automatic import of python-luciferase

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+/luciferase-2.4.5.tar.gz
diff --git a/python-luciferase.spec b/python-luciferase.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-luciferase
+Version:	2.4.5
+Release:	1
+Summary:	Helper functions for luciferase data
+License:	MIT License
+URL:		https://gitlab.com/aaylward/luciferase
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/54/6d/d2564e403a1eedfa546319bc74c558fde7860b39f016617cb1fc83fb6516/luciferase-2.4.5.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-estimateratio
+Requires:	python3-matplotlib
+Requires:	python3-pandas
+Requires:	python3-scipy
+Requires:	python3-seaborn
+Requires:	python3-xlrd
+
+%description
+# luciferase
+Tool for plotting luciferase reporter data. Thanks due to Joshua Chiou and Mei-lin Okino for inspiration and contributions.
+
+## Installation
+
+To use `luciferase`, you must first have [python](https://www.python.org/downloads/) installed. Then, you can install `luciferase` using `pip`:
+
+```sh
+pip install luciferase
+```
+or
+```sh
+pip install --user luciferase
+```
+
+## Command-line interface for barplots
+
+## Introduction
+
+This introduction demonstrates a simple analysis of data in Excel format.
+
+Here is our [example dataset](https://github.com/anthony-aylward/luciferase/raw/master/example/example-excel.xlsx), `example-excel.xlsx`:
+
+![screenshot of excel data](https://github.com/anthony-aylward/luciferase/raw/master/example/example-excel-screenshot.png)
+
+The first row contains headers and subsequent rows contain firefly/renilla ratios normalized to the empty vector.
+
+We can analyze these data by running:
+
+```luciferase-swarmplot example-excel.xlsx example-excel-plot.pdf```
+
+Here is the result:
+
+<img src="https://github.com/anthony-aylward/luciferase/raw/master/example/example-excel-plot.png" width="400"/>
+
+We can also use input data with more columns to produce plots with more bars. For example:
+
+![screenshot of expanded excel data](https://github.com/anthony-aylward/luciferase/raw/master/example/example-excel-expanded-screenshot.png)
+
+```luciferase-swarmplot example-excel-expanded.xlsx example-excel-expanded-plot.pdf```
+
+<img src="https://github.com/anthony-aylward/luciferase/raw/master/example/example-excel-expanded-plot.png" width="400"/>
+
+See the following sections for more details.
+
+### Barplots of enhancer activity
+
+A command-line tool called `luciferase-barplot` for creating bar plots is
+included. After installing `luciferase`, you can use it like this:
+```sh
+luciferase-barplot example.json example.pdf
+luciferase-barplot example.csv example.png
+luciferase-barplot example.tsv example.svg
+luciferase-barplot example.xls example.pdf
+luciferase-barplot example.xlsx example.png
+```
+
+JSON, CSV, TSV, or Excel files may be used as inputs, and output
+can be written in PDF, PNG, or SVG.
+
+See also the help message:
+```sh
+luciferase-barplot -h
+```
+
+Examples of luciferase reporter data in JSON format:
+```json
+{
+  "Non-risk, Fwd": [8.354, 12.725, 8.506],
+  "Risk, Fwd": [5.078, 5.038, 5.661],
+  "Non-risk, Rev": [9.564, 9.692, 12.622],
+  "Risk, Rev": [10.777, 11.389, 10.598],
+  "Empty": [1.042, 0.92, 1.042]
+}
+```
+```json
+{
+  "Alt, MIN6": [5.47, 7.17, 6.15],
+  "Ref, MIN6": [3.16, 3.04, 4.34],
+  "Empty, MIN6": [1.07, 0.83, 0.76],
+  "Alt, ALPHA-TC6": [2.50, 3.47, 3.33],
+  "Ref, ALPHA-TC6": [2.01, 1.96, 2.31],
+  "Empty, ALPHA-TC6": [1.042, 0.92, 1.042]
+}
+```
+Examples of luciferase reporter data in TSV format:
+```
+Non-risk, Fwd	Risk, Fwd	Non-risk, Rev	Risk, Rev	Empty
+8.354	5.078	9.564	10.777	1.042
+12.725	5.038	9.692	11.389	0.92
+8.506	5.661	12.622	10.598	1.042
+```
+```
+Ref, untreated	Alt, untreated	Empty, untreated	Ref, dex	Alt, dex	Empty, dex
+33.2	19.7	1.0	149.4	44.6	1.1
+30.3	16.2	1.0	99.7	37.6	1.0
+33.3	18.3	1.0	124.5	37.7	0.9
+```
+
+Significance indicators will be written above the bars: `***` if p<0.001,
+`**` if p<0.01, `*` if p<0.05, `ns` otherwise.
+
+Here are the resulting plots:
+
+<img src="https://github.com/anthony-aylward/luciferase/raw/master/example/example-5.png" width="400"/><img src="https://github.com/anthony-aylward/luciferase/raw/master/example/example-6.png" width="400"/>
+
+### Barplots of allelic ratio
+
+A second tool called `luciferase-ratioplot` takes the same input data and
+produces a comparative plot of allelic ratios:
+
+```sh
+luciferase-ratioplot --xlab control dexamethasone --ylab "Ref:Alt ratio" --title Default ratio.json ratio.png
+luciferase-ratioplot --xlab control dexamethasone --ylab "Alt:Ref ratio" --title Inverted --invert ratio.json ratio.png
+```
+
+The resulting plot shows the estimated allelic ratio of enhancer activity
+with confidence intervals (95% by default). Here is an example input dataset
+and plot:
+
+JSON:
+```json
+{
+  "Ref, untreated": [33.2, 30.3, 33.3],
+  "Alt, untreated": [19.7, 16.2, 18.3],
+  "Empty, untreated": [1.0, 1.0, 1.0],
+  "Ref, dex": [149.4, 99.7, 124.5],
+  "Alt, dex": [44.6, 37.6, 37.7],
+  "Empty, dex": [1.1, 1.0, 0.9]
+}
+```
+TSV:
+```
+Alt, dex	Ref, dex	Empty, dex	Alt, untreated	Ref, untreated	Empty, untreated
+44.6	149.4	1.1	19.7	33.2	1.0
+37.6	99.7	1.0	16.2	30.3	1.0
+37.7	124.5	0.9	18.3	33.3	1.0
+```
+
+<img src="https://github.com/anthony-aylward/luciferase/raw/master/example/ratio.png" width="200"/><img src="https://github.com/anthony-aylward/luciferase/raw/master/example/ratio-invert.png" width="200"/>
+
+## Meta-analysis
+
+For this section, we'll use another included command called
+`luciferase-swarmplot`. It functions exactly like `luciferase-barplot` except
+that individual data points will be plotted over the bars.
+
+It may be that we have performed two or more experiments
+(from separate minipreps) and wish to meta-analyze the results. As an example,
+let's consider the results of two identical experiments on a regulatory
+variant at the _SIX3_ locus: [SIX3-MP0](https://github.com/anthony-aylward/luciferase/raw/master/example/six3-mp0.json) and [SIX3-MP1](https://github.com/anthony-aylward/luciferase/raw/master/example/six3-mp1.json). First we'll plot both datasets separately:
+```sh
+luciferase-swarmplot six3-mp0.json six3-mp0.png --light-colors '#DECBE4' '#FED9A6' '#FBB4AE' --dark-colors '#984EA3' '#FF7F00' '#E41A1C' --title 'SIX3-MP0'
+luciferase-swarmplot six3-mp1.json six3-mp1.png --light-colors '#DECBE4' '#FED9A6' '#FBB4AE' --dark-colors '#984EA3' '#FF7F00' '#E41A1C' --title 'SIX3-MP1'
+```
+
+<img src="https://github.com/anthony-aylward/luciferase/raw/master/example/six3-mp0.png" width="400"/><img src="https://github.com/anthony-aylward/luciferase/raw/master/example/six3-mp1.png" width="400"/>
+
+We can see that the results are fairly consistent in character, but checking
+the y-axis tells us that they are on different scales. Intuitively, we might
+conclude from these results that there are allelic effects under all three
+conditions. Ideally though, we would like to use all of the data at once for
+one plot to get the most accurate conclusions about allelic effects.
+
+We might simply combine the data into one dataset, (as [here](https://github.com/anthony-aylward/luciferase/raw/master/example/six3-meta-nobatch.json)) and plot it:
+```sh
+luciferase-swarmplot six3-meta-nobatch.json six3-meta-nobatch.png --light-colors '#DECBE4' '#FED9A6' '#FBB4AE' --dark-colors '#984EA3' '#FF7F00' '#E41A1C'
+```
+
+![meta-analysis without batch](https://github.com/anthony-aylward/luciferase/raw/master/example/six3-meta-nobatch.png)
+
+The bar heights look reasonable, and the allelic effects appear clear from
+looking at them, but all of the hypothesis tests returned non-significant
+results. What gives?
+
+The answer is that combining data from experiments with different scales
+breaks the assumptions of the significance test (a t-test). To meta-analyze
+these data in a useful way, we first need to re-normalize the two experiments
+to put both of them on the same scale. `luciferase-barplot` and
+`luciferase-swarmplot` will re-normalize the data automatically if the dataset
+includes an additional entry ("Batch") indicating the batch of each data point,
+as in this example:
+[SIX3-META](https://github.com/anthony-aylward/luciferase/raw/master/example/six3-meta.json).
+```json
+{
+  "Alt, untreated": [19.7, 16.2, 18.3, 6.5, 8.0, 4.4],
+  "Ref, untreated": [33.2, 30.3, 33.3, 8.4, 13.6, 17.1],
+  "Empty, untreated": [1.0, 1.0, 1.0, 1.1, 1.0, 0.9],
+  "Alt, hi_cyt_noTNFA": [11.0, 8.8, 10.1, 3.2, 3.7, 3.3],
+  "Ref, hi_cyt_noTNFA": [17.1, 16.7, 18.8, 7.6, 6.7, 5.5],
+  "Empty, hi_cyt_noTNFA": [1.1, 0.9, 1.0, 1.1, 0.9, 1.0],
+  "Alt, hi_cyt": [10.8, 10.9, 9.1, 3.1, 2.7, 4.0],
+  "Ref, hi_cyt": [17.8, 16.1, 18.0, 7.7, 7.0, 7.1],
+  "Empty, hi_cyt": [1.0, 1.0, 1.0, 1.0, 1.0, 1.1],
+  "Batch": [0, 0, 0, 1, 1, 1]
+}
+```
+
+Here is what the results look like when they're re-normalized to correct for
+batch
+```sh
+luciferase-swarmplot six3-meta.json six3-meta.png --light-colors '#DECBE4' '#FED9A6' '#FBB4AE' --dark-colors '#984EA3' '#FF7F00' '#E41A1C' --title 'SIX3-META'
+```
+
+![meta-analysis with batch](https://github.com/anthony-aylward/luciferase/raw/master/example/six3-meta.png)
+
+See a more detailed explanation of the normalization procedure [here](https://github.com/anthony-aylward/luciferase/blob/master/example/meta-analysis.pdf)
+
+
+
+
+%package -n python3-luciferase
+Summary:	Helper functions for luciferase data
+Provides:	python-luciferase
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-luciferase
+# luciferase
+Tool for plotting luciferase reporter data. Thanks due to Joshua Chiou and Mei-lin Okino for inspiration and contributions.
+
+## Installation
+
+To use `luciferase`, you must first have [python](https://www.python.org/downloads/) installed. Then, you can install `luciferase` using `pip`:
+
+```sh
+pip install luciferase
+```
+or
+```sh
+pip install --user luciferase
+```
+
+## Command-line interface for barplots
+
+## Introduction
+
+This introduction demonstrates a simple analysis of data in Excel format.
+
+Here is our [example dataset](https://github.com/anthony-aylward/luciferase/raw/master/example/example-excel.xlsx), `example-excel.xlsx`:
+
+![screenshot of excel data](https://github.com/anthony-aylward/luciferase/raw/master/example/example-excel-screenshot.png)
+
+The first row contains headers and subsequent rows contain firefly/renilla ratios normalized to the empty vector.
+
+We can analyze these data by running:
+
+```luciferase-swarmplot example-excel.xlsx example-excel-plot.pdf```
+
+Here is the result:
+
+<img src="https://github.com/anthony-aylward/luciferase/raw/master/example/example-excel-plot.png" width="400"/>
+
+We can also use input data with more columns to produce plots with more bars. For example:
+
+![screenshot of expanded excel data](https://github.com/anthony-aylward/luciferase/raw/master/example/example-excel-expanded-screenshot.png)
+
+```luciferase-swarmplot example-excel-expanded.xlsx example-excel-expanded-plot.pdf```
+
+<img src="https://github.com/anthony-aylward/luciferase/raw/master/example/example-excel-expanded-plot.png" width="400"/>
+
+See the following sections for more details.
+
+### Barplots of enhancer activity
+
+A command-line tool called `luciferase-barplot` for creating bar plots is
+included. After installing `luciferase`, you can use it like this:
+```sh
+luciferase-barplot example.json example.pdf
+luciferase-barplot example.csv example.png
+luciferase-barplot example.tsv example.svg
+luciferase-barplot example.xls example.pdf
+luciferase-barplot example.xlsx example.png
+```
+
+JSON, CSV, TSV, or Excel files may be used as inputs, and output
+can be written in PDF, PNG, or SVG.
+
+See also the help message:
+```sh
+luciferase-barplot -h
+```
+
+Examples of luciferase reporter data in JSON format:
+```json
+{
+  "Non-risk, Fwd": [8.354, 12.725, 8.506],
+  "Risk, Fwd": [5.078, 5.038, 5.661],
+  "Non-risk, Rev": [9.564, 9.692, 12.622],
+  "Risk, Rev": [10.777, 11.389, 10.598],
+  "Empty": [1.042, 0.92, 1.042]
+}
+```
+```json
+{
+  "Alt, MIN6": [5.47, 7.17, 6.15],
+  "Ref, MIN6": [3.16, 3.04, 4.34],
+  "Empty, MIN6": [1.07, 0.83, 0.76],
+  "Alt, ALPHA-TC6": [2.50, 3.47, 3.33],
+  "Ref, ALPHA-TC6": [2.01, 1.96, 2.31],
+  "Empty, ALPHA-TC6": [1.042, 0.92, 1.042]
+}
+```
+Examples of luciferase reporter data in TSV format:
+```
+Non-risk, Fwd	Risk, Fwd	Non-risk, Rev	Risk, Rev	Empty
+8.354	5.078	9.564	10.777	1.042
+12.725	5.038	9.692	11.389	0.92
+8.506	5.661	12.622	10.598	1.042
+```
+```
+Ref, untreated	Alt, untreated	Empty, untreated	Ref, dex	Alt, dex	Empty, dex
+33.2	19.7	1.0	149.4	44.6	1.1
+30.3	16.2	1.0	99.7	37.6	1.0
+33.3	18.3	1.0	124.5	37.7	0.9
+```
+
+Significance indicators will be written above the bars: `***` if p<0.001,
+`**` if p<0.01, `*` if p<0.05, `ns` otherwise.
+
+Here are the resulting plots:
+
+<img src="https://github.com/anthony-aylward/luciferase/raw/master/example/example-5.png" width="400"/><img src="https://github.com/anthony-aylward/luciferase/raw/master/example/example-6.png" width="400"/>
+
+### Barplots of allelic ratio
+
+A second tool called `luciferase-ratioplot` takes the same input data and
+produces a comparative plot of allelic ratios:
+
+```sh
+luciferase-ratioplot --xlab control dexamethasone --ylab "Ref:Alt ratio" --title Default ratio.json ratio.png
+luciferase-ratioplot --xlab control dexamethasone --ylab "Alt:Ref ratio" --title Inverted --invert ratio.json ratio.png
+```
+
+The resulting plot shows the estimated allelic ratio of enhancer activity
+with confidence intervals (95% by default). Here is an example input dataset
+and plot:
+
+JSON:
+```json
+{
+  "Ref, untreated": [33.2, 30.3, 33.3],
+  "Alt, untreated": [19.7, 16.2, 18.3],
+  "Empty, untreated": [1.0, 1.0, 1.0],
+  "Ref, dex": [149.4, 99.7, 124.5],
+  "Alt, dex": [44.6, 37.6, 37.7],
+  "Empty, dex": [1.1, 1.0, 0.9]
+}
+```
+TSV:
+```
+Alt, dex	Ref, dex	Empty, dex	Alt, untreated	Ref, untreated	Empty, untreated
+44.6	149.4	1.1	19.7	33.2	1.0
+37.6	99.7	1.0	16.2	30.3	1.0
+37.7	124.5	0.9	18.3	33.3	1.0
+```
+
+<img src="https://github.com/anthony-aylward/luciferase/raw/master/example/ratio.png" width="200"/><img src="https://github.com/anthony-aylward/luciferase/raw/master/example/ratio-invert.png" width="200"/>
+
+## Meta-analysis
+
+For this section, we'll use another included command called
+`luciferase-swarmplot`. It functions exactly like `luciferase-barplot` except
+that individual data points will be plotted over the bars.
+
+It may be that we have performed two or more experiments
+(from separate minipreps) and wish to meta-analyze the results. As an example,
+let's consider the results of two identical experiments on a regulatory
+variant at the _SIX3_ locus: [SIX3-MP0](https://github.com/anthony-aylward/luciferase/raw/master/example/six3-mp0.json) and [SIX3-MP1](https://github.com/anthony-aylward/luciferase/raw/master/example/six3-mp1.json). First we'll plot both datasets separately:
+```sh
+luciferase-swarmplot six3-mp0.json six3-mp0.png --light-colors '#DECBE4' '#FED9A6' '#FBB4AE' --dark-colors '#984EA3' '#FF7F00' '#E41A1C' --title 'SIX3-MP0'
+luciferase-swarmplot six3-mp1.json six3-mp1.png --light-colors '#DECBE4' '#FED9A6' '#FBB4AE' --dark-colors '#984EA3' '#FF7F00' '#E41A1C' --title 'SIX3-MP1'
+```
+
+<img src="https://github.com/anthony-aylward/luciferase/raw/master/example/six3-mp0.png" width="400"/><img src="https://github.com/anthony-aylward/luciferase/raw/master/example/six3-mp1.png" width="400"/>
+
+We can see that the results are fairly consistent in character, but checking
+the y-axis tells us that they are on different scales. Intuitively, we might
+conclude from these results that there are allelic effects under all three
+conditions. Ideally though, we would like to use all of the data at once for
+one plot to get the most accurate conclusions about allelic effects.
+
+We might simply combine the data into one dataset, (as [here](https://github.com/anthony-aylward/luciferase/raw/master/example/six3-meta-nobatch.json)) and plot it:
+```sh
+luciferase-swarmplot six3-meta-nobatch.json six3-meta-nobatch.png --light-colors '#DECBE4' '#FED9A6' '#FBB4AE' --dark-colors '#984EA3' '#FF7F00' '#E41A1C'
+```
+
+![meta-analysis without batch](https://github.com/anthony-aylward/luciferase/raw/master/example/six3-meta-nobatch.png)
+
+The bar heights look reasonable, and the allelic effects appear clear from
+looking at them, but all of the hypothesis tests returned non-significant
+results. What gives?
+
+The answer is that combining data from experiments with different scales
+breaks the assumptions of the significance test (a t-test). To meta-analyze
+these data in a useful way, we first need to re-normalize the two experiments
+to put both of them on the same scale. `luciferase-barplot` and
+`luciferase-swarmplot` will re-normalize the data automatically if the dataset
+includes an additional entry ("Batch") indicating the batch of each data point,
+as in this example:
+[SIX3-META](https://github.com/anthony-aylward/luciferase/raw/master/example/six3-meta.json).
+```json
+{
+  "Alt, untreated": [19.7, 16.2, 18.3, 6.5, 8.0, 4.4],
+  "Ref, untreated": [33.2, 30.3, 33.3, 8.4, 13.6, 17.1],
+  "Empty, untreated": [1.0, 1.0, 1.0, 1.1, 1.0, 0.9],
+  "Alt, hi_cyt_noTNFA": [11.0, 8.8, 10.1, 3.2, 3.7, 3.3],
+  "Ref, hi_cyt_noTNFA": [17.1, 16.7, 18.8, 7.6, 6.7, 5.5],
+  "Empty, hi_cyt_noTNFA": [1.1, 0.9, 1.0, 1.1, 0.9, 1.0],
+  "Alt, hi_cyt": [10.8, 10.9, 9.1, 3.1, 2.7, 4.0],
+  "Ref, hi_cyt": [17.8, 16.1, 18.0, 7.7, 7.0, 7.1],
+  "Empty, hi_cyt": [1.0, 1.0, 1.0, 1.0, 1.0, 1.1],
+  "Batch": [0, 0, 0, 1, 1, 1]
+}
+```
+
+Here is what the results look like when they're re-normalized to correct for
+batch
+```sh
+luciferase-swarmplot six3-meta.json six3-meta.png --light-colors '#DECBE4' '#FED9A6' '#FBB4AE' --dark-colors '#984EA3' '#FF7F00' '#E41A1C' --title 'SIX3-META'
+```
+
+![meta-analysis with batch](https://github.com/anthony-aylward/luciferase/raw/master/example/six3-meta.png)
+
+See a more detailed explanation of the normalization procedure [here](https://github.com/anthony-aylward/luciferase/blob/master/example/meta-analysis.pdf)
+
+
+
+
+%package help
+Summary:	Development documents and examples for luciferase
+Provides:	python3-luciferase-doc
+%description help
+# luciferase
+Tool for plotting luciferase reporter data. Thanks due to Joshua Chiou and Mei-lin Okino for inspiration and contributions.
+
+## Installation
+
+To use `luciferase`, you must first have [python](https://www.python.org/downloads/) installed. Then, you can install `luciferase` using `pip`:
+
+```sh
+pip install luciferase
+```
+or
+```sh
+pip install --user luciferase
+```
+
+## Command-line interface for barplots
+
+## Introduction
+
+This introduction demonstrates a simple analysis of data in Excel format.
+
+Here is our [example dataset](https://github.com/anthony-aylward/luciferase/raw/master/example/example-excel.xlsx), `example-excel.xlsx`:
+
+![screenshot of excel data](https://github.com/anthony-aylward/luciferase/raw/master/example/example-excel-screenshot.png)
+
+The first row contains headers and subsequent rows contain firefly/renilla ratios normalized to the empty vector.
+
+We can analyze these data by running:
+
+```luciferase-swarmplot example-excel.xlsx example-excel-plot.pdf```
+
+Here is the result:
+
+<img src="https://github.com/anthony-aylward/luciferase/raw/master/example/example-excel-plot.png" width="400"/>
+
+We can also use input data with more columns to produce plots with more bars. For example:
+
+![screenshot of expanded excel data](https://github.com/anthony-aylward/luciferase/raw/master/example/example-excel-expanded-screenshot.png)
+
+```luciferase-swarmplot example-excel-expanded.xlsx example-excel-expanded-plot.pdf```
+
+<img src="https://github.com/anthony-aylward/luciferase/raw/master/example/example-excel-expanded-plot.png" width="400"/>
+
+See the following sections for more details.
+
+### Barplots of enhancer activity
+
+A command-line tool called `luciferase-barplot` for creating bar plots is
+included. After installing `luciferase`, you can use it like this:
+```sh
+luciferase-barplot example.json example.pdf
+luciferase-barplot example.csv example.png
+luciferase-barplot example.tsv example.svg
+luciferase-barplot example.xls example.pdf
+luciferase-barplot example.xlsx example.png
+```
+
+JSON, CSV, TSV, or Excel files may be used as inputs, and output
+can be written in PDF, PNG, or SVG.
+
+See also the help message:
+```sh
+luciferase-barplot -h
+```
+
+Examples of luciferase reporter data in JSON format:
+```json
+{
+  "Non-risk, Fwd": [8.354, 12.725, 8.506],
+  "Risk, Fwd": [5.078, 5.038, 5.661],
+  "Non-risk, Rev": [9.564, 9.692, 12.622],
+  "Risk, Rev": [10.777, 11.389, 10.598],
+  "Empty": [1.042, 0.92, 1.042]
+}
+```
+```json
+{
+  "Alt, MIN6": [5.47, 7.17, 6.15],
+  "Ref, MIN6": [3.16, 3.04, 4.34],
+  "Empty, MIN6": [1.07, 0.83, 0.76],
+  "Alt, ALPHA-TC6": [2.50, 3.47, 3.33],
+  "Ref, ALPHA-TC6": [2.01, 1.96, 2.31],
+  "Empty, ALPHA-TC6": [1.042, 0.92, 1.042]
+}
+```
+Examples of luciferase reporter data in TSV format:
+```
+Non-risk, Fwd	Risk, Fwd	Non-risk, Rev	Risk, Rev	Empty
+8.354	5.078	9.564	10.777	1.042
+12.725	5.038	9.692	11.389	0.92
+8.506	5.661	12.622	10.598	1.042
+```
+```
+Ref, untreated	Alt, untreated	Empty, untreated	Ref, dex	Alt, dex	Empty, dex
+33.2	19.7	1.0	149.4	44.6	1.1
+30.3	16.2	1.0	99.7	37.6	1.0
+33.3	18.3	1.0	124.5	37.7	0.9
+```
+
+Significance indicators will be written above the bars: `***` if p<0.001,
+`**` if p<0.01, `*` if p<0.05, `ns` otherwise.
+
+Here are the resulting plots:
+
+<img src="https://github.com/anthony-aylward/luciferase/raw/master/example/example-5.png" width="400"/><img src="https://github.com/anthony-aylward/luciferase/raw/master/example/example-6.png" width="400"/>
+
+### Barplots of allelic ratio
+
+A second tool called `luciferase-ratioplot` takes the same input data and
+produces a comparative plot of allelic ratios:
+
+```sh
+luciferase-ratioplot --xlab control dexamethasone --ylab "Ref:Alt ratio" --title Default ratio.json ratio.png
+luciferase-ratioplot --xlab control dexamethasone --ylab "Alt:Ref ratio" --title Inverted --invert ratio.json ratio.png
+```
+
+The resulting plot shows the estimated allelic ratio of enhancer activity
+with confidence intervals (95% by default). Here is an example input dataset
+and plot:
+
+JSON:
+```json
+{
+  "Ref, untreated": [33.2, 30.3, 33.3],
+  "Alt, untreated": [19.7, 16.2, 18.3],
+  "Empty, untreated": [1.0, 1.0, 1.0],
+  "Ref, dex": [149.4, 99.7, 124.5],
+  "Alt, dex": [44.6, 37.6, 37.7],
+  "Empty, dex": [1.1, 1.0, 0.9]
+}
+```
+TSV:
+```
+Alt, dex	Ref, dex	Empty, dex	Alt, untreated	Ref, untreated	Empty, untreated
+44.6	149.4	1.1	19.7	33.2	1.0
+37.6	99.7	1.0	16.2	30.3	1.0
+37.7	124.5	0.9	18.3	33.3	1.0
+```
+
+<img src="https://github.com/anthony-aylward/luciferase/raw/master/example/ratio.png" width="200"/><img src="https://github.com/anthony-aylward/luciferase/raw/master/example/ratio-invert.png" width="200"/>
+
+## Meta-analysis
+
+For this section, we'll use another included command called
+`luciferase-swarmplot`. It functions exactly like `luciferase-barplot` except
+that individual data points will be plotted over the bars.
+
+It may be that we have performed two or more experiments
+(from separate minipreps) and wish to meta-analyze the results. As an example,
+let's consider the results of two identical experiments on a regulatory
+variant at the _SIX3_ locus: [SIX3-MP0](https://github.com/anthony-aylward/luciferase/raw/master/example/six3-mp0.json) and [SIX3-MP1](https://github.com/anthony-aylward/luciferase/raw/master/example/six3-mp1.json). First we'll plot both datasets separately:
+```sh
+luciferase-swarmplot six3-mp0.json six3-mp0.png --light-colors '#DECBE4' '#FED9A6' '#FBB4AE' --dark-colors '#984EA3' '#FF7F00' '#E41A1C' --title 'SIX3-MP0'
+luciferase-swarmplot six3-mp1.json six3-mp1.png --light-colors '#DECBE4' '#FED9A6' '#FBB4AE' --dark-colors '#984EA3' '#FF7F00' '#E41A1C' --title 'SIX3-MP1'
+```
+
+<img src="https://github.com/anthony-aylward/luciferase/raw/master/example/six3-mp0.png" width="400"/><img src="https://github.com/anthony-aylward/luciferase/raw/master/example/six3-mp1.png" width="400"/>
+
+We can see that the results are fairly consistent in character, but checking
+the y-axis tells us that they are on different scales. Intuitively, we might
+conclude from these results that there are allelic effects under all three
+conditions. Ideally though, we would like to use all of the data at once for
+one plot to get the most accurate conclusions about allelic effects.
+
+We might simply combine the data into one dataset, (as [here](https://github.com/anthony-aylward/luciferase/raw/master/example/six3-meta-nobatch.json)) and plot it:
+```sh
+luciferase-swarmplot six3-meta-nobatch.json six3-meta-nobatch.png --light-colors '#DECBE4' '#FED9A6' '#FBB4AE' --dark-colors '#984EA3' '#FF7F00' '#E41A1C'
+```
+
+![meta-analysis without batch](https://github.com/anthony-aylward/luciferase/raw/master/example/six3-meta-nobatch.png)
+
+The bar heights look reasonable, and the allelic effects appear clear from
+looking at them, but all of the hypothesis tests returned non-significant
+results. What gives?
+
+The answer is that combining data from experiments with different scales
+breaks the assumptions of the significance test (a t-test). To meta-analyze
+these data in a useful way, we first need to re-normalize the two experiments
+to put both of them on the same scale. `luciferase-barplot` and
+`luciferase-swarmplot` will re-normalize the data automatically if the dataset
+includes an additional entry ("Batch") indicating the batch of each data point,
+as in this example:
+[SIX3-META](https://github.com/anthony-aylward/luciferase/raw/master/example/six3-meta.json).
+```json
+{
+  "Alt, untreated": [19.7, 16.2, 18.3, 6.5, 8.0, 4.4],
+  "Ref, untreated": [33.2, 30.3, 33.3, 8.4, 13.6, 17.1],
+  "Empty, untreated": [1.0, 1.0, 1.0, 1.1, 1.0, 0.9],
+  "Alt, hi_cyt_noTNFA": [11.0, 8.8, 10.1, 3.2, 3.7, 3.3],
+  "Ref, hi_cyt_noTNFA": [17.1, 16.7, 18.8, 7.6, 6.7, 5.5],
+  "Empty, hi_cyt_noTNFA": [1.1, 0.9, 1.0, 1.1, 0.9, 1.0],
+  "Alt, hi_cyt": [10.8, 10.9, 9.1, 3.1, 2.7, 4.0],
+  "Ref, hi_cyt": [17.8, 16.1, 18.0, 7.7, 7.0, 7.1],
+  "Empty, hi_cyt": [1.0, 1.0, 1.0, 1.0, 1.0, 1.1],
+  "Batch": [0, 0, 0, 1, 1, 1]
+}
+```
+
+Here is what the results look like when they're re-normalized to correct for
+batch
+```sh
+luciferase-swarmplot six3-meta.json six3-meta.png --light-colors '#DECBE4' '#FED9A6' '#FBB4AE' --dark-colors '#984EA3' '#FF7F00' '#E41A1C' --title 'SIX3-META'
+```
+
+![meta-analysis with batch](https://github.com/anthony-aylward/luciferase/raw/master/example/six3-meta.png)
+
+See a more detailed explanation of the normalization procedure [here](https://github.com/anthony-aylward/luciferase/blob/master/example/meta-analysis.pdf)
+
+
+
+
+%prep
+%autosetup -n luciferase-2.4.5
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-luciferase -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 17 2023 Python_Bot <Python_Bot@openeuler.org> - 2.4.5-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..59ce11d
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+3ddd1bc93c0473daf5bef18ca3c726b5  luciferase-2.4.5.tar.gz