Python script to plot the histogram comparing the word-based agreement between LWRM1 and LWRM2 for the Reuters news corpus.

This repository contains data and software related to an experiment in which we determine the lifetime of the cesium 52D5/2 state using atoms in a vapor cell. More information is available in the following paper:

arXiv:1912.10089

We provide the data and Python scripts for data evaluation in six folders. We zipped these folders with Windows 10 Enterprise, Version 1903. In the following, we describe how to use data and scripts to get the lifetime results published in our paper.

Raw Time-Tags

Here, we provide the raw measurement data. We perform several experiment cycles. An excitation laser is switched on at the beginning of each cycle. In the middle of the cycle, it is switched off. We use two single-photon counting modules (SPCM): one detects fluorescence photons emitted by the atoms, the other reference light from the excitation laser beam. We record the arrival times of those photons with respect to the beginning of the cycle. These time delays can be used to create a histogram and to determine the lifetime of the cesium 52D5/2 state.

For each measurement, we provide two data files which are encoded in ‘UTF-8’:

‘figx_xxx_reference_time_tags.dat’

‘figx_xxx_fluorescence_time_tags.dat’

where ‘figx_xxx’ is a unique tag indicating the figure and point to which this data corresponds in our paper. The ‘figx_xxx_fluorescence_raw_data.dat’ and ‘figx_xxx_reference_raw_data.dat’ files contain the raw time delays in picoseconds of the fluorescence and the reference photons, respectively.

We provide raw time delays in the following folders:

‘fig3_time_tags’: The data used in figure 3.

‘fig4_time_tags’: The data used in figure 4. This folder has six subfolders, named ‘point_x’, where x indicates to which point of figure 4 the data belongs. The data of the subfolders ‘point_x_y’ was used for points x and y of figure 4 (the time-tags of the fluorescence photons were split into two sub-datasets with equal size).

‘fig5_time_tags’: The data underlying figure 5. This folder has subfolders from ‘23C’ to ‘116C’ where the name indicates the temperature in units of °C of the vapor cell during the measurement. Note that the various measurements have different cycle lengths because reabsorption makes the decay of the fluorescence signal longer. For the lifetime value at a temperature of 23 °C, we used the lifetime which we found in figure 4. For some measurements, the time-tags of the reference SPCM are missing because only one SPCM was available for these measurements.

Histograms

Since the files of the raw measurement data are large, we also provide histograms of the time tags. For all datasets discussed above, we generated a histogram with a bin length of 5 ns. We save these histograms with the same file name as the files with the raw time tags but with the ending ‘_histo’ instead of ‘_time_tags’, e.g., ‘fig3_fluorescence_histo.dat’ and ‘fig3_reference_histo.dat’.

We always provide two file formats:

a data file (.dat), containing rows with the start time of a bin in microseconds, and the number of SPCM counts due to the fluorescence signal until the start of the next bin, separated by a comma. These files are encoded in ‘UTF-8’.

a NumPy compressed array format file (.npz), which includes two arrays: The first array is called ‘time’ and contains the starting times of the bins. The second array is called ‘counts’ and includes the corresponding measured number of fluorescence photons per bin. It is possible to load the arrays into a Python script with numpy.load (tested with NumPy version 1.18.1).

Additional Information on the Measurements

We provide a JavaScript Object Notation file (.json) for each measurement. These files provide the following information about every measurement: temperature of the cell, number of detected photons, photons per cycle, and the total measurement duration. They are named ‘figx_xxx_info.json’, where ‘figx_xxx’ is the same indicator as discussed in section ‘Raw Time-Tags’.

Scripts

This folder contains two sample scripts to illustrate how our data can be processed with Python. The first Python script (generate_histograms.py) generates a histogram of the photon arrival times. The second Python script performs a fit in order to determine the lifetime of the cesium 52D5/2 state. We wrote these scripts with Python 3.6.5. To avoid errors, one should download all zipped folders and extract them to the same folder.

The script ‘generate_histograms.py’ processes the fluorescence photon detection events stored in the folder ‘fig3_time_tags’. The file ‘fig3 _fluorescence_time_tags.dat’ is read into the script, and a histogram is generated. To run the script, the following Python libraries are required: NumPy (version 1.18.1), os (version 0.1.4), and json (version 2.0.9).

The script ‘fit_data.py’ loads the file ‘fig3_fluorescence_histogram.npz’ from the folder ‘histograms\ fig3_time_tags’ in NumPy arrays. We perform a least-square fit on the histogram of the fluorescence decay. From the fit, we get the lifetime of the cesium 52D5/2 state. Optionally, it is possible to print a fit report and to plot the fit with its residuals. The following Python libraries are required to run the script: NumPy (version 1.18.1), os (version 0.1.4), json (version 2.0.9), pyplot from matplotlib (version 3.1.1), and Parameters, ExponentialModel, and ConstantModel from LmFit (version 1.0.0).

Figures

In the folder ‘figures’, we provide the values of the points which we used to generate figure 4 and figure 5. For both figures, we made a JavaScript Object Notation file (.json) where the data of every point is stored in a dictionary. This data contains the fit result of the lifetime and the temperature of the measurement. Additionally, it contains the corresponding errors and the units of every value.

Python script to plot the histogram comparing the bigram-based agreement between TFIDF and RAKE for the ACM-TWEB corpus.

ObjectivesThis study aimed to construct prediction models based on computerized tomography (CT) signs, histogram and morphology features for the diagnosis of micropapillary or solid (MIP/SOL) components of stage IA lung adenocarcinoma (LUAC) and to evaluate the models’ performance.MethodsThis clinical retrospective study included image data of 376 patients with stage IA LUAC based on postoperative pathology, admitted to Putian First Hospital from January 2019 to June 2023. According to the presence of MIP/SOL components in postoperative pathology, patients were divided into MIP/SOL+ and MIP/SOL- groups. Cases with tumors ≤ 3 cm and ≤ 2 cm were separately analyzed. Each subgroup of patients was then randomly divided into a training set and a test set in a ratio of 7:3. The training set was used to build the prediction model, and the test set was used for internal validation.ResultsFor tumors ≤ 3 cm, ground-glass opacity (GGO) [odds ratio (OR) = 0.244; 95% confidence interval (CI): 0.103–0.569; p = 0.001], entropy (OR = 1.748; 95% CI: 1.213–2.577; p = 0.004), average CT value (OR = 1.002; 95% CI: 1.000–1.004; p = 0.002), and kurtosis (OR = 1.240; 95% CI: 1.023–1.513; p = 0.030) were independent predictors of MIP/SOL components of stage IA LUAC. The area under the ROC curve (AUC) of the nomogram prediction model for predicting MIP/SOL components was 0.816 (95% CI: 0.756–0.877) in the training set and 0.789 (95% CI: 0.689–0.889) in the test set. In contrast, for tumors ≤ 2 cm, kurtosis was no longer an independent predictor. The nomogram prediction model had an AUC of 0.811 (95% CI: 0.731–0.891) in the training set and 0.833 (95% CI: 0.733–0.932) in the test set.ConclusionFor tumors ≤ 3 cm and ≤ 2 cm, GGO, average CT value, and entropy were the same independent influencing factors in predicting MIP/SOL components of stage IA LUAC. The nomogram prediction models have potential diagnostic value for identifying MIP/SOL components of early-stage LUAC.

This histogram shows the frequency with which French consumers did their grocery shopping in 2018. According to this survey conducted by ProdegeMR, the vast majority of respondents (approximately 80 percent) did their food shopping at least once a week.

Osma vežba za predmet Tehnike obrade biomedicinskih signala na master akademskim studijama na Elektrotehničkom fakultetu Univerziteta u Beogradu.

This data is the stratigraphic histogram of Quanshui Lake in Kunlun mountain area, including the characteristic elements of stratigraphic thickness and lithologic changes, which is based on detailed field survey and indoor analysis. The specific processing method is as follows: through field investigation, obtain the material of formation lithology composition, formation thickness, structural characteristics, etc., and draw the draft of stratigraphic histogram by hand. Back in the room, the lithology of rock is confirmed by thin section identification, and then the histogram is electronized by CorelDRAW software. This map is about 4MB in size with high resolution. It can be used for stratigraphic investigation, lithological analysis, the highest marine strata in Kunlun Mountain, paleontology and paleogeography.

Python script to plot the histogram comparing the bigram-based agreement between LWRM2 and RAKE for the ACM-TWEB corpus.

Comparison of the proposed method and other methods.

Python script to plot the histogram comparing the bigram-based agreement between LWRM2 and RAKE for the Reuters news corpus.

This data is a histogram of red coral stratum in Kunlun mountain area, including the characteristic elements of stratum thickness and lithology change, which is based on detailed field survey and indoor analysis. The specific processing method is as follows: through field investigation, obtain the material of formation lithology composition, formation thickness, structural characteristics, etc., and draw the draft of stratigraphic histogram by hand. Back in the room, the lithology of rock is confirmed by thin section identification, and then the histogram is electronized by CorelDRAW software. This map is about 4MB in size with high resolution. It can be used for stratigraphic investigation, lithological analysis, the highest marine strata in Kunlun Mountain, paleontology and paleogeography.

description: Coastal storms and other meteorological phenomenon can have a significant impact on how high water levels rise and how often. The inundation analysis program is extremely beneficial in determining the frequency (or the occurrence of high waters for different elevations above a specified threshold) and duration (or the amount of time that the specified location is inundated by water) of observed high waters (tides). Statistical output from these analyses can be useful in planning marsh restoration activities. Additionally, the analyses have broader applications for the coastal engineering and mapping community, such as, ecosystem management and regional climate change. Since these statistical outputs are station specific, use for evaluating surrounding areas may be limited. Products The data input for this tool is 6-minute water level data time series and the tabulated times and heights of the high tides over a user specified time period, relative to a desired tidal datum or user-specified datum. The data output of this tool provides summary statistics, which includes the number of occurrences of inundation above the threshold (events) and length of duration of inundation of each events above the threshold elevation for a specified time period. In addition to summary statistics, graphical outputs are provided using three plots. The first plot is a histogram of frequency of occurrence relative to the threshold elevation, the second plot is a histogram of the frequency of duration of inundation, and the third plot is an X-Y plot of frequency of elevation versus duration of inundation for each event. Input data time series are presently limited to the verified data from a set of operating and historical tide stations in the NOAA CO-OPS data base.; abstract: Coastal storms and other meteorological phenomenon can have a significant impact on how high water levels rise and how often. The inundation analysis program is extremely beneficial in determining the frequency (or the occurrence of high waters for different elevations above a specified threshold) and duration (or the amount of time that the specified location is inundated by water) of observed high waters (tides). Statistical output from these analyses can be useful in planning marsh restoration activities. Additionally, the analyses have broader applications for the coastal engineering and mapping community, such as, ecosystem management and regional climate change. Since these statistical outputs are station specific, use for evaluating surrounding areas may be limited. Products The data input for this tool is 6-minute water level data time series and the tabulated times and heights of the high tides over a user specified time period, relative to a desired tidal datum or user-specified datum. The data output of this tool provides summary statistics, which includes the number of occurrences of inundation above the threshold (events) and length of duration of inundation of each events above the threshold elevation for a specified time period. In addition to summary statistics, graphical outputs are provided using three plots. The first plot is a histogram of frequency of occurrence relative to the threshold elevation, the second plot is a histogram of the frequency of duration of inundation, and the third plot is an X-Y plot of frequency of elevation versus duration of inundation for each event. Input data time series are presently limited to the verified data from a set of operating and historical tide stations in the NOAA CO-OPS data base.

In 2024, 34.59 percent of all households in the United States were two person households. In 1970, this figure was at 28.92 percent. Single households Single mother households are usually the most common households with children under 18 years old found in the United States. As of 2021, the District of Columbia and North Dakota had the highest share of single-person households in the United States. Household size in the United States has decreased over the past century, due to customs and traditions changing. Families are typically more nuclear, whereas in the past, multigenerational households were more common. Furthermore, fertility rates have also decreased, meaning that women do not have as many children as they used to. Average households in Utah Out of all states in the U.S., Utah was reported to have the largest average household size. This predominately Mormon state has about three million inhabitants. The Church of the Latter-Day Saints, or Mormonism, plays a large role in Utah, and can contribute to the high birth rate and household size in Utah. The Church of Latter-Day Saints promotes having many children and tight-knit families. Furthermore, Utah has a relatively young population, due to Mormons typically marrying and starting large families younger than those in other states.

This is the dataset and codes used to make main figures of Tanioka and Matsumo (2019), GRL (in review).

1. SupportInfo_T6_pdf = Macromolecule Data used to make Figure S8 in the supporting Information.

2. Data_Finkel16_Pone.mat = Phytoplankton macromolecule data used to make Figure 3a in the main text

3. montecarlo_result.mat = Result of Monte Carlo Simulation used to make histogram in Figure 3b in the main text

4. analyze_Finkel.m = MATLAB code used to make Figure 3a and 3b in the main text

Python script to plot the histogram comparing the agreement between LWRM2 and TFIDF for the ACM-TWEB corpus.

Histogram plot of the average alignment accuracy averaged over 10 runs for each viral genome shown in Table 1 and each aligner. Reads crossing splice junction regions are shown in pink, reads not crossing splice junction regions are shown in blue).

Python script to plot the histogram comparing the bigram-based agreement between LWRM1 and RAKE for the ACM-TWEB corpus.