_equal( + fit_peaks_result.df.sigma_x, + fit_peaks_result.df.sigma_x_init, + check_exact=False, + check_names=False, + rtol=1e-2, + ) + pd.testing.assert_series_equal( + fit_peaks_result.df.sigma_y, + fit_peaks_result.df.sigma_y_init, + check_exact=False, + check_names=False, + rtol=1e-2, + ) + + +def test_fit_from_simulated_data_jack_knife(): + fit_peaks_input = FitPeaksInput( + fit_peaks_args, Z_sim.reshape(1, y, x), dict(dims=[0, 1]), plane_numbers=[0] + ) + fit_peaks_input.args.jack_knife_sample_errors = True + fit_peaks_result = fit_peak_clusters(peak_parameters, fit_peaks_input) + + +# def plot3D(X,Y,Z,Z_singles): +# fig = plt.figure() +# ax = fig.add_subplot(projection='3d') +# ax.plot_wireframe(X,Y,Z) +# for Z_single in Z_singles: +# ax.plot_surface(X, Y, Z_single,alpha=0.5) +# plt.xlabel("X") +# plt.ylabel("Y") +# plt.show() From 76e19e839a8b8e2415c633e7468eca829b0cdffd Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Thu, 30 May 2024 09:01:52 -0400 Subject: [PATCH 36/37] refactored --- peakipy/cli/check_panel.py | 137 ---------------- peakipy/cli/edit_panel.py | 148 ----------------- peakipy/cli/main.py | 315 ++++++++++++++++++++++++++++++++++--- peakipy/io.py | 5 +- test/test_cli.py | 37 ++--- 5 files changed, 309 insertions(+), 333 deletions(-) delete mode 100644 peakipy/cli/check_panel.py delete mode 100644 peakipy/cli/edit_panel.py diff --git a/peakipy/cli/check_panel.py b/peakipy/cli/check_panel.py deleted file mode 100644 index 5dc06cd9..00000000 --- a/peakipy/cli/check_panel.py +++ /dev/null @@ -1,137 +0,0 @@ -from pathlib import Path -from dataclasses import dataclass, field -from functools import lru_cache -import panel as pn -import pandas as pd -import typer - -from peakipy.cli.main import check, validate_fit_dataframe - -pn.extension() - - -@dataclass -class Data: - fits_path: Path = Path("./fits.csv") - data_path: Path = Path("./test.ft2") - config_path: Path = Path("./peakipy.config") - _df: pd.DataFrame = field(init=False) - - def load_dataframe(self): - self._df = validate_fit_dataframe(pd.read_csv(self.fits_path)) - - @property - def df(self): - return self._df - - -@lru_cache(maxsize=1) -def data_singleton(): - return Data() - - -def get_cluster(cluster): - data = data_singleton() - cluster_groups = data.df.groupby("clustid") - cluster_group = cluster_groups.get_group(cluster) - df_pane = pn.widgets.Tabulator( - cluster_group[ - [ - "assignment", - "clustid", - "memcnt", - "plane", - "amp", - "height", - "center_x_ppm", - "center_y_ppm", - "fwhm_x_hz", - "fwhm_y_hz", - "lineshape", - ] - ], - selectable=False, - disabled=True, - ) - return df_pane - - -@dataclass -class PlotContainer: - main_figure: pn.pane.Plotly - residual_figure: pn.pane.Plotly - - -def create_plotly_pane(cluster, plane): - data = data_singleton() - fig, residual_fig = check( - fits=data.fits_path, - data_path=data.data_path, - clusters=[cluster], - plane=[plane], - # config_path=data.config_path, - plotly=True, - ) - - fig["layout"].update(height=800, width=800) - fig = fig.to_dict() - residual_fig = residual_fig.to_dict() - return pn.Column(pn.pane.Plotly(fig), pn.pane.Plotly(residual_fig)) - - -app = typer.Typer() - - -@app.command() -def create_check_panel( - fits_path: Path, - data_path: Path, - config_path: Path = Path("./peakipy.config"), - edit_panel: bool = False, - test: bool = False, -): - data = data_singleton() - data.fits_path = fits_path - data.data_path = data_path - data.config_path = config_path - data.load_dataframe() - - clusters = [(row.clustid, row.memcnt) for _, row in data.df.iterrows()] - - select_cluster = pn.widgets.Select( - name="Cluster (number of peaks)", options={f"{c} ({m})": c for c, m in clusters} - ) - select_plane = pn.widgets.Select( - name="Plane", options={f"{plane}": plane for plane in data.df.plane.unique()} - ) - result_table_pane = pn.bind(get_cluster, select_cluster) - interactive_plotly_pane = pn.bind( - create_plotly_pane, cluster=select_cluster, plane=select_plane - ) - info_pane = pn.pane.Markdown( - "Select a cluster and plane to look at from the dropdown menus" - ) - check_pane = pn.Card( - # info_pane, - # pn.Row(select_cluster, select_plane), - pn.Row( - pn.Column( - pn.Row( - pn.Card(interactive_plotly_pane, title="Fitted cluster"), - pn.Column(info_pane, select_cluster, select_plane), - ), - pn.Card(result_table_pane, title="Fitted parameters for cluster"), - ) - ), - title="Peakipy check", - ) - if edit_panel: - return check_pane - elif test: - return - else: - check_pane.show() - - -if __name__ == "__main__": - app() diff --git a/peakipy/cli/edit_panel.py b/peakipy/cli/edit_panel.py deleted file mode 100644 index f44b5faa..00000000 --- a/peakipy/cli/edit_panel.py +++ /dev/null @@ -1,148 +0,0 @@ -from pathlib import Path -from dataclasses import dataclass, field -from functools import lru_cache - -import panel as pn -from typer import Typer - -from peakipy.cli.edit import BokehScript -from peakipy.cli.check_panel import create_check_panel - -app = Typer() - -pn.extension("plotly") -pn.config.theme = "dark" - - -@dataclass -class Data: - peaklist_path: Path = Path("./test.csv") - data_path: Path = Path("./test.ft2") - _bs: BokehScript = field(init=False) - - def load_data(self): - self._bs = BokehScript(self.peaklist_path, self.data_path) - - @property - def bs(self): - return self._bs - - -@lru_cache(maxsize=1) -def data_singleton(): - return Data() - - -def update_peakipy_data_on_edit_of_table(event): - data = data_singleton() - column = event.column - row = event.row - value = event.value - data.bs.peakipy_data.df.loc[row, column] = value - data.bs.update_memcnt() - - -def panel_app(test=False): - data = data_singleton() - bs = data.bs - bokeh_pane = pn.pane.Bokeh(bs.p) - spectrum_view_settings = pn.WidgetBox( - "# Contour settings", bs.pos_neg_contour_radiobutton, bs.contour_start - ) - save_peaklist_box = pn.WidgetBox( - "# Save your peaklist", - bs.savefilename, - bs.button, - pn.layout.Divider(), - bs.exit_button, - ) - recluster_settings = pn.WidgetBox( - "# Re-cluster your peaks", - bs.clust_div, - bs.struct_el, - bs.struct_el_size, - pn.layout.Divider(), - bs.recluster_warning, - bs.recluster, - sizing_mode="stretch_width", - ) - button = pn.widgets.Button(name="Fit selected cluster(s)", button_type="primary") - fit_controls = pn.WidgetBox( - "# Fit controls", - button, - pn.layout.Divider(), - bs.select_plane, - bs.checkbox_group, - pn.layout.Divider(), - bs.select_reference_planes_help, - bs.select_reference_planes, - pn.layout.Divider(), - bs.set_initial_fit_threshold_help, - bs.set_initial_fit_threshold, - pn.layout.Divider(), - bs.select_fixed_parameters_help, - bs.select_fixed_parameters, - pn.layout.Divider(), - bs.select_lineshape_radiobuttons_help, - bs.select_lineshape_radiobuttons, - ) - - mask_adjustment_controls = pn.WidgetBox( - "# Fitting mask adjustment", bs.slider_X_RADIUS, bs.slider_Y_RADIUS - ) - - # bs.source.on_change() - def fit_peaks_button_click(event): - check_app.loading = True - bs.fit_selected(None) - check_panel = create_check_panel(bs.TEMP_OUT_CSV, bs.data_path, edit_panel=True) - check_app.objects = check_panel.objects - check_app.loading = False - - button.on_click(fit_peaks_button_click) - - def update_source_selected_indices(event): - # print(bs.tablulator_widget.selection) - # hack to make current selection however, only allows one selection - # at a time - bs.tablulator_widget._update_selection([event.value]) - bs.source.selected.indices = bs.tablulator_widget.selection - # print(bs.tablulator_widget.selection) - - bs.tablulator_widget.on_click(update_source_selected_indices) - bs.tablulator_widget.on_edit(update_peakipy_data_on_edit_of_table) - - template = pn.template.BootstrapTemplate( - title="Peakipy", - sidebar=[mask_adjustment_controls, fit_controls], - ) - spectrum = pn.Card( - pn.Column( - pn.Row( - bokeh_pane, - pn.Column(spectrum_view_settings, save_peaklist_box), - recluster_settings, - ), - bs.tablulator_widget, - ), - title="Peakipy fit", - ) - check_app = pn.Card(title="Peakipy check") - template.main.append(pn.Column(check_app, spectrum)) - if test: - return - else: - template.show() - - -@app.command() -def main(peaklist_path: Path, data_path: Path, test: bool = False): - data = data_singleton() - data.peaklist_path = peaklist_path - data.data_path = data_path - data.load_data() - panel_app(test=test) - - -if __name__ == "__main__": - app() diff --git a/peakipy/cli/main.py b/peakipy/cli/main.py index f22998c3..fce08f4f 100644 --- a/peakipy/cli/main.py +++ b/peakipy/cli/main.py @@ -1,9 +1,9 @@ #!/usr/bin/env python3 -import os import json import shutil from pathlib import Path -from enum import Enum +from functools import lru_cache +from dataclasses import dataclass, field from typing import Optional, Tuple, List, Annotated from multiprocessing import Pool, cpu_count @@ -17,10 +17,10 @@ from skimage.filters import threshold_otsu from mpl_toolkits.mplot3d import axes3d -from matplotlib import cm from matplotlib.backends.backend_pdf import PdfPages import plotly.io as pio +import panel as pn pio.templates.default = "plotly_dark" @@ -81,6 +81,57 @@ create_residual_figure, create_matplotlib_figure, ) +from peakipy.cli.edit import BokehScript + +pn.extension("plotly") +pn.config.theme = "dark" + + +@dataclass +class PlotContainer: + main_figure: pn.pane.Plotly + residual_figure: pn.pane.Plotly + + +@lru_cache(maxsize=1) +def data_singleton_edit(): + return EditData() + + +@lru_cache(maxsize=1) +def data_singleton_check(): + return CheckData() + + +@dataclass +class EditData: + peaklist_path: Path = Path("./test.csv") + data_path: Path = Path("./test.ft2") + _bs: BokehScript = field(init=False) + + def load_data(self): + self._bs = BokehScript(self.peaklist_path, self.data_path) + + @property + def bs(self): + return self._bs + + +@dataclass +class CheckData: + fits_path: Path = Path("./fits.csv") + data_path: Path = Path("./test.ft2") + config_path: Path = Path("./peakipy.config") + _df: pd.DataFrame = field(init=False) + + def load_dataframe(self): + self._df = validate_fit_dataframe(pd.read_csv(self.fits_path)) + print("Here") + + @property + def df(self): + return self._df + app = typer.Typer() @@ -497,24 +548,46 @@ def fit( run_log(log_path) +@app.command() +def edit(peaklist_path: Path, data_path: Path, test: bool = False): + data = data_singleton_edit() + data.peaklist_path = peaklist_path + data.data_path = data_path + data.load_data() + panel_app(test=test) + + fits_help = "CSV file containing peakipy fits" +panel_help = "Open fits in browser with an interactive panel app" +individual_help = "Show individual peak fits as surfaces" +label_help = "Add peak assignment labels" +first_help = "Show only first plane" +plane_help = "Select planes to plot" +clusters_help = "Select clusters to plot" +colors_help = "Customize colors for data and fit lines respectively" +show_help = "Open interactive matplotlib window" +outname_help = "Name of output multipage pdf" @app.command(help="Interactive plots for checking fits") def check( - fits: Annotated[Path, typer.Argument(help=fits_help)], + fits_path: Annotated[Path, typer.Argument(help=fits_help)], data_path: Annotated[Path, typer.Argument(help=data_path_help)], - clusters: Optional[List[int]] = None, - plane: Optional[List[int]] = None, - outname: Path = Path("plots.pdf"), - first: bool = False, - show: bool = False, - label: bool = False, - individual: bool = False, - ccpn: bool = False, + panel: Annotated[bool, typer.Option(help=panel_help)] = False, + clusters: Annotated[Optional[List[int]], typer.Option(help=clusters_help)] = None, + plane: Annotated[Optional[List[int]], typer.Option(help=plane_help)] = None, + first: Annotated[bool, typer.Option(help=first_help)] = False, + show: Annotated[bool, typer.Option(help=show_help)] = False, + label: Annotated[bool, typer.Option(help=label_help)] = False, + individual: Annotated[bool, typer.Option(help=individual_help)] = False, + outname: Annotated[Path, typer.Option(help=outname_help)] = Path("plots.pdf"), + colors: Annotated[Tuple[str, str], typer.Option(help=colors_help)] = ( + "#5e3c99", + "#e66101", + ), rcount: int = 50, ccount: int = 50, - colors: Tuple[str, str] = ("#5e3c99", "#e66101"), + ccpn: bool = False, plotly: bool = False, test: bool = False, ): @@ -553,7 +626,7 @@ def check( verb : bool verbose mode """ - log_path = create_log_path(fits.parent) + log_path = create_log_path(fits_path.parent) columns_to_print = [ "assignment", "clustid", @@ -567,13 +640,19 @@ def check( "fwhm_y_hz", "lineshape", ] - fits = validate_fit_dataframe(pd.read_csv(fits)) + fits = validate_fit_dataframe(pd.read_csv(fits_path)) args = {} # get dims from config file config_path = data_path.parent / "peakipy.config" args, config = update_args_with_values_from_config_file(args, config_path) dims = config.get("dims", (1, 2, 3)) + if panel: + create_check_panel( + fits_path=fits_path, data_path=data_path, config_path=config_path, test=test + ) + return + ccpn_flag = ccpn if ccpn_flag: from ccpn.ui.gui.widgets.PlotterWidget import PlotterWidget @@ -674,13 +753,207 @@ def check( if first: break - with PdfPages(outname) as pdf: - for plot_data in all_plot_data: - create_matplotlib_figure( - plot_data, pdf, individual, label, ccpn_flag, show, test - ) + with PdfPages(data_path.parent / outname) as pdf: + for plot_data in all_plot_data: + create_matplotlib_figure( + plot_data, pdf, individual, label, ccpn_flag, show, test + ) + + run_log(log_path) + + +def create_plotly_pane(cluster, plane): + data = data_singleton_check() + fig, residual_fig = check( + fits_path=data.fits_path, + data_path=data.data_path, + clusters=[cluster], + plane=[plane], + # config_path=data.config_path, + plotly=True, + ) + fig["layout"].update(height=800, width=800) + fig = fig.to_dict() + residual_fig = residual_fig.to_dict() + return pn.Column(pn.pane.Plotly(fig), pn.pane.Plotly(residual_fig)) + + +def get_cluster(cluster): + data = data_singleton_check() + cluster_groups = data.df.groupby("clustid") + cluster_group = cluster_groups.get_group(cluster) + df_pane = pn.widgets.Tabulator( + cluster_group[ + [ + "assignment", + "clustid", + "memcnt", + "plane", + "amp", + "height", + "center_x_ppm", + "center_y_ppm", + "fwhm_x_hz", + "fwhm_y_hz", + "lineshape", + ] + ], + selectable=False, + disabled=True, + ) + return df_pane + + +def update_peakipy_data_on_edit_of_table(event): + data = data_singleton_edit() + column = event.column + row = event.row + value = event.value + data.bs.peakipy_data.df.loc[row, column] = value + data.bs.update_memcnt() + + +def panel_app(test=False): + data = data_singleton_edit() + bs = data.bs + bokeh_pane = pn.pane.Bokeh(bs.p) + spectrum_view_settings = pn.WidgetBox( + "# Contour settings", bs.pos_neg_contour_radiobutton, bs.contour_start + ) + save_peaklist_box = pn.WidgetBox( + "# Save your peaklist", + bs.savefilename, + bs.button, + pn.layout.Divider(), + bs.exit_button, + ) + recluster_settings = pn.WidgetBox( + "# Re-cluster your peaks", + bs.clust_div, + bs.struct_el, + bs.struct_el_size, + pn.layout.Divider(), + bs.recluster_warning, + bs.recluster, + sizing_mode="stretch_width", + ) + button = pn.widgets.Button(name="Fit selected cluster(s)", button_type="primary") + fit_controls = pn.WidgetBox( + "# Fit controls", + button, + pn.layout.Divider(), + bs.select_plane, + bs.checkbox_group, + pn.layout.Divider(), + bs.select_reference_planes_help, + bs.select_reference_planes, + pn.layout.Divider(), + bs.set_initial_fit_threshold_help, + bs.set_initial_fit_threshold, + pn.layout.Divider(), + bs.select_fixed_parameters_help, + bs.select_fixed_parameters, + pn.layout.Divider(), + bs.select_lineshape_radiobuttons_help, + bs.select_lineshape_radiobuttons, + ) + + mask_adjustment_controls = pn.WidgetBox( + "# Fitting mask adjustment", bs.slider_X_RADIUS, bs.slider_Y_RADIUS + ) + + # bs.source.on_change() + def fit_peaks_button_click(event): + check_app.loading = True + bs.fit_selected(None) + check_panel = create_check_panel(bs.TEMP_OUT_CSV, bs.data_path, edit_panel=True) + check_app.objects = check_panel.objects + check_app.loading = False + + button.on_click(fit_peaks_button_click) + + def update_source_selected_indices(event): + # print(bs.tablulator_widget.selection) + # hack to make current selection however, only allows one selection + # at a time + bs.tablulator_widget._update_selection([event.value]) + bs.source.selected.indices = bs.tablulator_widget.selection + # print(bs.tablulator_widget.selection) + + bs.tablulator_widget.on_click(update_source_selected_indices) + bs.tablulator_widget.on_edit(update_peakipy_data_on_edit_of_table) + + template = pn.template.BootstrapTemplate( + title="Peakipy", + sidebar=[mask_adjustment_controls, fit_controls], + ) + spectrum = pn.Card( + pn.Column( + pn.Row( + bokeh_pane, + pn.Column(spectrum_view_settings, save_peaklist_box), + recluster_settings, + ), + bs.tablulator_widget, + ), + title="Peakipy fit", + ) + check_app = pn.Card(title="Peakipy check") + template.main.append(pn.Column(check_app, spectrum)) + if test: + return + else: + template.show() + + +def create_check_panel( + fits_path: Path, + data_path: Path, + config_path: Path = Path("./peakipy.config"), + edit_panel: bool = False, + test: bool = False, +): + data = data_singleton_check() + data.fits_path = fits_path + data.data_path = data_path + data.config_path = config_path + data.load_dataframe() + + clusters = [(row.clustid, row.memcnt) for _, row in data.df.iterrows()] - run_log(log_path) + select_cluster = pn.widgets.Select( + name="Cluster (number of peaks)", options={f"{c} ({m})": c for c, m in clusters} + ) + select_plane = pn.widgets.Select( + name="Plane", options={f"{plane}": plane for plane in data.df.plane.unique()} + ) + result_table_pane = pn.bind(get_cluster, select_cluster) + interactive_plotly_pane = pn.bind( + create_plotly_pane, cluster=select_cluster, plane=select_plane + ) + info_pane = pn.pane.Markdown( + "Select a cluster and plane to look at from the dropdown menus" + ) + check_pane = pn.Card( + # info_pane, + # pn.Row(select_cluster, select_plane), + pn.Row( + pn.Column( + pn.Row( + pn.Card(interactive_plotly_pane, title="Fitted cluster"), + pn.Column(info_pane, select_cluster, select_plane), + ), + pn.Card(result_table_pane, title="Fitted parameters for cluster"), + ) + ), + title="Peakipy check", + ) + if edit_panel: + return check_pane + elif test: + return + else: + check_pane.show() if __name__ == "__main__": diff --git a/peakipy/io.py b/peakipy/io.py index 88ed5be8..8df438f6 100644 --- a/peakipy/io.py +++ b/peakipy/io.py @@ -733,8 +733,9 @@ def mask_method(self, overlap=1.0, l_struc=None): return ClustersResult(labeled_array, num_features, mask, peaks) - def to_fuda(self, fname="params.fuda"): - with open("peaks.fuda", "w") as peaks_fuda: + def to_fuda(self): + fname = self.peaklist_path.parent / "params.fuda" + with open(self.peaklist_path.parent / "peaks.fuda", "w") as peaks_fuda: for ass, f1_ppm, f2_ppm in zip(self.df.ASS, self.df.Y_PPM, self.df.X_PPM): peaks_fuda.write(f"{ass}\t{f1_ppm:.3f}\t{f2_ppm:.3f}\n") groups = self.df.groupby("CLUSTID") diff --git a/test/test_cli.py b/test/test_cli.py index d89454bd..4704bbb8 100644 --- a/test/test_cli.py +++ b/test/test_cli.py @@ -4,8 +4,6 @@ import pytest import peakipy.cli.main -import peakipy.cli.check_panel -import peakipy.cli.edit_panel from peakipy.cli.main import PeaklistFormat, Lineshape from peakipy.io import StrucEl @@ -179,7 +177,7 @@ def test_fit_main_with_pv_pv(protein_L): def test_check_main_with_default(protein_L): args = dict( - fits=protein_L / Path("fits_PV.csv"), + fits_path=protein_L / Path("fits_PV.csv"), data_path=protein_L / Path("test1.ft2"), clusters=[1], first=True, @@ -193,7 +191,7 @@ def test_check_main_with_default(protein_L): def test_check_main_with_gaussian(protein_L): args = dict( - fits=protein_L / Path("fits_G.csv"), + fits_path=protein_L / Path("fits_G.csv"), data_path=protein_L / Path("test1.ft2"), clusters=[1], first=True, @@ -207,7 +205,7 @@ def test_check_main_with_gaussian(protein_L): def test_check_main_with_lorentzian(protein_L): args = dict( - fits=protein_L / Path("fits_L.csv"), + fits_path=protein_L / Path("fits_L.csv"), data_path=protein_L / Path("test1.ft2"), clusters=[1], first=True, @@ -221,7 +219,7 @@ def test_check_main_with_lorentzian(protein_L): def test_check_main_with_voigt(protein_L): args = dict( - fits=protein_L / Path("fits_V.csv"), + fits_path=protein_L / Path("fits_V.csv"), data_path=protein_L / Path("test1.ft2"), clusters=[1], first=True, @@ -235,7 +233,7 @@ def test_check_main_with_voigt(protein_L): def test_check_main_with_pv_pv(protein_L): args = dict( - fits=protein_L / Path("fits_PV_PV.csv"), + fits_path=protein_L / Path("fits_PV_PV.csv"), data_path=protein_L / Path("test1.ft2"), clusters=[1], first=True, @@ -251,41 +249,30 @@ def test_check_panel_PVPV(protein_L): args = dict( fits_path=protein_L / Path("fits_PV_PV.csv"), data_path=protein_L / Path("test1.ft2"), - config_path=protein_L / Path("peakipy.config"), test=True, + panel=True, ) - peakipy.cli.check_panel.create_check_panel(**args) + peakipy.cli.main.check(**args) def test_check_panel_PV(protein_L): args = dict( fits_path=protein_L / Path("fits_PV.csv"), data_path=protein_L / Path("test1.ft2"), - config_path=protein_L / Path("peakipy.config"), test=True, + panel=True, ) - peakipy.cli.check_panel.create_check_panel(**args) + peakipy.cli.main.check(**args) def test_check_panel_V(protein_L): args = dict( fits_path=protein_L / Path("fits_V.csv"), data_path=protein_L / Path("test1.ft2"), - config_path=protein_L / Path("peakipy.config"), test=True, + panel=True, ) - peakipy.cli.check_panel.create_check_panel(**args) - - -def test_check_panel_edit(protein_L): - args = dict( - fits_path=protein_L / Path("fits_V.csv"), - data_path=protein_L / Path("test1.ft2"), - config_path=protein_L / Path("peakipy.config"), - edit_panel=True, - test=True, - ) - peakipy.cli.check_panel.create_check_panel(**args) + peakipy.cli.main.check(**args) def test_edit_panel(protein_L): @@ -294,4 +281,4 @@ def test_edit_panel(protein_L): data_path=protein_L / Path("test1.ft2"), test=True, ) - peakipy.cli.edit_panel.main(**args) + peakipy.cli.main.edit(**args) From 68a07030f3c82918a3ee2e226743d685df29ef41 Mon Sep 17 00:00:00 2001 From: Jacob Brady Date: Thu, 30 May 2024 09:12:57 -0400 Subject: [PATCH 37/37] adding coverage badge --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index b35fb261..243e0cb8 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,8 @@ # Peakipy - NMR peak integration/deconvolution using python ![Test](https://github.com/j-brady/peakipy/actions/workflows/ci.yml/badge.svg) +[![codecov](https://codecov.io/gh/j-brady/peakipy/graph/badge.svg?token=1F90QI5WMS)](https://codecov.io/gh/j-brady/peakipy) + [peakipy documentation](https://j-brady.github.io/peakipy) @@ -26,7 +28,7 @@ should have been added to your path. ## Inputs -1. Peak list (NMRPipe, Analysis v2.4, Sparky) +1. Peak list (NMRPipe, Analysis v2.4, Sparky) 2. NMRPipe frequency domain dataset (2D or Pseudo 3D) There are four main commands: